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: In the present invention, a Smith predictor enhanced PID controller, SP-PID, is proposed. A tuning parameter Ksp is devised and the SP-PID controller would be gradually transformed from a PID controller to a Smith predictor as Ksp changes from 0 to 1. Properties of the SP-PID are explored and the design procedure is given to ensure a certain degree of robustness. Simulation results clearly indicate that the SP-PID takes advantage of the SP when small modeling error is encountered and it is gradually detuned to a PID controller, a user-friendly controller, when the model quality degrades. Moreover, the controller and its design procedure can be implemented in current process control computers with virtually no extra hardware cost.

Abstract: A computer system for controlling a nonlinear physical process. The computer system comprises a linear controller and a neural network. The linear controller receives a command signal for control of the nonlinear physical process and a measured output signal from the output of the nonlinear physical process. The linear controller generates a control signal based on the command signal, a measured output signal, and a fixed linear model for the process. The neural network receives the control signal from the linear controller and the measured output signal from the output of the nonlinear physical process. The neural network uses the measured output signal to modify the connection weights of the neural network. The neural network also generates a modified control signal supplied to the linear controller to iterate a fixed point solution for the modified control signal used to control the nonlinear physical process.

Abstract: A PID control for a vapor compression system utilized to heat water identifies a particular range of error signals and derivative of the error signals that could be indicative of the cycle moving in an inefficient direction. If this determination is made, then a substitute for the error signal is utilized. In particular, the determination is made if both the error and the derivative of the error are negative. The substitute multiplies the error with its derivative to result in a positive product. This ensures the system will not move in the inefficient direction.

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 producing a control output is described. The method includes identifying an error signal and decomposing the error signal into a plurality of signal components. The signal components are determined based on a plurality of orthogonal functions representing multi-resolution decomposition properties. The method further includes transforming each signal component. The transformed signal components are summed to determine a control signal.

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: A method of regulating a target system generates a plurality of digital signals which define a reference pulse train with a frequency dependent upon a reference signal. A target system to be regulated (such as a transport speed of a paper transport assembly in a printer) has an output in the form of a plurality of digital signals defining a feedback pulse train having a frequency. The frequency of the reference pulse train is compared with the frequency of the feedback pulse train. A control signal is generated dependent upon the frequency comparison, and is provided as an input to the target system.

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: In tuning a controller for a process in a feedback control system, a method is provided for bringing the system into asymmetric self-excited oscillations for measuring the frequency of the oscillations, average over the period value of the process output signal and average over the period control signal and tuning the controller in dependence of the measurements obtained. An element having a non-linear characteristic is introduced into the system in series with the process and set point signal is applied to excite asymmetric self-excited oscillations in the system. An algorithm and formulas are given for identification of the process model having the form of first order plus dead time transfer function. PI controller settings are given as a function of the dead time/time constant ratio. An apparatus for performing the method is disclosed.

Abstract: A method and apparatus for automatically adjusting the gains of a feedback controller while the process continues to run and the controller continues to operate and control the process is disclosed. A desired closed-loop control bandwidth and a target loop transfer function are specified by the operator, and the tuning is accomplished automatically with minimal operator intervention and without the need for developing a model of the process. The automatic tuner subjects the process to one or more disturbance and the operation of both the process and the controller are monitored.

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: 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: An adaptive process controller drives a process variable to be substantially equivalent to a set point and adapts the controller gain, the controller reset, and/or the controller rate, based on model free adaptation. The adaptive controller combines a controller gain computed from an oscillation index with a controller gain computed from a steady state estimate and that adapts the controller reset/rate by forcing the ratio of two of the controller proportional, integral or derivative terms to be equal to a predetermined value.

Abstract: A system and method 100 of using residual feedback in a control loop in a manner that substantially eliminates the steady state error in the predicted states that results from the mismatch in gain between the plant and the model. The control effort used by the estimator to predict the next position is modified to compensate for this difference in gain. By integrating the residual, and modifying the apparent control effort accordingly, the residual is driven to have a mean value of zero. When the residual goes to zero, by definition, the steady state error in the position state goes to zero; and to the extent that the model matches the plant, the velocity state also approaches zero such that the steady state error in the predicted states are substantially eliminated, allowing for improved control.

Abstract: The present invention includes a system and method for fine tuning the control of a manufacturing process. A material adjusting device is in communication with a PID controller and PID control loop, and is used to alter a flow of material used in the manufacturing process, so as to maintain a target physical property of the material at a setpoint. A measurement device captures measurements of the flow relevant to the physical property of interest. A change is introduced to the material adjusting device while the PID controller and PID control loop are disabled, and appropriate measurements of the flow are continually captured; a process that may be repeated several times. Once sufficient physical property measurement data has been captured, the data is loaded into an optimization program that outputs optimized controlled parameters that may be used by the PID controller and control loop to better control the physical property of the material.

Abstract: A method for tuning a PID controller includes the steps of inducing equivalent relationships between PID gains of the PID controller and parameters of time delay control (TDC), selecting a natural frequency vector and a damping ratio vector so as to acquire a desired error dynamics of the closed PID control loop system, selecting a sampling time of the closed PID control loop system, determining the parameters of TDC on the basis of the natural frequency vector, the damping ratio vector and a closed loop stability condition for TDC, and selecting PID gains of the PID controller on the basis of the equivalent relationships.

Abstract: A preferential upper limit processing section L_MH1 limits the value of a pre-input manipulated variable output upper limit MH1 so as to adjust a manipulated variable output MV1 of a preferential controller PID1 to a predetermined value MT1 or less. A non-preferential upper limit calculating section C_MH2 calculates a manipulated variable output upper limit MH2 of a non-preferential controller PID2 so as to adjust the sum of manipulated variable outputs MV1 and MV2 to the predetermined value MT1 or less. A preferential upper limit calculating section C_MH1 performs calculation of increasing/decreasing the manipulated variable output upper limit MH1 of the controller PID1 in accordance with the margin of the manipulated variable output MV2 of the controller PID2 with respect to the manipulated variable output upper limit value MH2, and sets the calculated value as a manipulated variable output upper limit value MH1? in the next control cycle.

Abstract: According to a feedback control method, the response process of set point tracking control is divided into three, tracking, convergence, and stabilization phases. The phase is switched to the tracking phase at set point change start time as the tracking phase start time. The manipulated variable which causes the controlled variable to tracking the set point is continuously output in the tracking phase. The phase is switched to the convergence phase at, as the convergence phase start time, specific set point tracking control elapsed time at which the controlled variable does not exceed the set point in the tracking phase. A manipulated variable which converges the controlled variable to the vicinity of the set point is continuously output in the convergence phase. The phase is switched to the stabilization phase at, as the stabilization phase start time, time at which the controlled variable reaches a preset situation in the convergence phase.

Abstract: This invention describes a reconfigured form of the PID compensator such that the rate of change of the position error is inherently limited without affecting the performance of the servo loop when the position error is small. The technique described here maintains the performance of the conventional PID compensator when the position error is close to zero, which is the operating point of primary interest.

Abstract: An apparatus is provided for controlling a system to achieve a specified response. In one embodiment, the apparatus is a proportional, integrative, and derivative (PID) controller having a proportional element, an integrative element, and a derivative element coupled together. The elements respond to a reference signal and generate a control signal that causes a plant to generate a plant output. The proportional element has a gain element where the gain is a function of the ultimate gain of the plant (Ku) and the ultimate period of the plant (Tu). The controllers may also be embodied in non-PID controllers that share common elements, such as the use of: (a) Astrom-Hagglund controller output as an input for a subsequent controller; (b) internal feedback; and (c) a subsequent controller that performs a subtraction operation to generate the difference between the output of the Astrom-Hagglund controller and the output of the subsequent controller.

Abstract: A method is provided, the method comprising measuring at least one parameter characteristic of rapid thermal processing performed on a workpiece in a rapid thermal processing step, and modeling the at least one characteristic parameter measured using a first-principles radiation model. The method also comprises applying the first-principles radiation model to modify the rapid thermal processing performed in the rapid thermal processing step.

Abstract: In a rotor assembly having a rotor supported for rotation by magnetic bearings, a processor controlled by software or firmware controls the generation of force vectors that position the rotor relative to its bearings in a “bounce” mode in which the rotor axis is displaced from the principal axis defined between the bearings and a “tilt” mode in which the rotor axis is tilted or inclined relative to the principal axis. Waveform driven perturbations are introduced to generate force vectors that excite the rotor in either the “bounce” or “tilt” modes.

Abstract: A system and method for controlling a critical process variable, such as the temperature of one or more temperature control units for cluster tools in a semiconductor fabrication facility, uses dual interrelated PID) algorithms for interrelated but at times separate control of heating capabilities. The temperature control units operate with high power efficiency, because no heating energy is expended during cooling and non-transition modes. When approaching a temperature threshold, however, the heating algorithm is reinstated just long enough to provide minimum undershoot and enabling precise, low per consuming, steady state control at ±0.1° C., minimizing undershoot and enabling precise steady state control at ±0.1° C.

Abstract: An adaptive control system and method for controlling the path feed rate to achieve a target spindle load during machine tool operations. The adaptive control system can provide load monitoring capability, which will actively monitor an incoming load signal from a spindle drive and determine if it exceeds warning and alarm levels. If the incoming load being monitored exceeds the set warning level, a warning output is asserted and maintained until the incoming load falls below the set warning level. If the incoming load exceeds the set alarm level, the alarm output and feed hold output are asserted and maintained until an adaptive controller reset is requested. Normal cutting operations cannot resume until the conditions that generated the alarm level are addressed. Adaptive control of machine tool operations is provided by monitoring of the incoming load and requesting feed rate changes based on a proportional integral derivative (PID) controller algorithm.

Abstract: A method for tuning a PID controller includes the steps of inducing equivalent relationships between PID gains of the PID controller and parameters of time delay control (TDC), selecting a natural frequency vector and a damping ratio vector so as to acquire a desired error dynamics of the closed PID control loop system, selecting a sampling time of the closed PID control loop system, determining the parameters of TDC on the basis of the natural frequency vector, the damping ratio vector and a closed loop stability condition for TDC, and selecting PID gains of the PID controller on the basis of the equivalent relationships.

Abstract: A control arithmetic device includes a subtracting section, disturbance application detecting section, error correction amount calculating section, error correction amount convergence calculating section, and control arithmetic section. The subtracting section calculates the error of a controlled variable on the basis of a controlled variable and set point for a controlled system. The disturbance application detecting section detects, in control cycles, on the basis of the error output whether a disturbance is applied. The error correction amount calculating section calculates an error correction amount on the basis of the magnitude of the error when application of a disturbance is detected. The error correction amount convergence calculating section performs a convergence operation. A control arithmetic method is also presented.

Abstract: A predictive and self-tuning PI controller includes an apparatus for variably assigning gains thereto in accordance with specified time functions following a change in set-point. The control gains are computed in accordance with a plurality of input parameters and subsequently continuously adjusted to set-point errors based on a GPC approach thereby to optimize performance indices derived from simple and classical user specifications. Variation and tuning of control gains in accordance with set-point errors permits the PI controller to be useful in the general area of process control and applicable to a wider range of processes compared to traditional PI control, including time-delay processes, unstable processes and processes with time-varying dynamics.

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: A controller (1) has a fuzzy calculation means (64) for performing a fuzzy calculation in order to calculate a PID value (P, I, D) based on a switch time T, a PID value (P1, I1, D1) before switching of the PID value, and a PID value (P2, I2, D2) after the switching. The switch time T is obtained by manual through the switch time input means (7) or by calculating it automatically. The PID value (P1, I1, D1) is stored in a PID value storage means (62), and the PID value (P2, I2, D2) is stored in a PID value storage means (63).

Abstract: The subject of the invention is a process for adjusting a processing unit, whereby at least one set value is varied from a starting value to a final value. In this case, the target value of a correcting value is set based on the starting value and the final value of the adjustment value, and the value of the correcting value is run up from its starting value to its target value using a parameter-dependent transfer function. A control variable is measured, the value of the control variable is compared to a comparison value, and in the case of deviations of the value of the control variable from the comparison value, at least one parameter of the transfer function is corrected.

Abstract: A process control system in a multi-input/output coordinate control system for executing coordinate control of a main input. The process control system sets an operating point set value for an operating point of an n-th control output, generates the n-th control output to control the main input based on the n-th control output, and generates an m-th control output to control the operating point of the n-th control output so that the operating point of the n-th control output becomes equal to the operating point set value, thereby to allow the n-th control output of fast response to operate at the operating point set value by controlling the main input.

Abstract: A system using backside helium in the processing of wafers in the manufacturing of semiconductor products can cause alarms that can interrupt the manufacturing of the wafers and create damaged wafers. A PID controller responds to the flow of helium gas to generate control signals for operating several algorithms. A filter connected to the output of the PID controller removes unwanted and dangerous noise spikes that have in the past caused a condition called backside alarm. Noise spikes on the dc voltages, the helium supply pressure, the pressure set point, the pressure reading and the non-optimization of the PID controller can cause the alarm.

Abstract: A control system using a genetic analyzer based on discrete constraints is described. In one embodiment, a genetic algorithm with step-coded chromosomes is used to develop a teaching signal that provides good control qualities for a controller with discrete constraints, such as, for example, a step-constrained controller. In one embodiment, the control system uses a fitness (performance) function that is based on the physical laws of minimum entropy. In one embodiment, the genetic analyzer is used in an off-line mode to develop a teaching signal for a fuzzy logic classifier system that develops a knowledge base. The teaching signal can be approximated online by a fuzzy controller that operates using knowledge from the knowledge base. The control system can be used to control complex plants described by nonlinear, unstable, dissipative models. In one embodiment, the step-constrained control system is configured to control stepping motors.

Abstract: A method for controlling a solenoid using a single coil solenoid for both course and fine adjustment. A resistor or series of resistors may be used in parallel/for current controlled solenoids or in series for voltage controlled solenoids for allowing course or finite control of the solenoids.

Abstract: Controllers (PID1 to PIDn) perform control so that the controlled variables of first to n-th control loops may agree with their preset values. A step response progress calculating section (C_R1) calculates the progress &agr;1 of the step response of the first control loop where the variation of the controlled variable is the slowest. Control loop internal preset value calculating sections (C_S2 to C_Sn) correct the preset values of the second to n-th control loops according to the progress &agr;1 of the step response so that the controlled variables of the second to n-th control loops vary synchronously with the controlled variable of the first control loop and give the corrected preset values to the controllers (PID2 to PIDn).

Abstract: A Robust Model-Free Adaptive controller is disclosed for effectively controlling simple to complex systems including industrial processes, equipment, facilities, devices, engines, robots, vehicles, and appliances. Without the need of re-designing a controller or re-tuning the controller parameters, the inventive controller is able to provide a wide robust range and keep the system under automatic control during normal and extreme operating conditions when there are significant disturbances or changes in system dynamics. Because of its simplicity and capability, the control system is useful for building flexible and adaptive production systems to fulfill the on demand manufacturing needs of the new e-commerce environment.

Abstract: The present invention includes a system and method for fine tuning the process of controlling the weight of a flow of material, such as a rod of material in a production facility. A control actuator in communication with a PID controller and PID control loop is used to change the unit volume of the flow so as to maintain a target unit weight. A measurement device captures unit weight measurements of the flow at intervals over the length of the flow. A change is introduced to a control actuator while the PID controller and PID control loop are disabled, and appropriate weight measurements of the flow are continually captured along with the control actuator's “feed back” position; a process that may be repeated several times. Once sufficient weight measurement data has been captured, the data is loaded into an optimization program that outputs optimized controlled parameters that may be used by the PID controller and control loop to better control the weight of the flow of material.

Abstract: The controller 70 of the brine supply device 10 performs the PID calculation of the manipulated variable MV of the valve 14, calculates the compensated manipulated variable MV′ by compensating the manipulated value MV, and controls the operation of the valve 14 based on the compensated manipulated variable MV′. The variation &Dgr;MV of the manipulated variable MV becomes proportional to the variation &Dgr;PV of the brine supply temperature Pt1 as the operation of the valve 14 is controlled with the compensated manipulated variable MV′. This makes it possible to control temperature with a high accuracy using only one set of PID constants.

Abstract: A method of adjusting a MEMS mirror control system is provided to calibrate a MEMS mirror control system to a particular MEMS mirror in a fashion that optimizes MEMS mirror control loop performance. This calibration is implemented by measuring the gain and resonant frequency of the particular MEMS mirror, and adjusting one or more of the parameters used in the implementation of a PID controller, a state estimator, and a feed forward component used to perform seeks.

Abstract: A computer based method of controlling an industrial process, including the steps of measuring the values of at least one process variable (y1, y2), and predicting future deviations of one process variable (y1) with regard to the measured value of said at least one process variable (y1, y2), and providing a control signal (uFB) based on said predictions, and by means of a first control law. The method comprises the further steps of measuring a measurable disturbance (d) in the process, predicting future deviations of said process variable (y1) with regard to said disturbance but without regard to the measured value of said at least one process variable (y1, y2), and providing a control signal (ud) based on said predictions by means of a second control law.

Abstract: The present invention provides a method and apparatus for adaptive control which models marginally stable systems by creating a reference or disturbance model to account for the slope of the process variable being constant for different control inputs to the process.

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 disk apparatus includes a DSP (digital signal processor), and a DSP core of the DSP receives a feedback input from a pick-up, and generates a focus control pulse and a tracking control pulse. That is, a focus servo loop and a tracking servo loop are formed. The DSP core detects a phase difference between a sine wave of a target frequency and a sine wave superposed on an input signal, and adjusts a gain of the servo loop so as to bring a DC component of a correlation signal correlated with the phase difference to a 0 level. More specifically, if the DC component of the correlation signal exceeds a predetermined threshold value, an adjusting amount of the gain is rendered large, and greatly brought closer to the 0 level. On the other hand, if the DC component of the correlation signal is equal to or less than the predetermined threshold value, the adjusting amount of the gain is rendered small, and gradually brought to the 0 level.

Abstract: A controller (1) has a fuzzy calculation means (64) for performing a fuzzy calculation in order to calculate a PID value (P, I, D) based on a switch time T, a PID value (P1, I1, D1) before switching of the PID value, and a PID value (P2, I2, D2) after the switching. The switch time T is obtained by manual through the switch time input means (7) or by calculating it automatically. The PID value (P1, I1, D1) is stored in a PID value storage means (62), and the PID value (P2, I2, D2) is stored in a PID value storage means (63).

Abstract: A Y stage is capable of translational driving in the Y-axial direction, by independently controllable Y1 linear motor and Y2 linear motor. The amount of movement of the Y stage is detected by a Y1 linear encoder and Y2 linear encoder, and fed back to a Y control system and &thgr; control system. The Y control system outputs a translational thrust command value, by receiving the average value of each positional detecting value measured by the Y1 linear encoder and Y2 linear encoder as the stage translational direction position feedback value. The &thgr; control system receives the difference between each position detecting value as a stage yawing direction position feedback value, and outputs a yawing direction thrust command value. A non-interference block outputs Y1 linear motor thrust command value and Y2 linear motor thrust command value, by the use of the translational thrust command value and the thrust command value.

Abstract: A system and method for adaptively designing self-tuning controllers, specifically PID controllers for process control systems. The method is based on a model-parameter interpolation, according to which a candidate process model is defined by a predetermined, limited set of models. Each of the models is characterized by a plurality of parameters, and, for each model, each of the parameters has a respective value that is selected from a set of predetermined initialization values corresponding to the parameter. Evaluation of each of the models includes computation of a model squared error and computation of a Norm that is derived from the model square errors calculated for the models. The Norm value is assigned to every parameter value represented in the model that is represented in an evaluation scan. As repeated evaluations of models are conducted, an accumulated Norm is calculated for each parameter value.

Abstract: A controller controls a physical variable such as temperature of a controlled system. A variable calculator calculates and output a manipulated variable on the controlled system based on of a target value such as a target temperature and a feedback value such as the measured temperature of the controlled system. A dead-time compensator provides a dead-time compensated output using a model simulating the controlled system having a dead time and an idealized model having no dead time, based on the manipulated variable outputted from the variable calculator to carry out an ordinary control and a dead-time compensated control. A switch functions to select the dead-time compensated control by providing a dead-time compensated output to the variable calculator or an ordinary control providing no dead-time compensated output to the variable calculator. The dead-time compensated control is carried out at least during set point response of the controller.

Abstract: For controlling multivariable systems, a control unit for controlling a system with several coupled control variables. The control unit includes controllers (10, 11) having associated control variables (x1, x2) as well as a decoupling network. The decoupling network is connected upstream from the system and includes at least one first decoupling member (12). A first output variable (y1) of a first one of the controllers (10) is routed to the first decoupling member (12). The first decoupling member generates a first correcting quantity (14) for a second output variable (y2) of a second one of the controllers (11). The second controller (11) has a PI- or PID-controller core (40) and is configured such that integrator windup is eliminated when the second output variable (y2) corrected with the first correcting quantity (14) reaches a manipulated variable limit. Bumpless manual/automatic changeover of the controller is also made possible.

Abstract: The present invention includes a system and method for fine tuning the process of controlling the weight of a flow of material, such as a rod of material in a production facility. A control actuator in communication with a PID controller and PID control loop is used to change the unit volume of the flow so as to maintain a target unit weight. A measurement device captures unit weight measurements of the flow at intervals over the length of the flow. A change is introduced to a control actuator while the PID controller and PID control loop are disabled, and appropriate weight measurements of the flow are continually captured along with the control actuator's “feed back” position; a process that may be repeated several times. Once sufficient weight measurement data has been captured, the data is loaded into an optimization program that outputs optimized controlled parameters that may be used by the PID controller and control loop to better control the weight of the flow of material.