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700 - Data processing: generic control systems or specific applications

700001000 - GENERIC CONTROL SYSTEM, APPARATUS OR PROCESS

700028000 - Optimization or adaptive control

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Class / Patent application numberDescriptionNumber of patent applications / Date published
700030000 Comparison with model (e.g., model reference) 49
700031000 Having adjustment of model (e.g., update) 27
Entries
DocumentTitleDate
20100138004APPARATUS AND METHOD FOR MODEL-BASED CONTROL - Various methods and systems for the parametric control of a process include representing the process with a process model used to generate future predictions of a process variable. In one embodiment, the process exhibits integrating behavior that is represented by a non-integrating process model. In another embodiment, an inverse of the model is filtered using a filter that includes a lead time constant that is selected to minimize a steady state error of the predicted process variable. In yet another embodiment, an array of output model values is revised or reindexed in response to a change in a time-varying parameter related to the process.06-03-2010
20130030554INTEGRATED LINEAR/NON-LINEAR HYBRID PROCESS CONTROLLER - A model predictive controller (MPC) for controlling physical processes includes a non-linear control section that includes a memory that stores a non-linear (NL) model that is coupled to a linearizer that provides at least one linearized model, and a linear control section that includes a memory that stores a linear model. A controller engine is coupled to receive both the linearized model and linear model. The MPC includes a switch that in one position causes the controller engine to operate in a linear mode utilizing the linear model to implement linear process control and in another position causes the controller engine to operate in a NL mode utilizing the linearized model to implement NL process control. The switch can be an automatic switch configured for automatically switching between linear process control and NL process control.01-31-2013
20090299497Tolerance interval determination method - A method for containing a fraction of values of a measurable characteristic of interest, occurring in process outcomes provided from a corresponding formation process, within tolerance limits based on samples thereof, the tolerance limits being based on a different formation process by which similar process outcomes are known to have been previously through selecting a probability representation over a representational variable to represent the distribution of values of the measurable characteristic of interest in the formation processes outcomes and using a selected Monte Carlo method with the probability representation to provide a plurality of sample values sets for the measurable characteristic of interest each containing a common selected number of sample values. A statistic is formed to test selected tolerance limits to find a value for tan incremental variable to assure those tolerance limits will be met with a selected confidence.12-03-2009
20130085584PLANT CONTROL APPARATUS, PLANT CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE RECORDING MEDIUM - In accordance with an embodiment, a plant control apparatus includes a deviation calculation unit, a velocity-type PID calculation unit, a plurality of integral calculation units, a plurality of overwrite units, and an automatic balance unit. The deviation calculation unit calculates a deviation between a process value from a plant with operation terminals and a set value corresponding to a control object, and generates a deviation signal. The velocity-type PID calculation unit generates a velocity-type operation amount command signal corresponding to the deviation. The integral calculation units generate position command signals as defined operation terminal position command signals. The overwrite units generate additional position command signals to perform overwrite processing and newly define operation terminal position command signals. The automatic balance unit calculates a deviation between the defined operation terminal position command signals, corrects the operation amount command signal and supplies the corrected operation amount command signal.04-04-2013
20090093892Automatic determination of the order of a polynomial regression model applied to abnormal situation prevention in a process plant - A system for preventing abnormal situations in process plants is provided. A polynomial regression model is employed to predict values of a monitored variable based on measured samples of a load variable. An abnormal situation is detected when a predicted value of the monitored variable differs from a measured value of the monitored variable by more than a predetermined amount. The system employs one or more algorithms for automatically determining an optimal order or degree of the polynomial regression model.04-09-2009
20110196513APPARATUS AND METHOD FOR DEMAND COORDINATION NETWORK - An apparatus for controlling peak demand of a resource. The apparatus includes a plurality of devices and a plurality of control nodes. Each of the plurality of devices consumes a portion of the resource when turned on, and the each are capable of performing a corresponding function within an acceptable operational margin by cycling on and off. Each of the plurality of control nodes is coupled to a corresponding one of the plurality of devices, where the plurality of control nodes is coupled together via a demand coordination network, and where the plurality of control nodes coordinates run times for the each of the plurality of devices to reduce the peak demand of the resource, and where one or more of the run times start prior to when otherwise required to maintain corresponding local environments, but which still operate within the acceptable operating margin for the corresponding local environments.08-11-2011
20130041482METHOD AND SYSTEM FOR UPDATING A MODEL IN A MODEL PREDICTIVE CONTROLLER - Exemplary embodiments relate to a method and system for updating a model in a model predictive controller. The system executing a method that includes assessing the deviation of the operating performance level from the desired performance level of the process plant, and diagnosing the model predictive control for the model plant mismatch by updating the model in a model predictive controller. The step of diagnosing the model predictive controller includes determining the model prediction error in relation to model plant mismatch, quantifying the model plant mismatch, and updating the model in the model predictive controller.02-14-2013
20090125126METHODS AND APPARATUS TO MODIFY A RECIPE PROCESS FLOW ASSOCIATED WITH A PROCESS CONTROL SYSTEM DURING RECIPE EXECUTION - Example methods and apparatus to modify a recipe process flow during recipe execution are disclosed. A disclosed example method involves executing a recipe, and before completion of execution of the recipe, receiving process flow change information indicative of a modification to a process flow of the recipe. Process flow rules are then retrieved from a process flow rules data structure. The recipe process flow is modified based on the process flow change information in response to determining that at least one requested change indicated by the process flow change information does not violate one of the process flow rules.05-14-2009
20090105851METHODS AND SYSTEMS FOR OPERATING AN AUTOMATED SYSTEM - A method is provided for operating an automated process defined by an execution model. The automated process includes a plurality of sub-processes. The method includes detecting an event during a first sub-process of the plurality of sub-processes, and resetting the first sub-process by operating an unload procedure that is written into the execution model. The method also includes operating at least one of a pre-process and a pre-sub-process procedure to facilitate continuing the automated process at a predetermined step of the automated process. The pre-process and the pre-sub-process procedures are written into the execution model.04-23-2009
20130073061METHOD OF MODEL IDENTIFICATION FOR A PROCESS WITH UNKNOWN INITIAL CONDITIONS IN AN INDUSTRIAL PLANT - A method of model identification for a process with unknown initial conditions in an industrial plant, the method comprising collecting a set of manipulated variables and corresponding set of process variables from the process; obtaining a plurality of manipulated variables from the collected set of manipulated variables; for each of the plurality of manipulated variables, obtaining optimal model parameters of a model transfer function and computing a model fitting index for optimized simulated process variables generated by the model transfer function using the optimal model parameters; identifying a best model fitting index among the model fitting indices computed; selecting a manipulated variable associated with the best model fitting index as an initial steady state condition for the model transfer function; and selecting the optimal model parameters corresponding with the best model fitting index as the best model parameters of the model transfer function to tune the controller.03-21-2013
20130060353APPARATUS AND METHOD FOR PREDICTING WINDUP AND IMPROVING PROCESS CONTROL IN AN INDUSTRIAL PROCESS CONTROL SYSTEM - A method includes identifying one of multiple regions in a range where an output (OP) value used to implement a manipulated variable is located. The manipulated variable is associated with an industrial process, and the OP value represents an output of a downstream controller. The method also includes calculating an achievable manipulated variable (MV) limit for the manipulated variable based on the region in which the OP value is located. For example, when the OP value is located in one region, the achievable MV limit could match a user-specified limit or be based on a gain between the OP value and a value of a process variable. When the OP value is located in another region, the achievable MV limit could track the value of the process variable with a gap.03-07-2013
20090299496Controller - A controller is provided, operable to control a system on the basis of measurement data received from a plurality of sensors indicative of a state of the system, with at least partial autonomy, but in environments in which it is not possible to fully determine the state of the system on the basis of such sensor measurement data. The controller, comprises: a system model, defining at least a set of probabilities for the dynamical evolution of the system and corresponding measurement models for the plurality of sensors of the system; a stochastic estimator operable to receive measurement data from the sensors and, with reference to the system model, to generate a plurality of samples each representative of the state of the system; a rule set corresponding to the system model, defining, for each of a plurality of possible samples representing possible states of the system, information defining an action to be carried out in the system; and an action selector, operable to receive an output of the stochastic estimator and to select, with reference to the rule set, information defining one or more corresponding actions to be performed in the system.12-03-2009
20110022194TRANSDUCER ACCESS POINT - The invention relates to a method and apparatus for exposing (i.e. bridging) data and services offered by low power, low duty cycle transducers (e.g. sensors and actuators) in a standardized format over existing and established home networking technologies. A transducer access point is a functional component that serves as a proxy for health devices and sensors that are off/asleep the majority of the time to conserver power (e.g. battery life). The transducer access point may be implemented as a stand alone device or embedded within a computing device such as a home PC.01-27-2011
20130166043OPTIMAL ENERGY MANAGEMENT OF A MICROGRID SYSTEM USING MULTI-OBJECTIVE OPTIMIZATION - Systems and methods are disclosed to improve energy efficiency of a farm with livestock wastes by generating a cooling, heating, and power (CCHP) microgrid model; performing on a computer a multi-objective optimization to improve system efficiency of energy utilization and reduce environmental problems caused by animal wastes; and displaying results of the optimization for review.06-27-2013
20090012632OPERATION CONTROL METHOD, OPERATION CONTROL DEVICE, AND OPERATION CONTROL SYSTEM - It is an object to provide an operation control apparatus and an operation control method which allow operation of a control object without causing adverse affect on the operational condition of the control object, even when a deviation of a real system from a model (model error) arises. The operation control method employable in the control apparatus controls a control object by calculating operation amount to maximize or minimize an evaluation value obtained from a control deviation of the control object from a target value. The operation control method includes the steps of: establishing a model for simulating a property of the control object; calculating operation amount to maximize or minimize an evaluation value based on a control deviation of the model as a target; calculating an evaluation value based on a control deviation in controlling the control object by the operation amount; and determining an operation amount change width defined by a difference between current step operation amount and next step operation amount, based on the deviation of the control object from the model in the evaluation value of the control deviation.01-08-2009
20100168875Method for Process Optimisation - A method is disclosed. In at least one embodiment of the method, firstly, the process as described by a process definition is analysed such that those control sequence dependencies are discovered which are not supported by the data stream, the control sequences contained in the process are transformed in a first step into a corresponding Petri network and, in a second step, the Petri network is analysed. Secondly, the process is then reconstructed without the discovered redundant or unnecessary control sequence dependencies and an optimum process definition generated. Excess dependencies are thus automatically discovered and removed by way of at least one embodiment of the invention, in order to achieve, for example, an increased parallelisation or compatibility of partial processes.07-01-2010
20130024014OPTIMAL ENERGY MANAGEMENT OF A RURAL MICROGRID SYSTEM USING MULTI-OBJECTIVE OPTIMIZATION - Systems and methods are disclosed to improve energy efficiency of a farm with livestock wastes by generating a cooling, heating, and power (CCHP) microgrid model; performing on a computer a multi-objective optimization to improve system efficiency of energy utilization and reduce environmental problems caused by animal wastes; and displaying results of the optimization for review.01-24-2013
20130024013SYSTEM AND METHOD FOR ACTUATOR CONTROL - Information from a real sensor and a virtual sensor are fused to form a hybrid sensor. Control signals (and/or the absolute value of the control signals) applied to an actuator are accumulated and converted to a position the actuator should be in based on the accumulated control signals to form the virtual sensor. The actuator position from the virtual sensor is fused with an actuator position from a real sensor to form the hybrid sensor. Small periodic corrections can be made to the accumulating control signals to maintain or achieve, if possible, correlation between the virtual sensor and the real sensor over time. The corrections slowly decrement errors in the actuator position indicated by the virtual sensor. Accumulating numerical errors in the accumulating control signals are reduced and the significance of long past events is de-emphasized by a forgetting factor (k01-24-2013
20110087340MOVING OBJECT FEED-FORWARD CONTROL METHOD - In a case where a position command path for a control position of a load 04-14-2011
20110301723USING MODEL PREDICTIVE CONTROL TO OPTIMIZE VARIABLE TRAJECTORIES AND SYSTEM CONTROL - A method and system of predictive model control of a controlled system with one or more physical components using a model predictive control (MPC) model, determining an iterative, finite horizon optimization of a system model of the controlled system, in order to generate a manipulated value trajectory as part of a control process. At time t sampling a current state of the controlled system a cost function minimizing manipulated variables trajectories is computed with the MPC model for a relatively short time horizon in the future, wherein the MPC uses a quadratic programming (QP) algorithm to find the optimal solution, and wherein the QP algorithm is solved using an Active Sets solver (AS) class algorithm with simple constraints based on gradient projection and using Newton step projection. A move of the manipulated value trajectory is implemented and the control process is moved forward by continuing to shift the prediction horizon forward.12-08-2011
20090118840METHOD AND DEVICE FOR CONTROLLING A MOTION SEQUENCE OF A MACHINE ELEMENT - For controlling a motion sequence of a machine element, with which the control of the motion sequence of the machine element is carried out based on a functional relationship between a master shaft and a slave shaft, the functional relationship IS ascertained with consideration for several conditions of this motion sequence. The functional relationship includes at least one first section, which is defined by an nth-order polynomial, and at least one second section, which is at least partially separated from the first section, and which is defined by an ath-order polynomial. In this case, “a” is less than “n”.05-07-2009
20100268353Systems and Methods for Offset-Free Model Predictive Control - Techniques, systems and methods for designing, implementing, and operating model predictive controllers that can deliver perfect tracking of set points and that can reject the effect of disturbances when steady-state operation is reached are disclosed. High performance is achieved through the incorporation of set-point tracking costs, integral costs, and velocity costs, as well as the adoption of incremental model systems for prediction purposes. Embodiments can deliver offset-free performance for tracking set points with constant final values, set points of a ramp type, and set points of a parabolic form, while rejecting disturbances that have a constant final value. The approach reduces the complexity of model predictive control design, delivers improved performance, and requires modest computational power. An Incremental Model State Estimator (IMES) is disclosed that reduces the computation load required for producing estimated values for the unmeasured star vector of a model under the presence of unmeasured disturbances. 10-21-2010
20120035747SCALING AND PARAMETERIZING A CONTROLLER - Controller scaling and parameterization are described. Techniques that can be improved by employing the scaling and parameterization include, but are not limited to, controller design, tuning and optimization. The scaling and parameterization methods described here apply to transfer function based controllers, including PID controllers. The parameterization methods also applies to state feedback and state observer based controllers, as well as linear active disturbance rejection controllers. It is emphasized that this abstract is provided to comply with the rules requiring an abstract that will allow a searcher or other reader to quickly ascertain the subject matter of the application. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. 37 CFR 1.72(b).02-09-2012
20090287320System and Method for the Model Predictive Control of Batch Processes using Latent Variable Dynamic Models - A computer implemented method for modeling and controlling batch or transitional processes is disclosed including collecting, or initiating the collection of measurements on a plurality of process variables. The method may include creating, or initiating the creation of, a latent variable model predictive controller based on the collected measurements. The method further provides for applying or initiating the application of, the model predictive controller to predict and control at least one of the process variables to track a desired trajectory, by operation of at least one computer including one or more computer processors. A related system for implementing the method is disclosed as is a computer program operable with this method.11-19-2009
20090276061Online Modular Parameterization and Successive Linear Programming for Improving Manufacturing Plant Operations - Methods and systems for substantially optimizing plant operations within a manufacturing environment. The method can include separating the manufacturing environment into two or more individual modules, wherein each individual module contains a fundamental principles-based model, and wherein the totality of the individual modules represents the entire manufacturing environment. Each individual module can be independently parameterized upon said module reaching steady state, wherein inter-module data flow can be provided to at least one of the individual modules during parameterization, and wherein an output of the parameterization comprises an individual, calibrated steady-state model of each individual module. A reduced order model can be derived from each parameterized module, and the reduced order models can be assembled to provide a facility reduced order model. The facility reduced order model can then be solved to provide improved or new operating conditions or operating condition targets.11-05-2009
20080312756Virtual sensor system and method - A method is provide for providing sensors for a machine. The method may include obtaining data records including data from a plurality of sensors for the machine and determining a virtual sensor corresponding to one of the plurality of sensors. The method may also include establishing a virtual sensor process model of the virtual sensor indicative of interrelationships between at least one sensing parameters and a plurality of measured parameters based on the data records and obtaining a set of values corresponding to the plurality of measured parameters. Further, the method may include calculating the values of the at least one sensing parameters substantially simultaneously based upon the set of values corresponding to the plurality of measured parameters and the virtual sensor process model and providing the values of the at least one sensing parameters to a control system.12-18-2008
20120271437ELECTRICAL DISTRIBUTION NETWORK IMPROVEMENT FOR PLUG-IN ELECTRIC VEHICLES - Electrical distribution network (EDN) improvement method for plug-in electric vehicles receives and stores in a database EDN configuration information, demography information and load information for simulating load of the EDN assets. The method dynamically updates the EDN configuration, demography information and/or load information to provide an efficient and customizable method of simulating a PEV load impact on an EDN configuration and apply improvements to the EDN in real time.10-25-2012
20080275574METHOD AND SYSTEM FOR VIBRATION AVOIDANCE FOR AUTOMATED MACHINERY - A method for vibration avoidance in automated machinery produces actuator space-time contours that meet design objectives of the machinery while suppressing energy content at frequencies in the space-time contour, by concatenating multiple space-time contour segments together in such a way as to be mostly free of energy at the frequencies of interest while meeting other specified design goals. The segments used to construct these frequency-optimized-contours are a series of concatenated polynomial segments, the independent variable t being time. These segments can define the variable to be controlled (e.g. speed or distance) versus time, or define one of the controlled variable's time-derivatives (e.g., the slope of the speed vs. time, etc.). When these frequency-optimized-contours are fed as a command to a machine controller through an actuator or actuators, the energy at the frequencies of interest is low enough to avoid deleterious vibration from occurring while still meeting the machine performance objectives.11-06-2008
20080281440Stabilizing solutions to output feedback pole placement problem with parameter drift and automated alerting of system parameter changes - Output feedback pole placement problems with parameter drift are solved with stabilizing solutions. Changes in system parameters trigger alerts in an automated manner. A representative method includes determining a set of solutions for an output feed pole placement problem, based on parameters of a physical system. The solutions are stable and well-conditioned for monitoring changes to the parameters of the physical system. The physical system is adjusted, or controlled, based on the solutions determined. Updated parameters of the physical system are acquired. A set of updated solutions for the output feedback pole placement problem are determined based on the updated parameters. The physical system is then adjusted, or controlled, based on the updated solutions determined. A system manager may also be notified of the updated parameters and/or the updated solutions. Furthermore, changes within the system may be monitored, and/or potentially critical changes within the system may be detected.11-13-2008
20100292810METHOD FOR ORCHESTRATING SERVICES OF A SERVICE-ORIENTED AUTOMATION SYSTEM AND ORCHESTRATION MACHINE - A method for orchestrating services of a service-oriented automation system (SOAS), system components (SMC, LCC) offering services (S, WS) that represent the functionality thereof and requesting services (S) of other system components (SMC, LCC), the behavior of the automation system (SOAS) being controlled by the orchestration of the services (S) of the system components (SMC, LCC) using an orchestration machine (OE), and to an orchestration machine for orchestrating services of a service-oriented automation system (SOAS). In order to achieve an orchestration of services at a device level, it is provided that the orchestration machine (OE) uses high-level Petri nets tailored to service-oriented systems and the orchestration of the services (S) at the device level is performed by interpretation and execution of various HLPN models, which represent the behavior of the automation system (SOAS) and/or the system components (SMC, LCC).11-18-2010
20080281439Building automation systems and methods - An environment control system which includes a network, a plurality of units and a controller. The system being configured to utilize a nonlinearity compensation controller to compensate for nonlinearity in the system.11-13-2008
20090005886Extended Active Disturbance Rejection Controller - Multiple designs, systems, methods and processes for controlling a system or plant using an extended active disturbance rejection control (ADRC) based controller are presented. The extended ADRC controller accepts sensor information from the plant. The sensor information is used in conjunction with an extended state observer in combination with a predictor that estimates and predicts the current state of the plant and a co-joined estimate of the system disturbances and system dynamics. The extended state observer estimates and predictions are used in conjunction with a control law that generates an input to the system based in part on the extended state observer estimates and predictions as well as a desired trajectory for the plant to follow.01-01-2009
20090177291DYNAMIC CONTROLLER UTILIZING A HYBRID MODEL - A system and method for predicting operation of a plant or process receive an input value from the plant or process. An integrity of a non-linear model corresponding to a local input space of the input value may be determined. The non-linear model may include an empirical representation of the plant or process. If the integrity is above a first threshold, non-linear model may be used to provide a first output value. However, if the integrity is below the first threshold, a linearized first principles model may be used to provide a second output value. The linearized first principles model may include an analytic representation of the plant or process. Additionally, the analytic representation of the plant or process may be independent of the empirical representation of the plant or process. The first output value and/or the second output value may be usable to manage the plant or process.07-09-2009
20090143871CONTROLLERS, OBSERVERS, AND APPLICATIONS THEREOF - Controller scaling and parameterization are described. Techniques that can be improved by employing the scaling and parameterization include, but are not limited to, controller design, tuning and optimization. The scaling and parameterization methods described here apply to transfer function based controllers, including PID controllers. The parameterization methods also apply to state feedback and state observer based controllers, as well as linear active disturbance rejection (ADRC) controllers. Parameterization simplifies the use of ADRC. A discrete extended state observer (DESO) and a generalized extended state observer (GESO) are described. They improve the performance of the ESO and therefore ADRC. A tracking control algorithm is also described that improves the performance of the ADRC controller. A general algorithm is described for applying ADRC to multi-input multi-output systems. Several specific applications of the control systems and processes are disclosed.06-04-2009
20090326679BEHAVIOR ESTIMATING SYSTEM - A behavior estimating system is provided. According to the system, an estimated trajectory which provides the basis on which the behavior of an agent is controlled is generated according to a second model which represents a motion of an instructor in which the position and the displacing velocity of the position of a state variable and the time differential values thereof continuously change, in addition to the position of a characteristic point of a reference trajectory which represents a motion of the instructor and a plurality of first models which represent a plurality of shape characteristics of reference trajectories. A behavior manner corresponding to a first model whose fluctuation, which is allowed under a condition that an estimated trajectory passes a characteristic state variable or a range in the vicinity thereof, is the smallest and whose stability is the highest is estimated as the behavior manner of the instructor.12-31-2009
20090054998SYSTEM AND PROCESS FOR OPTIMIZING PROCESS CONTROL - A system and process is provided for optimizing process control by linking planning linear programming and advanced process control optimization activities. The processing steps include feeding back constraint data to a planning LP, LP modeling a process, calculating optimum setpoints, and passing LP plan parameters to a plurality of APC controllers.02-26-2009
20090222108INTEGRATED CONTROLS DESIGN OPTIMIZATION - A control system (09-03-2009
20090254202METHODS AND SYSTEMS FOR THE DESIGN AND IMPLEMENTATION OF OPTIMAL MULTIVARIABLE MODEL PREDICTIVE CONTROLLERS FOR FAST-SAMPLING CONSTRAINED DYNAMIC SYSTEMS - Methods and systems for the design and implementation of optimal multivariable MPC controllers for fast-sampling constrained dynamic systems utilizing a primal-dual feasibility approach and/or a graph approach. The primal-dual feasibility approach can compute and store matrices defining constraints of quadratic programming problems in an off-line part in order to calculate vectors of Lagrange multipliers and an optimizer. Then primal-dual feasibility can be checked in an on-line part using the Lagrange multipliers and the optimizer can provide a unique optimal solution for the constrained dynamic system. The graph approach can compute and store the matrices and the vectors, and also prepare and store a structure of directed graph in off-line part. An optimizer for a given parameter vector can be determined in on-line part using the directed graph, the matrices and the vectors.10-08-2009
20100222898Stage-control systems and methods including inverse closed loop with adaptive controller - Stage assemblies and control methods are disclosed. An exemplary stage assembly includes a movable stage and a control system. The stage-control system has first and second control loops. In the first control loop a first controller is programmed with a feedback-control transfer-function that determines a feedback-control output from an input including a following-error of the stage. The second control loop includes an inverse closed loop having an inverse plant model and a second controller programmed with an adaptive transfer-function connected to receive inputs including the following-error and the feedback-control output. The second controller determines, from the inputs, an adapted control output to the stage. The adaptive transfer-function can be, e.g., an AFC transfer-function producing an AFC controlled output or an ILC transfer-function producing an ILC controlled output.09-02-2010
20130123949USING MODEL COMMUNICATION AS A PROTOCOL IN A MANAGED ELECTRICAL SYSTEM - One embodiment of the present invention provides managing component driver for an energy-management system that manages energy within a predominantly closed power system. The component driver includes a receiving mechanism configured to receive current status information for a component associated with the component driver, a model-construction mechanism configured to construct a performance model for the component, and a transmitting mechanism configured to transmit the performance model to an optimization and control module.05-16-2013
20100241249SYSTEM FOR OPTIMIZING OXYGEN IN A BOILER - A method and apparatus for optimizing air flow to a boiler of a power generating unit using advanced optimization, modeling, and control techniques. Air flow is optimized to maintain flame stability, minimize air pollution emissions, and improve efficiency.09-23-2010
20100241248METHOD AND SYSTEM FOR OPTIMIZING THE LAYOUT OF A ROBOT WORK CELL - The present invention relates to a method for optimizing the placement of a plurality of workstations (09-23-2010
20110112659System identification in automated process control - The systems and methods described herein allow for automatic identification experiments in a closed loop, where the old control strategy, already tuned and tested, is utilized. The strategy is modified to inject additional signal optimized for identification. The experimenting time may be reduced by performing only those system manipulations which explore model uncertainties important to potential degradation of controller performance by discrepancy between the system and the model. The disruptions are reduced by keeping the control loop closed, which eliminates waiting for steady state before applying steps to the inputs and reduces the risk of process limits crossing. The energy of additional signal can be set to meet the maximum allowable disruption requirements. The energy of additional signal is in a direct relation to the speed of identification related information gathering. It can be varied in time to follow the needs of system operators.05-12-2011
20090204233APPARATUS AND METHOD FOR SYSTEM IDENTIFICATION AND LOOP-SHAPING CONTROLLER DESIGN IN A PROCESS CONTROL SYSTEM - One method includes obtaining a preliminary model associated with a system to be controlled and constructing a weighted model using one or more weighting factors. The method also includes identifying a final model of the system using the preliminary and weighted models, where the final model has a stability margin that is greater than an uncertainty associated with the final model. The method further includes controlling the system using a controller designed based on the final model. Another method includes identifying a first model associated with a system to be controlled, performing model order reduction to identify a second model, and controlling the system using a controller designed based on the second model. Performing the model order reduction includes reducing a weighted coprime factor model uncertainty between the first and second models.08-13-2009
20090138101Method, System and Computer Program Product for Improving Information Technology Service Resiliency - A method is provided. The method includes the steps of: generating a model of an information technology process, wherein the process comprises a plurality of process steps and wherein the model identifies resources associated with the process; identifying dependencies on the resources for at least one process step or the plurality of process steps; perturbing the model; assessing an impact of the perturbation on the model; and reducing the impact of the perturbation on the model by utilizing at least one remedial action.05-28-2009
20090112334Fixed-point virtual sensor control system and method - One aspect of the present disclosure includes a method for a control system of a machine. The method may include establishing a virtual sensor model indicative of interrelationships between at least one sensing parameter and a plurality of measured parameters related to the machine. The method may also include obtaining data and function information representing the virtual sensor model and converting the data information into fixed-point representation. Further, the method may include converting the function information into fixed-point representation and loading the converted fixed-point representation of data information and function information in the control system such that the control system uses the virtual sensor model in fixed-point arithmetic operation.04-30-2009
20110251700SYSTEM AND METHOD FOR SOLVING CHEMICAL ENGINEERING EQUATIONS AND MODEL DEVELOPMENT USING EQUATION EDITOR - A system includes a process controller and an equation evaluation apparatus. The equation evaluation apparatus includes an equation editor, a model factory, and an equation evaluation engine. The equation editor is adapted to receive equations describing a process to be controlled by the process controller. The equation editor is also adapted to generate model information representing the equations. The model factory is adapted to receive the model information and generate an equation stack representing the equations. The equation evaluation engine is adapted to receive evaluation information from the process controller, evaluate at least one of the equations using the evaluation information and the equation stack, and send a result of the evaluation to the process controller. The model information could include information representing algebraic equations, differential equations, algebraic states, differential states, inputs, parameters, constants, and/or expressions.10-13-2011
20090312850Simulation apparatus, model for simulation, and apparatus forming model for simulation - A simulation apparatus is composed of an integrated plant model process unit 12-17-2009
20110071653METHOD AND SYSTEM FOR UPDATING TUNING PARAMETERS OF A CONTROLLER - A method and system for updating tuning parameters associated with a controller without repetitive compilation of a controller code. The controller code represents an algorithm associated with the controller and can be compiled separately from a data set representing a solution for an optimization problem and also from a data set representing parameters required for prediction. The algorithm can be implemented in a programming language code suitable for implementation on an embedded platform or other types of computer platforms. The data sets can be represented in a specified data structure and the variables associated with the data structure can be declared in the controller template code. The variables can be updated independently without varying the compiled code associated with the controller algorithm that is referring to the variables. The controller can also be updated while the controller actively performs online. Such an approach enables repetitive tuning of the controller without repetitive compilation of the code representing the controller algorithm.03-24-2011
20110054642Optimizing Consumption of Resources - Methods, systems and apparatus for optimizing consumption of one or more resources are presented. For example, a method that may be implemented on a processor device and includes obtaining user preferences for the consumption of resources that include water and electricity, predicting the consumption of, and a first metric for the consumption of, the resources for each of a plurality of first time periods, determining a projected second metric for the consumption of the resources during a second time period according to the predicted consumption and the predicted first metric, and optimizing the consumption of the resources according to the projected second metric and the user preferences. The second time period includes the plurality of first time periods. The first metric is associated with the user preferences and at least one of the plurality of first time periods. The second metric indicates full or partial projected attainment of the preferences during the second time period.03-03-2011
20110022193METHOD AND APPARATUS OF A SELF-CONFIGURED, MODEL-BASED ADAPTIVE, PREDICTIVE CONTROLLER FOR MULTI-ZONE REGULATION SYSTEMS - A control system simultaneously controls a multi-zone process with a self-adaptive model predictive controller (MPC), such as temperature control within a plastic injection molding system. The controller is initialized with basic system information. A pre-identification procedure determines a suggested system sampling rate, delays or “dead times” for each zone and initial system model matrix coefficients necessary for operation of the control predictions. The recursive least squares based system model update, control variable predictions and calculations of the control horizon values are preferably executed in real time by using matrix calculation basic functions implemented and optimized for being used in a S7 environment by a Siemens PLC. The number of predictions and the horizon of the control steps required to achieve the setpoint are significantly high to achieve smooth and robust control. Several matrix calculations, including an inverse matrix procedure performed at each sample pulse and for each individual zone determine the MPC gain matrices needed to bring the system with minimum control effort and variations to the final setpoint. Corrective signals, based on the predictive model and the minimization criteria explained above, are issued to adjust system heating/cooling outputs at the next sample time occurrence, so as to bring the system to the desired set point. The process is repeated continuously at each sample pulse.01-27-2011
20090204234SYSTEM AND METHOD FOR DYNAMIC MULTI-OBJECTIVE OPTIMIZATION OF MACHINE SELECTION, INTEGRATION AND UTILIZATION - The invention provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like. More particularly, the subject invention provides for employing machine diagnostic and/or prognostic information in connection with optimizing an overall business operation over a time horizon.08-13-2009
20100082120SYSTEM AND METHOD FOR OPTIMIZING A PAPER MANUFACTURING PROCESS - A technique is disclosed for optimizing a quality parameter in a process that is not directly measurable online using conventional measurement devices. The technique includes the use of a first inferential model to predict a value for the parameter based upon other process variables. A second inferential model predicts a residual component of the process parameter based off non-controllable residual variables of the process. The inferential model outputs are combined to produce a composite predicted value which may be further adjusted by an actual prediction error determined via comparison with an offline measurement. The adjusted predicted value is provided to a dynamic predictive model which may be adapted to implement control actions to drive or maintain the quality parameter at a target set point. The technique may further consider cost optimization factors and production reliability factors in order to produce a product meeting the target quality set point or range while considering production requirements and minimizing overall costs.04-01-2010
20110218653CONTROLLING STATE TRANSITIONS IN A SYSTEM - A control system is described for controlling the operation of a target system, such as a data center. The control system uses a prediction module to predict demand for resources of the target system for future time steps. The control system then uses a transition determination module to determine state transitions within the target system to address the predicted demand. Each state transition describes a number of units to be advanced from a first state, at a first time step, to a second state, at a second time step. The control system then commences those state transitions which begin in the current step, and then repeats the predicting and determining for a next time step. The transition determination module can determine the state transitions by operating on an objective function that includes a demand difference component and a cost component, as subject to a set of conservation equations.09-08-2011
20080281438Critical dimension estimation - Estimating a state of a critical dimension system comprises inputting a critical dimension measurement and inferring the state of the system based on a model of the critical dimension system and the critical dimension measurement.11-13-2008
20110118855SCALABLE MOTION CONTROL SYSTEM - A control system includes a clustered architecture having a master controller, a central control section including one or more first remote controllers under direct control of the master controller, and a distributed control section including a cluster controller controlled by the master controller. The cluster controller controls the activities of one or more second remote controllers. Each of the first and second remote controllers are utilized to drive one or more axes.05-19-2011
20110307082Method for Axis Correction in a Processing Machine and Processing Machine - A method for axis correction in a processing machine, in particular a shaftless printing machine, has at least one axis for processing and/or transporting a material, at least one detection device for detecting a processing parameter and at least one controller device for calculating a controller output variable for axis correction of the at least one axis using the detected processing parameter. The method is implemented iteratively, with the result that feedforward control output values for the feedforward control of the axis correction are determined during an (n+1)-th change in rotation speed of the at least one axis using observation of the controller output variable and/or the processing parameter during an n-th change in rotation speed of the at least one axis.12-15-2011
20120022670SYSTEM AND METHOD FOR UTILIZING A HYBRID MODEL - A system and method for predicting operation of a plant or process receive an input value from the plant or process. An integrity of a non-linear model corresponding to a local input space of the input value may be determined. The non-linear model may include an empirical representation of the plant or process. If the integrity is above a first threshold, non-linear model may be used to provide a first output value. However, if the integrity is below the first threshold, a linearized first principles model may be used to provide a second output value. The linearized first principles model may include an analytic representation of the plant or process. Additionally, the analytic representation of the plant or process may be independent of the empirical representation of the plant or process. The first output value and/or the second output value may be usable to manage the plant or process.01-26-2012
20120071991Method of Connecting Different Layers of Optimization - The present invention is a method for synchronizing multiple layers of constrained optimization with both layers having some common variables in a to processing plant. The layers of optimization can include Planning, Scheduling, Real-Time Optimization and Model Predictive Control.03-22-2012
20110066258System and Method for Energy Plant Optimization Using Mixed Integer-Linear Programming - A method for optimizing operational settings for a plurality of energy devices includes representing each of the plurality of energy devices in terms of a set of decision variables and operational parameters. The decision variables and operational parameters are constrained based on operational conditions and interrelationship within the plurality of energy devices. A two-tiered model of the plurality of energy devices is generated wherein a top tier of the model represents interaction of various sub-models and a bottom tier of the model includes a set of the sub-models that form the top tier, each sub-model representing detailed operation of the plurality of energy devices. The two-tiered model is optimized to provide either a schedule of operation for the plurality of energy devices or real-time control for the plurality of energy devices.03-17-2011
20090287319Universal model predictive controller - A method for building robust model predictive controller universally applicable is presented based on the innate process characteristics independent of the method of control actuation. The method of universal MPC design permits proper configuration of requisite regulatory control loops for measured and unmeasured disturbance rejections consistent with the underlying innate process characteristics and their embedding within the overall process unit model predictive controller. The method of universal MPC design requires that manipulated variables process value based model (PV-based models) be used in control and optimization in place of the customary set point based models (SP-based models) or control output based models (OP-based models). The PV-based models are devoid of the manipulated variables regulatory controllers response and tuning. Based on the PV-based models, an alternate method of MPC called PV-based MPC is presented that is most robust and adaptable of possible three types of MPC. Based on the universal MPC design, the prior art MPC can be adapted to improve its robustness at or near control valve saturation.11-19-2009
20110035028ACCELERATION/DECELERATION CONTROL DEVICE - A residual-velocity calculating unit calculates a residual velocity that corresponds to a velocity increment when an acceleration is reduced from a current command acceleration to zero according to an acceleration reduction curve. A differential-velocity calculating unit calculates a differential velocity vs=v0-vn, which is a difference between a target velocity v0 and a current velocity command for every command generation period. An acceleration-reduction-start-timing determining unit compares the residual velocity to the differential velocity for determining whether acceleration reduction starts. When a condition that the residual velocity is equal to or larger than the differential velocity is satisfied, the acceleration-reduction-start-timing determining unit determines the start of the acceleration reduction and starts to reduce the command acceleration according to the acceleration reduction curve generated by the command generating unit.02-10-2011
20120173004System and Method for Real-Time Industrial Process Modeling - The present invention presents two new model types and a new method for evaluating a model used in the control application. These include a compound model, a hybrid model and a directional change coefficient model. The present invention allows the mixing of models with different inputs and outputs and the switching between these models based criteria for measuring optimization accuracy. The present invention allows switching between these models. The compound model is a model type that allows zooming in on the process to model parts of the data space with higher fidelity or resolution without loosing the capability to model the complete data space. The modeler does not loose any functionally over a regular neural network, but instead gains the ability to define the conditions when the model should use network weights best matched to the defined local conditions. The hybrid model is an extended version of a compound model. A hybrid model allows the combining of one or more models into a single model for purposes of interrogation or optimization. Within the hybrid model may reside a compound model itself. The directional change model (DCC) allows better evaluation of the predictive capability of Compound Models. It may also be used with any other model type.07-05-2012
20090105852Control loop for regulating a process, in particular a combustion process - A control loop, which is for regulating a process in a plant having a controlled system, comprises: at least one measuring device for recording observation values of the controlled system, at least one adjustment device for acting on the controlled system in response to the adjustment device being controlled by way of action values, and a regulator. The regulator is operative to provide the action values. The regulator being operative to provide the action values comprises the regulator being adapted for: predicting, by way of a process model and at least one probability distribution of the observation values, a set of distributions of probable future states of the system; evaluating the set of distributions of probable future states of the system using target values and/or distributions of the target values; and selecting at least one probability distribution of action values.04-23-2009
20110112660FIRE PROTECTION DEVICE, METHOD FOR PROTECTING AGAINST FIRE, AND COMPUTER PROGRAM - The invention relates to a fire protection device (05-12-2011
20120265323MONITORING PROCESS CONTROL SYSTEM - A system includes an identification component configured to identify a set of key performance indicators that fail to satisfy predetermined acceptance criteria based on acquired performance data, where the set of key performance indicators is indicative of performance of components of a process control system. The system further includes a visualization component configured to visually present the identified set of key performance indicators, the components, and the acquired performance data in a graphical user interface displayed via a monitor. The system further includes a manual override component configured to allow a user to manually override and modify the information presented by the graphical user interface based, at least in part, on the acquired performance data.10-18-2012
20120265324METHOD FOR CONFIGURATION SOA-BASED AUTOMATION DEVICES AND FOR DEVELOPING AN ORCHESTRATION MACHINE, PRODUCTION METHOD AND PRODUCTION SYSTEM IN SERVICE-ORIENTED ARCHITECTURE HAVING EMBEDDED SERVICE ORCHESTRATION ENGINE - A method for configuring an automation device or simulator for controlling mechatronics components of an automation system, including: generating HLPN component models for each type of the mechatronic components of the automation system, creating a component instance model from an HLPN component model for each physically present mechatronic component, creating a layout configuration file, which describes relationships of the component instance models to be connected, composing the component instance models into a system model based on the layout configuration file, wherein logic ports of the component instance models are connected/linked to each other, generating configuration files based on a system model and device description files and WSDL files of the component instance models, loading the configuration files into the automation device or simulator containing the HLPN orchestration machine, and executing the configuration files by the HLPN orchestration machine of the automation device or the simulator.10-18-2012
20120323342APPARATUS AND METHOD FOR NONLINEAR PROCESS IDENTIFICATION USING ORTHONORMAL BASES AND ORDINAL SPLINES IN A PROCESS CONTROL SYSTEM - A method includes receiving data associated with operation of an industrial process system. The method also includes identifying a model defining a behavior of the industrial process system using the data, an orthonormal bases function, and an ordinal spline bases function. The orthonormal bases function can be generated using estimated poles of the industrial process system. The ordinal spline bases function can be generated using a specified set of cubic splines. The ordinal spline bases function can also be generated using a distribution of knots and multiple ordinal spline functions associated with the knots. More knots can be associated with a more nonlinear portion of the industrial process system, and fewer knots can be associated with a less nonlinear portion or a linear portion of the industrial process system.12-20-2012
20120290104SYSTEM AND METHOD FOR OPTIMIZING PLANT OPERATIONS - Embodiments of the present disclosure include systems and a method. In one embodiment, a system is provided. The system includes a risk calculation system configured to calculate a risk based on a static input and a dynamic input, and a decision support system configured to use the risk to derive a decision. The system also includes a plant control system configured to update operations of a plant based on the decision, wherein the decision predicts future plant conditions.11-15-2012
20130013086DYNAMIC MODEL GENERATION FOR IMPLEMENTING HYBRID LINEAR/NON-LINEAR CONTROLLER - A method of dynamic model selection for hybrid linear/non-linear process control includes developing a plurality of process models including at least one linear process model and at least one non-linear process model from inputs including dynamic process data from a processing system that runs a physical process. At least two of the plurality of process models are selected based on a performance comparison based on at least one metric, wherein the selected process models number less than a number of the plurality of process models received. A multi-model controller is generated that includes the selected process models. The physical process is simulated using the multi-model controller by applying the selected process models to obtain closed loop performance test data for each of the selected models. The performance test data is compared. A selected process model is then selected.01-10-2013
20090112335METHOD AND APPARATUS FOR INTELLIGENT CONTROL AND MONITORING IN A PROCESS CONTROL SYSTEM - A controller includes a control module to control operation of a process in response to control data, a plug-in module coupled to the control module as a non-layered, integrated extension thereof, and a model identification engine. The plug-in detects a change in the control data, and a collects the control data and data in connection with a condition of the process in response to the detected change. The model identification engine executes a plurality of model parameter identification cycles. Each cycle includes simulations of the process each having different simulation parameter values and each using the control data as an input, an estimation error calculation for each simulation based on an output of the simulation and based on the operating condition data, and a calculation of a model parameter value based on the estimation errors and simulation parameter values used in the simulation corresponding to each of the estimation errors.04-30-2009
20080234840Multi-Variable Operations - Controlling a multi-variable process involves multi-dimensional representation of the values (Qa-Qh) of the process variables (a-h) according to individual coordinate axes (Xa-Xh), and response based on historical values for the process-variables accumulated from multiple, earlier processes. An envelope (UL-LL) showing the best operating zone (‘BOZ’) for each process variable based on current values of the other variables is calculated from the accumulated historical values, and alarm conditions in which the current value of a variable lies outside the BOZ is rectified by changing the values (Qa-Qc) of manipulatable variables (a-c). Variable targets are achieved, alarms rectified and value optimisation realised using an inner envelope (UI-LI) derived from a subset of the BOZ-defining set of historical values. Where the alarm rate is low, operation is improved by narrowing the BOZ set to tighten the BOZ envelope (UL-LL) reducing an inner envelope where alarm rate remains acceptable, as a new BOZ.09-25-2008
20080234839CONTROL SYSTEM AND ADJUSTING METHOD THEREOF - A control system used to control a controlled plant includes a main control unit, a first tuning unit, and a second tuning unit. The control system regulated by two weighting parameters of a first multiple and a second multiple, robustness and rapid response are attained, and excess of the output signal the controlled plant generates disappears or approaches zero. The control system has technical features of objective bandwidth, offsetting of low frequency disturbance, and matching of transfer functions. By designing the main control unit, the first tuning unit, and the second tuning unit, regulating the two weighting parameters of the first multiple and the second multiple, and tuning the actual system, the above technical features are obtained.09-25-2008
20080215165Control loop for regulating a combustion process - In a control loop for regulating a combustion process in a plant (e.g., a power-generating plant, a waste incineration plant or a cement works) having a controlled system for converting material by way of the combustion process while supplying air, with at least one flame body being formed, and having at least one observation device for imaging the flame body, other sensors for determining the input data, at least one adjustment device that can be controlled by output data for supplying at least material and/or air, and a computer for evaluating the input data in relation to target values and for determining the output data by using a current process model, the computer has a feature extraction module that extracts features from the input data using an information measure. The features are informative for the target values, and are for use in an alternative process model.09-04-2008
20080215164Method and Device for Controlling Movement of a Movable Machine Element of a Machine - The movement of a machine element associated with a machine axis of a machine is simulated with a model by inputting in the model a movement to be performed by the machine element, and determining with the model at least one of a position profile, a velocity profile and a torque profile suitable for optimized movement of the movable machine element, as well as a predetermined quality function and a limitation for the movement of the machine axis. The position profile, velocity profile or torque profile is then used as a reference or pilot control variable in a control loop to control the moving machine element. The predetermined quality function is an integral of the square of a torque or of a variable which is directly related to the torque. The method can be used to optimize the guided movement of the machine element.09-04-2008
20110060424SYSTEM AND METHOD FOR PREDICTING FUTURE DISTURBANCES IN MODEL PREDICTIVE CONTROL APPLICATIONS - A system and method for predicting future disturbance in MPC applications by segregating a transient part and a steady state value associated with the disturbance. A dynamic state space model that includes a variable disturbance prediction module can be utilized for analyzing a dynamic behavior of a physical process associated with a process model. The process model represents a dynamic behavior of the physical process being controlled and the dynamic state space model represents current deviations from the process model and future deviations over a predetermined prediction horizon. A predicted trajectory can be calculated as a response to the initial conditions estimated by a Kalman Filter for the process model extended by a disturbance model. The output of the dynamic state space model utilized for the disturbance prediction can be further provided as an estimated input to a MPC.03-10-2011
20100286798ECONOMIC CALCULATIONS IN A PROCESS CONTROL SYSTEM - A process control system includes economic models disposed in communication with process control modules, as well as with sources of economic data, such as cost, throughput and profit data, and uses the economic models to determine useful economic parameters or information associated with the actual operation of the process plant at the time the plant is operating. The economic models can be used to provide financial statistics such as profitability, cost of manufactured product, etc. in real time based on the actual current operating state of the process and the business data associated with the finished product, raw materials, etc. These financial statistics can be used to drive alarms and alerts within the process network and be used as inputs to process plant optimizers, etc. to provide for better or more optimal control of the process and to provide a better understanding of the conditions which lead to maximum profitability of the plant.11-11-2010
20100292811METHOD FOR DETERMINING ADAPTED MEASURING VALUES AND/OR MODEL PARAMETERS FOR CONTROLLING THE AIR FLOW PATH OF INTERNAL COMBUSTION ENGINES - In a method for determining adapted measuring values and/or model parameters for controlling the air flow path of internal combustion engines, at least two measuring values and/or model parameters are simultaneously adapted, at least one total error variable which describes an inconsistency of the measuring values and model parameters being divided into individual correction variables for the measuring values and/or model parameters to be adapted, and these correction variables being applied to the measuring values and/or model parameters to be adapted.11-18-2010
20100305719METHOD AND SYSTEM FOR COMBINING FEEDBACK AND FEEDFORWARD IN MODEL PREDICTIVE CONTROL - A method and system for combining a feedback control and a feedforward control in a linear MPC to minimize effect of model uncertainty. An externally computed feedforward signal, which is more accurate and reliable, can be utilized in association with the MPC. A steady state relation between system parameters can be determined in order to compute the feedforward signal for a set of actuators associated with a non-linear system. A feedback MPC controller can then be designed. A state observer can be configured as an unknown input observer to estimate the effect of the feedforward signal. A strategy for manipulating the constraints of the MPC feedback signal can be implemented. A resulting control action for the actuators can be provided as a sum of corresponding feedback and feedforward signal while ensuring the constraints satisfaction.12-02-2010
20120283848METHOD FOR ASCERTAINING FUNCTIONAL PARAMETERS FOR A CONTROL UNIT - A method for ascertaining functional parameters for a control unit and to a control unit in which the provided method is carried out. The control unit is provided for controlling a technical system wherein, in the method, at least one target variable on a system response is specified and a variation of the functional parameters is carried out, from a response received to the functional parameters, a valuation being carried out of the set functional parameters while taking into account the at least one specified target variable.11-08-2012
20120283847Method for Controlling Behavioral Intervention of a Submodule - A method for controlling behavioral intervention of a submodule, where a device model is used to model and operate an automation system, where the device model comprises an input/output device, a first module including at least one submodule and a first controller, a supervisory controller and a control module including at least one control submodule. The submodule is provided with a behavior identification code and behavior information in addition to interconnection information. A respective control submodule having a specific behavior identification code is addressed by the supervisory controller to control the intervention in the submodule, where the addressed control submodule thereupon issues a control command containing the specific behavior identification code to all other submodules, and where the other submodules having the specific behavior identification code are induced to adopt a behavioral change based on the behavior information.11-08-2012
20130158680Hybrid Control System - A hybrid control system and a method for predicting a behavior of a physical system using the hybrid control system is disclosed. The hybrid control system may include a model inverting control system capable of implementing a model inverting control law and determining an active set of goals and limits and a model predictive control system capable of implementing a model predictive control law and utilizing the active set of goals and limits to determine current effector requests, the current effector requests being used to control behavior of the physical system.06-20-2013
20120029663COORDINATED JOINT MOTION CONTROL SYSTEM WITH POSITION ERROR CORRECTION - Disclosed are an articulated hydraulic machine supporting, control system and control method for same. The articulated hydraulic machine has an end effector for performing useful work. The control system is capable of controlling the end effector for automated movement along a preselected trajectory. The control system has a position error correction system to correct discrepancies between an actual end effector trajectory and a desired end effector trajectory. The correction system can employ one or more absolute position signals provided by one or more acceleration sensors supported by one or more movable machine elements. Good trajectory positioning and repeatability can be obtained. A two joystick controller system is enabled, which can in some cases facilitate the operator's task and enhance their work quality and productivity.02-02-2012
20120029662ADVANCED PROCESS CONTROL SYSTEM AND METHOD UTILIZING VIRTUAL METROLOGY WITH RELIANCE INDEX - An advanced process control (APC) system, an APC method, and a computer program product, which, when executed, performs an APC method are provided for incorporating virtual metrology (VM) into APC. The present inventions uses a reliance index (RI) and a global similarity index (GSI) to adjust at least one controller gain of a run-to-run (R2R) controller when the VM value of a workpiece is adopted to replace the actual measurement value of the workpiece. The RI is used for gauging the reliability of the VM value, and the GSI is used for assessing the degree of similarity between the set of process data for generating the VM value and all the sets of historical process data used for building the conjecturing model.02-02-2012
20120065744MODEL BASED CONTROL OF SHAPE MEMORY ALLOY DEVICE - A method of modeling a Shape Memory Alloy (SMA) element to predict a response of the SMA element includes obtaining the resistivity of the SMA element over a range of a physical property of the SMA element; correlating variations in the obtained resistivity with respect to the physical property of the SMA element to identify behavioral differences in the resistivity for the different phases of the SMA element; calculating a rate of change of the resistivity of the SMA element over a period of time; calculating the derivative of the rate of change in the resistivity of the SMA element over the period of time; and comparing real time data of the physical property to the derivative of the rate of change to predict the response of the shape memory alloy element.03-15-2012

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