Abstract: An adaptive control system is described. The system includes a control having a plurality of control parameters, the control parameters providing for control of an associated plant. The control parameters are tuned using a prediction error filter, the prediction error filter selecting values of the control parameters that minimize the values of a prediction error between actual and predicted values of an autoregressive process.

Abstract: A robust digital controller is equipped with a high degree of approximation and is able to incorporate a novel two-degree-of-freedom robust digital control system without substantially considering the magnitude of the control inputs and there is provided its designing device. A control compensating means is configured as an integral type control system in which a discrete transfer function Wry (z) between a target value r and a controlled variable y is approximated to a higher-approximate quadratic approximate model transfer function Wm (z) and an arithmetic processing can be performed within the digital controller based on the model transfer function Wm (z). Further, the designing device automatically calculates parameters constituting the control system. Consequently, a robust digital controller can be easily realized that is equipped with a high degree of approximation as compared with a conventional approximate digital control system for realizing a first-order model and is robust against output noises.

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: A system and method for controlling a flow of a fluid using an adaptive closed-loop-control algorithm is described. One embodiment includes a method for receiving a set point indicator and/or a sensor indicator generated by a sensor. A response time of a closed-loop-control algorithm is modified when at least one threshold value is satisfied based on the sensor indicator and/or the set point indicator. The closed-loop-control algorithm, which is implemented by a flow controller, is stabilized in response to the modifying of the response time of the closed-loop-control algorithm by adjusting at least one parameter associated with the closed-loop-control algorithm.

Abstract: 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.

Abstract: A state based adaptive feedback/feedforward PID controller includes a model set component, communicatively coupled to a process input, having a state variable defining a number of process regions, and a number of models grouped into the process regions. Each of the grouped models includes a plurality of parameters having a value selected from a set of predetermined initial values assigned to the respective parameter. The adaptive controller further includes an error generator communicatively coupled to the model set component and a process output. The error generator configured to generate a model error signal representative of the difference between a model output signal and a process output signal. The error generator, communicatively coupled to a model evaluation component, is configured to compute a model squared error corresponding to a model and correlating the model squared error to parameter values represented in the model.

Abstract: A controller and associated methods. A controller has an input for a controlled variable; a proportional term; an integral term; and a predictive term. The predictive term has an error-predictive subterm including at least a past value of an error; and a command-predictive subterm including at least one past value of a control command value; and an output. The controller can apply the predictive term and at least one of the proportional term and integral term to generate a command for the output. A method for controlling a process variable is also provided including applying a predictive term having an error-predictive subterm that includes at least a past value of an error and a command-predictive subterm that has at least a past value of a control command. The method further includes the step of generating a command based on the results of the applying the terms.

Abstract: A system and method for user configuration of an autotuning algorithm for a PID controller. User input is received via a Graphical User Interface (GUI) indicating a desired characteristic of a PID controller, such as stiffness or response time. The system is excited via a proportional controller to characterize the intrinsic behavior of the system, i.e., to calculate a system transfer function. An autotuning algorithm is configured in accordance with the user input. The configured autotuning algorithm is applied to the transfer function to generate gain values for the PID controller resulting in the PID controller characteristic specified by the user. The PID controller gains are loaded into the PID controller hardware or software, thereby configuring the PID controller to operate according to the desired characteristic specified by the user. The user may trigger and view a step response of the system to review the results of the tuning process.

Abstract: A method and a device for tuning and control of industrial processes having varying material flow rate. An adder is configured to add excitation signals to the controller output signal. A measurement system is configured to measure a property in response to the excitation signals. A model based tuning unit is adapted to estimate the value of at least one parameter with unknown value of a process model structure describing the effect of varying material flow rate, based on the measurements of the property and the output signal from the controller, and to calculate a model that describes the dynamics from controller output to controller input based on the estimated value of the parameter, and to perform model based tuning of the controller based on the model that describes the dynamics from controller output to controller input.

Abstract: 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.

Abstract: An auto-tuner is adapted to be used in a distributed process control network having a communications network that communicatively couples a process controller, which executes a process control routine, and one or more process devices used in a process control loop. The auto-tuner includes a first tuning element configured to cause a control entity to force the process loop to undergo an auto-tuning procedure and a tuning data stack operating within one of the process devices to receive and store a tuning signal associated with the control entity along with a time stamp indicating the time the tuning signal was acted on by the device. A measurement data stack is disposed in the same or a different process device and operates to receive and store a response or measurement signal generated by the process device along with a time stamp indicating when the response signal was generated or detected.

Abstract: A method for controlling a process variable as it approaches a predetermined value (setpoint) so that the setpoint is not exceeded. The method employs a time domain polynomial equation in a feedback configuration and utilizes a controller that acts as an On/Off controller until the process variable approaches setpoint. As the process variable approaches setpoint, the controller acts as a fast responding analog controller thereby “tailoring” a control variable to precisely bring the process variable to the setpoint without exceeding or overshooting the setpoint.

Abstract: The present disclosure relates to monitoring of electromechanical oscillations in electric power systems, and their identification by an adaptive algorithm based on a repeatedly measured and evaluated signal. In order for an estimation of parameters of a model of the power system to reasonably converge, proper initialization of the recursive calculation is required, including the definition of tuning parameters constraining the model and the calculation. Initialization for a second signal to be exploited can then be simplified by copying the set of tuning parameters tuned previously for a different signal. A conditioning gain multiplying the second signal establishes compatibility between the different signals, and a signal pre-filter in turn discards contributions beyond a frequency band comprising typical electromagnetic oscillations.

Abstract: A method of creating and using an adaptive DMC type or other MPC controller includes using a model switching technique to periodically determine a process model, such as a parameterized process model, for a process loop on-line during operation of the process. The method then uses the process model to generate an MPC control model and creates and downloads an MPC controller algorithm to an MPC controller based on the new control model while the MPC controller is operating on-line. This technique, which is generally applicable to single-loop MPC controllers and is particularly useful in MPC controllers with a control horizon of one or two, enables an MPC controller to be adapted during the normal operation of the process, so as to change the process model on which the MPC controller is based to thereby account for process changes.

Abstract: Embodiments of the invention provide an adaptive notch filter that employs a power detector as a feedback control mechanism to steer the notch filter. One such embodiment of the invention provides adaptive control of the notch filter capacitor to tune the notch filter frequency based upon a diode power detector. One embodiment of the invention provides a system including multiple cascaded adaptive notch filters each having a feedback control method from a power detector to separately control each filter. For one embodiment of the invention a method is disclosed for tuning an adaptive notch filter using a dithering process to determine a minimum power output at the power detector. One embodiment includes an adaptive notch filter implementing a device with a tunable resonance frequency.

Abstract: A method and apparatus for controlling a non-linear mill. A linear controller is provided having a linear gain k that is operable to receive inputs representing measured variables of the plant and predict on an output of the linear controller predicted control values for manipulatible variables that control the plant. A non-linear model of the plant is provided for storing a representation of the plant over a trained region of the operating input space and having a steady-state gain K associated therewith. The gain k of the linear model is adjusted with the gain K of the non-linear model in accordance with a predetermined relationship as the measured variables change the operating region of the input space at which the linear controller is predicting the values for the manipulatible variables. The predicted manipulatible variables are then output after the step of adjusting the gain k.

Abstract: A method of predictive control for a single input, single output (SISO) system, including modeling the SISO system with model factors, detecting output from the SISO system, estimating a filtered disturbance from the output, determining a steady state target state from the filtered disturbance and a steady state target output, populating a dynamic optimization solution table using the model factors and a main tuning parameter, and determining an optimum input from the dynamic optimization solution table. Determining an optimum input includes determining a time varying parameter, determining a potential optimum input from the time varying parameter, and checking whether the potential optimum input is the optimum input.

Abstract: The invention relates to a method in which at least one component of a technical plant is controlled by means of a PI controller. The actual value of the regulating parameter is continuously determined during operation of the plant and the amplification factor of the PI controller is altered depending on the time relationship of the actual value, until the actual value remains with a tolerance band relative to the set value. The invention further relates to a controller for carrying out said method.

Abstract: A system and method for controlling a mass flow controller to have a constant control loop gain under a variety of different types of fluids and operating conditions, and for configuring the mass flow controller for operation with a fluid and/or operating conditions different from that used during a production of the mass flow controller. Further, the system and method includes providing control by reducing the effects of hysteresis in solenoid actuated devices by providing a non-operational signal to the solenoid actuated device.

Abstract: An autotuning method using an integral of a relay feedback response is disclosed, which obtains an ultimate data and frequency model of a process with greatly removing effects of harmonics by using integral of a relay feedback signal. The autotuning method using an integral of a relay feedback response comprises the first step for integrating a process data; and the second step for removing effects of harmonics after the first step and computing an ultimate gain, a frequency model and a parametric process model.

Abstract: Systems and methods are provided for optimizing the performance and/or allocation of constrained resources in a dynamic computing environment using adaptive regulatory control methods. For example, systems and methods for providing constrained optimization in a dynamic computing system implement model-based adaptive (self-tuning) regulatory control schemes that are designed to handle the system dynamics and which take into consideration control costs (such as the overheads of changing resource allocations and performance degradation due to transient load imbalances) to find an optimal solution. To facilitate practical application, a dual control architecture is disclosed which combines a heuristic fixed step control process that is implemented when there is no valid system model for model-based control. A system model is continually obtained and validated during run-time to adapt control parameters to variations in system dynamics.

Abstract: A method, apparatus and computer program product for performing integrated model generation and controller tuning is presented. The method, apparatus, and computer program product acquires data regarding the system. The data is then analyzed and preprocessed to provide modified data, and a model is generating from this modified data. A controller design is determined from the modified data and the model, and controller parameters are calculated from the modified data and the model.

Abstract: In a position control device having a disturbance suppression function, loop gain is calibrated without stopping disturbance suppression control. The position control device has, for disturbance suppression control, a feedback controller for changing the loop characteristic, a table for storing a target gain according to a disturbance frequency, and a gain calibration section for calibrating open loop gain. The gain is calibrated using a target gain in the table according to the change of the loop characteristic of the feedback controller. Open loop gain can be calibrated without interrupting the disturbance suppression control, so the open loop gain can be accurately calibrated without being affected by disturbance, and accurate position control is possible.

Abstract: In adjustment of the control parameters of a magnetic suspension system, an object of the present invention is to provide a technique for adjusting the control parameters to optimum values automatically by employing the iterative feedback tuning method, without increasing the complexity of the program, the amount of calculation and the overall size of the system, and still reducing the amount of calculation required for calculation of the safety margin (gain margin and phase margin). In a method of automatic adjustment of control parameters according to the invention, when applying the iterative feedback tuning method to a feedback control system for a magnetic suspension system, only those control parameters that have a significant effect on the control performance of the system are selectively adopted/discarded from among control parameters so that the control parameters are guided to optimum values to obtain a response waveform desired by a designer directly from input/output data of a control subject.

Abstract: An automatic gain adjustment device and method capable of accurately adjusting a gain with a simple structure. In an automatic gain adjustment method of a feedback control system that uses a phase difference between an output signal obtained from a controlled object and an input signal while controlling the object based on the input signal, a phase shift amount is set to cause a frequency of the input signal to coincide with the crossover frequency at which the open loop gain forming the feedback control system becomes 0 dB, and a phase of the input signal is shifted based on the phase shift amount.

Abstract: A fast and reliable technique for tuning multivariable model predictive controllers (MPCs) that accounts for performance and robustness is provided. Specifically, the technique automatically yields tuning weights for the MPC based on performance and robustness requirements. The tuning weights are parameters of closed-loop transfer functions which are directly linked to performance and robustness requirements. Automatically searching the tuning parameters in their proper ranges assures that the controller is optimal and robust. This technique will deliver the traditional requirements of stability, performance and robustness, while at the same time enabling users to design their closed-loop behavior in terms of the physical domain. The method permits the user to favor one measurement over another, or to use one actuator more than another.

Abstract: A positioning control system for positioning a moving element on a basis of position command data is provided with a feedback loop. The system is also provided with a loop gain modifier for determining a loop gain, which is to be used in a following positioning operation, on a basis of a difference between an amount of overshoot measured in a current positioning operation and a predetermined tolerance or on a basis of a difference between an amount of overshoot measured in a current positioning operation and a first predetermined tolerance and a difference between an amount of undershoot measured in the current positioning operation and a second predetermined tolerance. The first and second tolerances may preferably be the same in absolute value. The moving element may specifically be a steerable mirror for drilling holes in a work by reflecting a laser beam. Also disclosed is a laser drilling machine including the system.

Abstract: A control device receives an output voltage of a voltage-up converter from a voltage sensor, and calculates a feedback preliminary voltage control value determined by the difference between a voltage control value and the output voltage. The control device corrects the calculated feedback preliminary voltage control value in accordance with the output voltage to calculate a feedback voltage control value where the follow-up property of the output voltage with respect to the voltage control value is equal to a reference property. The control device controls the voltage-up converter using a feedback voltage control value. The voltage-up converter converts a direct current voltage from a DC power supply into the output voltage maintaining the follow-up property of the output voltage with respect to the voltage control value at the reference property.

Abstract: There is realized a control device capable of speeding up an output response without causing deterioration in the resolution of a time-proportional output. The control device controls a control object at a ratio of ON-time to OFF-time for a time-proportional output. The control device comprises a PID computation unit for executing PID computation of deviation between a set value and a measured value from the control object, an integrator for sequentially adding up control output values each representing the result of the PID computation executed by the PID computation unit, an ON/OFF determination unit for determining whether the time-proportional output is turned ON or OFF on the basis of an integrated value of the integrator, and a time-proportional output unit for turning the time-proportional output ON/OFF on the basis of a determination value of the ON/OFF determination unit.

Abstract: A system and method for controlling a mass flow controller to have a constant control loop gain under a variety of different types of fluids and operating conditions, and for configuring the mass flow controller for operation with a fluid and/or operating conditions different from that used during a production of the mass flow controller. Further, the system and method includes providing control by reducing the effects of hysteresis in solenoid actuated devices by providing a non-operational signal to the solenoid actuated device.

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: A method for optimizing an industrial process data is disclosed. The method includes collecting data from a plurality of sensor elements, wherein each sensor element collects data from a portion of the industrial process and verifying the data collected. The method further includes analyzing the data collected for efficiency and generating at least one recommendation for optimizing the industrial process. The method further includes presenting the at least one recommendation generated to an administrator of the industrial process.

Abstract: A control system for a plant is provided. This control system can control the plant more stably, when the model parameters of the controlled object model which are obtained by modeling the plant, which is a controlled object, are identified and the sliding mode control is performed using the identified model parameters. The model parameter identifier (22) calculates a model parameter vector (?) by adding an updating vector (d?) to a reference vector (?base) of the model parameter. The updating vector (d?) is corrected by multiplying a past value of at least one element of the updating vector by a predetermined value which is greater than “0” and less than “1”. The model parameter vector (?) is calculated by adding the corrected updating vector (d?) to the reference vector (?base).

Abstract: A method and system are disclosed for controlling a process by establishing a control factor for a proportional-integral-derivative (PID) controller used to control a parameter of a process relative to a setpoint. A feedback signal regarding the parameter of the process is received via a sensor of the process and a first feedback loop. Automatic adjusting of the control factor of the PID controller is based on the feedback signal.

Abstract: A method and apparatus is disclosed for acquiring and processing parameters used to adjust and tune a controller used, for example, to govern and compensate for motion, including vibrations and disturbances, in a physical system, such as a piece of manufacturing equipment. The method and apparatus may also be used to control, for example, a robot or other spatially dependent machine. The method and apparatus may comprise systems and methods for generating a controller, and for controlling motion in a physical system or apparatus.

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: The invention provides a control apparatus comprising a controller for determining a manipulated variable for manipulating a controlled object so that an output of the controlled object converges to a desired value and a modulator for modulating the manipulated variable by using one of a delta-sigma modulation algorithm, a sigma-delta modulation algorithm and a delta modulation algorithm to generate a modulated signal to be applied to the controlled object. The modulator generates the modulated signal so that a center value of an amplitude of the modulated signal follows a change of the manipulated variable. Thus, the modulated signal in which the manipulated variable is reflected without any loss can be generated even when the manipulated variable changes.

Abstract: A method for controlling a controlled process in response to an input signal and a disturbance signal includes modeling the controlled process in a process model; controlling the process model by a first controller; isolating the first controller from the disturbance signal so that the first controller may be designed for an optimal response to the input signal; driving the first controller by a first drive signal proportional to the difference between the input signal and a process model output signal; isolating a second controller from the input signal so that the second controller may be designed for an optimal response to the disturbance signal; and driving the second controller by a second drive signal proportional to difference between a process output signal and the process model output signal.

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: 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: A state based adaptive PID controller includes a model set component including a plurality of process models, each process model including a plurality of parameters. An error generator generates a model error signal representative of a difference between a model output signal and a process output signal. A model evaluation component computes a model squared error based on the model error signal. A parameter interpolator calculates an adaptive parameter value based on the model squared error. A controller update component updates adaptive controller parameter values based on adaptive parameter values.

Abstract: The response process of disturbance recovery control is divided into a follow-up phase, a convergence phase, and a stable phase. A feedback control device includes a first phase switching unit (3) which switches to the follow-up phase, a second phase switching unit (4) which switches to the convergence phase, a third phase switching unit (5) which switches to the stable phase, a first manipulated variable determining unit (6) which outputs a manipulated variable which makes the controlled variable follow up the set point in the follow-up phase, a second manipulated variable determining unit (7) which outputs a manipulated variable which makes the controlled variable converge near the set point in the convergence phase, and a third manipulated variable determining unit (8) which outputs a manipulated variable which makes the controlled variable stable at the set point in the stable phase.

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: A method and apparatus for operating a production system that includes a plurality of production facilities is provided. The method includes receiving, in real-time, for each facility, cost data for a first resource used by each respective facility to produce an output, receiving, in real-time, for each facility, cost data for a second resource used by each respective facility to produce the output, determining, in real-time; an automated incremental cost curve for the system based on a level of production of each facility and the received resource cost data, and determining a production output target for each production facility to achieve an optimum production system output based on the real-time incremental cost curves. The system includes at least one production facility that includes a software code segment programmed to determine, in real-time, an incremental cost of a first resource based on a level of production of each respective facility.

Abstract: A control unit has at least one control element, in particular with at least one integrating and/or differentiating transfer characteristic which is constructed as a temporally discrete dynamic fuzzy logic control element. A temporally discrete dynamic fuzzy logic control element of this kind is for example a so-called fuzzy logic automaton, which has processing states. It is advantageous that the control unit of the invention can be constructed with control elements that each have systematically dynamic fuzzy logic properties, and in which nonlinearities can be introduced in a targeted way for a desired control performance.

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: A control system for a plant is provided. This control system can control the plant more stably, when the model parameters of the controlled object model which are obtained by modeling the plant, which is a controlled object, are identified and the sliding mode control is performed using the identified model parameters. The model parameter identifier (22) calculates a model parameter vector (?) by adding an updating vector (d ?) to a reference vector (? base) of the model parameter. The updating vector (d ?) is corrected by multiplying a past value of at least one element of the updating vector by a predetermined value which is greater than “0” and less than “1”. The model parameter vector (?) is calculated by adding the corrected updating vector (d ?) to the reference vector (? base).

Abstract: A control system for a plant is disclosed. According to this system, a model parameter vector of a controlled object model which is obtained by modeling the plant, is identified. A controller controls the plant using the identified model parameter vector. An identifying error of the model parameter vector is calculated, and the calculated identifying error is limited in a predetermined range. An updating vector is calculated according to the limited identifying error. The model parameter vector is calculated by adding the updating vector to a reference vector of the model parameter vector.

Abstract: A method and apparatus for controlling a non-linear mill. A linear controller is provided having a linear gain k that is operable to receive inputs representing measured variables of the plant and predict on an output of the linear controller predicted control values for manipulatible variables that control the plant. A non-linear model of the plant is provided for storing a representation of the plant over a trained region of the operating input space and having a steady-state gain K associated therewith. The gain k of the linear model is adjusted with the gain K of the non-linear model in accordance with a predetermined relationship as the measured variables change the operating region of the input space at which the linear controller is predicting the values for the manipulatible variables. The predicted manipulatible variables are then output after the step of adjusting the gain k.

Abstract: Quick real-time identification and estimation of a time-non-varying or time-varying system. A new H? evaluation criterion is determined, a fast algorithm for a modified H? filter based on the criterion is developed, and a quick time-varying system identifying method according to the fast H? filtering algorithm is provided. By the fast H? filtering algorithm, a time-varying system sharply varying can be traced with an amount of calculation O(N) per unit time step. The algorithm completely agrees with a fast Kalman filtering algorithm at the extreme of the upper limit value. If the estimate of impulse response is determined, a pseudo-echo is sequentially determined from the estimate and subtracted from the real echo to cancel the echo. Thus an echo chancellor is realized.