Abstract: A controller directs the operation of an air pollution control (APC) system having one or more controllable operating parameters and a defined operating limit representing a regulatory limit on an allowed amount of pollutant to be emitted by the APC system. An interface receives data representing a value of a regulatory credit available for emitting less of the pollutant than the regulatory limit on the allowed amount of pollutant. A control processor (i) determines a target set point for each of at least one of the one or more controllable operating parameters, which will maximize the regulatory credits earned, based on the received data and (ii) to directs control of each of the at least one controllable operating parameter based on the determined target set point for that parameter.

Abstract: Methods and apparatus for controlling load on a machine are provided. The method includes determining a maximum value of a process variable using a predictive model of the machine while holding a control output associated with the process variable substantially constant over a prediction period, incrementing the control output if the determined maximum value of the process variable is within an allowable limit range, and setting the control output to the last value of the process variable that did not cause the process value to exceed the allowable range.

Abstract: A positioning control apparatus includes a position detector 102 which detects the position of a positioning object 101, a target position generator 103 which generates a target position signal based on an output from the position detector, and a driving unit 104 which drives the positioning object based on an output from the target position generator. The target position generator 103 includes a control unit which sets a temporary target position of the positioning object and performs the learning of a reference position of the positioning object. A control gain given to the driving unit includes a first control gain, which is valid until the positioning object reaches the set temporary target position, and a second control gain, which is smaller than the first control gain and which is valid after the time when the positioning object has reached the temporary target position until the reference position learning is completed.

Abstract: A method to adjust a boiler having a flue gas duct including: sensing flue gas emissions in the gas duct with a plurality of emission sensors arranged in an array; generating a multidimensional graphical depiction of the flue gas emissions by plotting sensor data captured from the emission sensor; adjusting the boiler to modify the distribution of flue gases in the gas duct; generating a subsequent multidimensional graphical depiction of the flue gas emissions by plotting sensor data captured subsequent to the boiler adjustment, and repeating these steps until the graphical depiction displays an acceptable plot of flue gas emissions.

Abstract: A computer method and apparatus for adaptive model predictive control (MPC) of multivariable processes is disclosed. The adaptive MPC system can perform automatic implementation for a new MPC controller, and, for an existing MPC controller, it can perform automatic maintenance when necessary. The adaptive MPC system consists of three modules: an MPC control module, an online identification module and a control monitor module. In MPC implementation, the online identification module and the MPC control module work together and perform various steps in MPC implementation automatically and efficiently. When the MPC controller is online, the control monitor module continuously monitors the MPC performance and model quality. When control performance becomes poor and considerable model degradation is detected, monitor module will start the maintenance by activating the online identification module. The identification module will re-identify the model and replace the old model.

Abstract: A process controller that may be used to control a process having a set of process outputs effected by a set of process control input signals includes a multiple-input/multiple output controller that uses the process outputs to develop the set of process control input signals and a process model, which may be a non-linear process model, that receives the set of process control input signals to produce a prediction signal for one or more of the process outputs. The multiple-input/multiple-output control element includes another process model, which may be a standard linear process model, to develop a prediction vector for each of the process outputs and includes a correction unit that modifies the prediction vector for the one or more of the process outputs using the prediction signal for the one or more of the process outputs to thereby compensate for the non-linearities of the process.

Abstract: The disclosed process control system makes use of Optimal control (OC) and model predictive control (MPC) techniques for selection of the Expert Systems (ES) targets values U. The ES target values U are selected to minimize the performance criterion J. A mathematical model of an extended system given by the process P and the ES is developed. This hybrid mathematical model has both continuous dynamics and logical relationships. Controlled variables of the mathematical model are the ES target values U and inputs are the measurements y and the performance criterion J. The OC and/or MPC techniques are used to compute values U. An optimizer of the OC/MPC selects values of the ES target values U only. This activity has lower sampling rates than selection of controller values, which simplifies the design of the OC/MPC controller.

Abstract: A method is disclosed for removing the dynamics of the PID controllers from a Model Predictive Controller that was developed using identification testing of a process. This allows creation of valve-based off-line process simulators and provides methods to generate new MPC controllers for complex multivariable process control when a change has been made in any PID control configuration or tuning and to do so without having to conduct new identification testing of the process.

Abstract: An apparatus, system, and method for optimization. The apparatus, system, and method include a mechanism for ending an optimization when all models within one design tolerance of an optimum model have been simulated.

Abstract: A method and apparatus that generates an estimate of a property of a batch process uses a non-parametric model to generate a plurality of rate of reaction estimates associated with the batch process. Each rate of reaction estimate may correspond, for example, to a particular time during the batch process. The plurality of rate of reaction estimates are then integrated to generate an estimate of a property of the batch at the particular time.

Abstract: A digital control device for tracking a sine wave according to the present invention has a compensator, a control object and a feedback gain. An input into the compensator is a signal obtained by subtracting a control quantity from a reference value. An input into the control object is a signal obtained by subtracting an output of the feedback gain from an output of the compensator. A transfer function of the compensator is (k2z+k1)/(z2?2z cos ?T+1), where ? is an angular frequency, T is a sampling period, z is a z operator, and k1 and k2 are constants. Thus, a second-order compensator can be configured, with which a sinusoidal reference waveform can be tracked simply and with high accuracy.

Abstract: The present invention relates to a system and a method for developing an engine model. The system broadly comprises a module for generating a state variable model of an engine, which module receives a plurality of inputs to an engine representative of a particular flight condition and generates a set of estimated engine parameters representative of the model. The system further comprises a comparator for comparing the set of estimated engine parameters to a set of measured engine parameters for generating a set of residuals and an artificial neural network module to be trained and to be used in an implementation configuration after training has been completed. The artificial neural network receives the set of residuals and the engine inputs during a training phase and generates a set of estimated residuals representative of the engine condition.

Abstract: In accordance with an embodiment of the present invention, attractive haptic objects are associated with a target region for performing a medical procedure and repulsive haptic objects are associated with anatomical features to be avoided during the medical procedure. A surgeon may perform surgical planning by moving the haptic device around. The surgeon moves the haptic device until a pose is found where haptic cues from the attractive haptic objects are active indicating that a medical device if attached to the haptic device would reach the target region, and haptic cues from the repulsive haptic objects are inactive indicating that the medical device would not penetrate any of the anatomical features to be avoided. During the planning process a virtual tool may be displayed on a display device to indicate the position and/or movement of the medical device if the medical device had been coupled to the haptic device.

Abstract: In accordance with an embodiment of the present invention, a method for haptic sculpting of a physical object using a haptic device comprises defining a haptic object for aiding in the sculpting of the physical object, dynamically modifying a configuration of the haptic object in response to a medical device coupled to the haptic device being in proximity to the haptic object, and providing haptic cues to guide a user of the medical device in sculpting at least a portion of the physical object corresponding to the haptic object.

Abstract: A modular formulation of model predictive controller is proposed by this invention (400). The modular formulation offers a robust model predictive controller with unified method of tuning. Explicit use of material and energy balance provides stable and robust performance (412). Use of economic based tuning offers a unified tuning method (411). The unified tuning method establishes consistency of control actions resulting from the steady state optimization and the dynamic control (400). Further, a method for formulating and tuning integrated optimization and control using multiple modular model predictive controllers is presented in this invention (100). The method incorporates a direct and explicit method of integration of a number of modular model predictive controllers. The integration of the modular controllers is achieved seamlessly without requiring any further tuning adjustments (200).

Abstract: A control device for controlling the motion of a movable part in a machine while suppressing vibration generated in the movable part. The control device includes a state-variable estimating section for estimating a state variable of a controlled system in the machine and outputting an estimated state variable; a reference model for outputting an ideal controlled variable for the controlled system; a compensator for calculating a compensation value for correcting a control input for the controlled system, based on a difference between the estimated state variable and the ideal controlled variable; and a feedback control section for determining the control input, based on a deviation between a desired command value and one of the estimated state variable and the ideal controlled variable.

Abstract: By applying time-frequency analysis to a given standard iterative learning control or ILC an adaptive filter for the learned feed-forward loop is designed. This time varying filter varies according to the momentary frequency content of the error signal and allows to discriminate between areas of deterministic and stochastic error. Its application results in selective application of ILC to those intervals where error signals of high level are concentrated and allows application of a single ILC acquisition to different setpoint trajectories. The adaptive filter finds particular use in lithographic scanning systems where it is used for varying scan length.

Abstract: A force-feedback input device includes an operation section to be operated by an operator; a position detection section for detecting the operating state of the operation section; a force-feedback generation section for applying force feedback to the operation section; an external device, and a haptic commander for controlling the driving of the force-feedback generation section on the basis of position information output from the position detection section and part data transmitted from the external device, and applying predetermined force feedback to the operation section according to the operating state thereof. The haptic commander includes a part data storage section for storing the part data transmitted from the external device, and a part data management table for managing the enabled and disabled states of each piece of the part data stored in the part data storage section.

Abstract: An apparatus and method is disclosed for automatically controlling single-input-multi-output (SIMO) systems or processes. The control output signals of a plurality of single-input-single-output (SISO) automatic controllers are combined by a combined output setter so that these SISO controllers are converted to a multi-input-single-output (MISO) automatic controller based on certain criteria; and its resulting controller output signal is able to manipulate only one actuator to control a plurality of continuous process variables or attempt to minimize a plurality of error signals between the setpoints and their corresponding process variables.

Abstract: A method for controlling a non-linear process includes the steps of first providing a controller that is operable to receive inputs representing measured variables of the process and predicting on an output of the controller predicted control values for manipulatible variables that control the process. An expert system is provided that models the actions of an operator of the process over an operating region of the process that represents a set of rules for actions to be taken by an operator upon the occurrence of predetermined conditions in the operation of the process. The operation of the controller is modified with the expert system when one of the predetermined conditions exists.

Abstract: In a method and apparatus for predicting process results of objects being processed in a processing chamber of a processing apparatus, operation data and process result data obtained at the time of processing each of the objects are collected and a multivariate analysis is performed on the basis of the collected operation data and process result data to obtain a first correlation therebetween. Process results are predicted on the basis of the first correlation. Weighting coefficients for the respective operation data are set on the basis of the predicted process results. A second correlation between the weighted operation data and the process result data is obtained by a multivariate analysis. Process results are predicted by using operation data, obtained when objects other than the objects used to obtain the second correlation are processed, on the basis of the second correlation.

Abstract: A kiln thermal and combustion control. A predictive model is provided of the dynamics of selected aspects of the operation of the system for modeling the dynamics thereof. The model has at least two discrete models associated therewith that model at least two of the selected aspects, the at least two discrete models having different dynamic responses. An optimizer receives desired values for the selected aspects of the operation of the system modeled by the model and optimizes the inputs to the model to minimize error between the predicted and desired values. A control input device then applies the optimized input values to the system after optimization thereof.

Abstract: A technique is provided for automatic optimization of a splice loss estimator of a fiber splicer (1), where the splice loss estimator is adapted, in a splice loss estimation procedure, to estimate the splice losses (Lti) of splices (i) of optical fibers as produced by the fiber splicer from images taken of the optical fibers at the splicing thereof, and the splice loss estimation procedure includes the use of splice loss estimation parameters (Pj). The estimator estimates splice losses based on information (Cij) obtained from the images and the estimation parameters. Further, the splice losses are measured by means of a measurement instrument (3). The estimated (Lti) and measured (LMi) splice losses, and the information obtained from the images are uploaded (71) into an off-line computer (5) and the key estimation parameters are automatically optimized by the selection of any solution within the Bellcore accuracy criteria (75), whereafter the optimized estimation parameters are downloaded (81) to the splicer.

Abstract: Non-linear model with disturbance rejection. A method for training a non linear model for predicting an output parameter of a system is disclosed that operates in an environment having associated therewith slow varying and unmeasurable disturbances. An input layer is provided having a plurality of inputs and an output layer is provided having at least one output for providing the output parameter. A data set of historical data taken over a time line at periodic intervals is generated for use in training the model. The model is operable to map the input layer through a stored representation to the output layer. Training of the model involves training the stored representation on the historical data set to provide rejection of the disturbances in the stored representation.

Abstract: At least one of the multiple process parameters (MPPs) is a controllable process parameter (CTPP) and one is a targeted process parameter (TPP). The process also has a defined target limit (DTV) representing a first limit on an actual average value (AAV) of the TPP. A first logical controller predicts future average values (FAVs) of the TPP based on the AAVs of the TPP over a first prior time period and the DTV. A second logical controller establishes a further target limit (FTV) representing a second limit on the AAV of the TPP based on one or more of the predicted FAVs, and also determines a target set point for each CTPP based on the AAVs of the TPP over a prior time period and the FTV. The second logical controller directs control of each CTPP in accordance with the determined target set point.

Abstract: Stochastic control problems of linear systems in high dimensions are solved by modeling a structured Markov Decision Process (MDP). A state space for the MDP is a polyhedron in a Euclidean space and one or more actions that are feasible in a state of the state space are linearly constrained with respect to the state. One or more approximations are built from above and from below to a value function for the state using representations that facilitate the computation of approximately optimal actions at any given state by linear programming.

Abstract: A controller directs performance of a process having multiple process parameters (MPPs), including a controllable process parameter (CTPP), a targeted process parameter (TPP), a defined target value (DTV) representing a limit on an actual average value (AAV) of the TPP over a defined moving time period of length TPLAAV. A storage device stores historical data representing the AVs of the TPP at various times over a prior time period (PTP) having a length of at least TPLAAV. A processor predicts future average values (FAVs) of the TPP over a future time period (FTP) based on the stored historical data and the current values of the MPPs. The processor also determines a target set point for each CTPP based on the predicted FAVs, the current values of the MPPs and the DTV, and directs control of each CTPP in accordance with the determined target set point for that CTPP.

Abstract: In a predictive action determination apparatus (10), a state observation section (12) observes a state with respect to an environment (11) and obtains state data s(t). An environment prediction section (13) predicts, based on the state data s(t), a future state change in the environment. A target state determination section (15) determines, as a target state, a future state suitable for action determination with reference to a state value storage section (14). A prediction-based action determination section (16) determines an action based on a determined target state.

Abstract: A method for identifying a control path of a controlled system, and more particularly to a method for identifying a control path in the presence of deterministic perturbations is described. At least one deterministic perturbation correcting signal is determined in a first identification process, and the perturbation correcting signal is stored in the form of a function. A control path of the controlled system is identified in a second identification process by adding to the controlled system the at least one stored deterministic perturbation correcting signal with a negative feedback. The method can be used with machine tools, production machines and/or robots which demand a high control accuracy and/or a high-quality control characteristic. In particular, perturbation effects due to slot latching in motors, in particular linear motors, can be minimized.

Abstract: A computer based method for controlling an industrial process that includes at least two unit processes, including the steps of directly controlling the process by a control model including one or more algorithms, and automatic diagnosis of the validity of the process data retrieved from the process for the purpose of preventing irrelevant process data from being used as an input in the control model.

Abstract: An embodiment of the present invention uses an on-board microcontroller of a household appliance to receive signals from a directionless encoder attached to a knob for receiving user input and processing the signals from the direction encoder according to a control algorithm. The microcontroller may increase the output to an electric heating element on the appliance based on a variety of criteria, including either direction of rotation, current heating element setting, or a change in directional rotation. Alternatively, or in addition, the microcontroller may provide various types of graduate visual or aural feedback to the user regarding the output provided by the microcontroller.

Abstract: A motor control device comprises: a current limiter 115 for limiting a current-instruction signal Ir for a motor 18 and for turning a limiting signal L from off to on when the current-instruction signal Ir reaches a predetermined value; a model-position-generating part 220 having a model of an equivalent position-control system that includes characteristics of the motor control device 100 and a control target object, and calculating rotational position of the motor 18 as a model-position signal ?m by inputting to the model a position-instruction signal ?r; a correctional acceleration-generating part 240 for generating a correctional acceleration signal ?se based on a correctional position deviation ?se when the limiting signal L turns on; and a position-instruction-generating part 260 for generating the position-instruction signal ?r based on an acceleration deviation ?r that is equal to the difference between the original-acceleration-instruction signal Va and the correctional acceleration signal ?se.

Abstract: To provide an algorithm-based optimizing controller which can search for an optimal solution in line with user requirements with reduced time requirements for optimization. An interactive optimizer 310 searches for an optimal solution of a responsivity calculator 342 using a GA while repeatedly evaluating, interactively, responsivity outputted from the responsivity calculator 342 which receives control coefficients that affect control characteristics of the interactive optimizer 310 and outputs the responsivity.

Abstract: A method for estimating load inertia and system for controlling motor speed using inverse model are provided, suitable for an alternating current (AC) servo module. An inverse system is connected to an actual system to inversely deduce a substantial torque output, wherein when the value of the speed signal generated by the actual system gradually increases, a net inertia ratio is accumulated; whereas when the value of the speed signal generated by the actual system increases to a fixed value, the net inertia ratio is updated.

Abstract: A process control configuration system is provided for use in creating or viewing an integrated optimization and control block that implements an optimization routine and a multiple-input/multiple-output control routine. The configuration system may enable a user to display or configure the optimizer or the control routine. A storage routine may store information pertaining to a plurality of control and auxiliary variables and to a plurality of manipulated variables to be used by the optimization routine and/or the control routine. A display routine may present a display to a user regarding the information pertaining to the plurality of control and auxiliary variables and to the plurality of manipulated variables.

Abstract: A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bi variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y.

Abstract: The present invention provides a method for controlling nonlinear process control problems. This method involves first utilizing modeling tools to identify variable inputs and controlled variables associated with the process, wherein at least one variable input is a manipulated variable. The modeling tools are further operable to determine relationships between the variable inputs and controlled variables. A control system that provides inputs to and acts on inputs from the modeling tools tunes one or more manipulated variable inputs to achieve a desired result like greater efficiency, higher quality, or greater consistency.

Abstract: A linear predictive system for a DC—DC converter including a linear predictive controller, first and second adders and a multiplier. The DC—DC converter generates an output signal and includes a digital compensation block that converts a feedback error signal into a main duty cycle signal. The linear predictive controller predicts linear changes of the main duty cycle signal in response to changes of the output signal and provides a predictive duty cycle signal. The first adder subtracts the predictive duty cycle signal from the main duty cycle signal to provide a duty cycle delta. The multiplier multiplies the duty cycle delta by a gain factor to provide a duty cycle delta sample. The second adder adds the duty cycle delta sample to the first duty cycle signal to generate an adjusted duty cycle signal.

Abstract: A method and apparatus for estimating a state parameter in a nonlinear discrete time system are provided. The method for estimating a state parameter has the steps of (a) predicting a state parameter at a current time using an estimated state parameter at a previous time and a system dynamics; and (b) estimating an optimal state parameter at the current time from the state parameter predicted in the step (a) and a system output parameter measured at the current time, using a geometric data fusion method. Since the method and apparatus for estimating a state parameter have an excellent estimation performance particularly when nonlinearity is great or the error in an estimated initial value is big, the method and apparatus solve many problems, which cannot be solved by the conventional extended Kalman filter, and more improve the performance of estimating a state parameter, by analyzing the system characteristic and then appropriately utilizing constraints such as the operation range of the state parameter.

Abstract: A facility for selecting and refining electrical parameters for processing a microelectronic workpiece in a processing chamber is described. The facility initially configures the electrical parameters in accordance with either a numerical of the processing chamber or experimental data derived from operating the actual processing chamber. After a workpiece is processed with the initial parameter configuration, the results are measured and a sensitivity matrix based upon the numerical model of the processing chamber is used to select new parameters that correct for any deficiencies measured in the processing of the first workpiece. These parameters are then used in processing a second workpiece, which may be similarly measured, and the results used to further refine the parameters.

Abstract: A hybrid cascade Model-Based Predictive control (MBPC) and conventional control system for thermal processing equipment of semiconductor substrates, and more in particular for vertical thermal reactors is described. In one embodiment, the conventional control system is based on a PID controller. In one embodiment, the MBPC algorithm is based on both multiple linear dynamic mathematical models and non-linear static mathematical models, which are derived from the closed-loop modeling control data by using the closed-loop identification method. In order to achieve effective dynamic linear models, the desired temperature control range is divided into several temperature sub-ranges. For each temperature sub-range, and for each heating zone, a corresponding dynamic model is identified. During temperature ramp up/down, the control system is provided with a fuzzy control logic and inference engine that switches the dynamic models automatically according to the actual temperature.

Abstract: Predictions may be made regarding heat removal requirements depending on certain operational characteristics of an information processing system which have been monitored over time. A fan may be controlled based on the observed operational characteristics and based on the predictions made regarding the heat removal requirements for the system. For example, system utilization by applications may be monitored, possibly along with system performance parameters such as power level and frequency. These and other operational characteristics may be used to predict heat generation so that a fan may be controlled to anticipate temperature changes and thereby flatten temperature curves over time. This may be done in addition to monitoring the ambient temperature of the system and reacting to temperature spikes that may have already occurred.

Abstract: A method for calculating the health of a process control loop based on multiple key assessments of the loop's performance. The method uses the concepts of baselines and thresholds. The typical calculation of loop health is the average of the Percent Towards Threshold for all the assessments divided by the economic significance of the loop.

Abstract: A plant is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) models the plant by providing a predicted output which is combined with a desired output to generate an error that is back propagated through an inverse control network to generate a control error signal that is input to a distributed control system to vary the control inputs to the plant in order to change the output y(t) to meet the desired output. The inverse model represents the dependencies of the plant output on the control variables parameterized by external influences to the plant.

Abstract: A control apparatus is provided for eliminating a slippage in control timing between the input/output of a controlled object, even when the control object exhibits a relatively large dynamic characteristic such as a phase delay, a dead time, or the like, to improve the stability and the controllability of the control. The control apparatus comprises a state predictor for calculating a predicted value of a value indicative of an output of a controlled object based on a prediction algorithm, and a DSM controller for calculating a control input to the controlled object based on one modulation algorithm selected from a ? modulation algorithm, a ?? modulation algorithm, and a ?? modulation algorithm for controlling the output of the controlled object in accordance with the calculated predicted value.

Abstract: A method is disclosed for removing the dynamics of the PID controllers from a Model Predictive Controller that was developed using identification testing of a process. This allows creation of valve-based off-line process simulators and provides methods to generate new MPC controllers for complex multivariable process control when a change has been made in any PID control configuration or tuning and to do so without having to conduct new identification testing of the process.

Abstract: Methods and apparatus, including computer program products, implementing techniques for declaring application data. The techniques include establishing a model, the model implementing application logic of an application; establishing at least one view for presenting the model; establishing at least one controller for manipulating the model; establishing at least one storage area, the storage area relating to the controller; and establishing at least one access method for storing and accessing application data in the storage area according to a predetermined structure.

Abstract: A method for automated system identification of a linear system is disclosed. A model structure is selected and one or more reference signal values are generated for input into the system. Input signal values and output signal values are retrieved from the system and system identification is performed on the model structure using the input signal values, the output signal values, and the one or more reference signal values. A point model, obtained as a result of the system identification, is then verified for accuracy.

Abstract: A method for evaluating whether a measurable disturbance on a process controlled by a feedback controller is suitable for feed-forward control. The disturbance is measured. The controller output signal due to the disturbance is measured. A first reference signal corresponding to the controller output signal is measured when responding to disturbance entering before the process. A second reference signal corresponding to the controller output signal is generated when responding to a disturbance entering after the process. It is estimated where in the process the disturbance enters by comparing the measured controller output signal due to the disturbance with the reference signals. It is evaluated whether the disturbance is suitable for feed-forward control depending on where in the process the disturbance enters.

Abstract: A method and apparatus is provided for a process control based on an estimated process result. The method comprises processing a workpiece using a processing tool, receiving trace data associated with the processing of the workpiece from the processing tool and estimating at least one process result of the workpiece based on at least a portion of the received trace data. The method further comprises adjusting processing of a next workpiece based on the estimated at least one process result.