Abstract: Example methods, apparatus and articles of manufacture to test process control systems are disclosed. A disclosed example method includes obtaining user inputs to a first process control system, obtaining process inputs and first process outputs of the first process control system, providing the user inputs and the process inputs to a second process control system to operate the second process control system, obtaining second process outputs of the second process control system operated with the user inputs and the process inputs, and comparing the first and second process outputs to determine whether the second process control system is implemented as intended.

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: A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained.

Abstract: An MPC controller technique integrates feedback control performance better than methods commonly used today in MPC type controllers, resulting in an MPC controller that performs better than traditional MPC techniques in the presence of process model mismatch. In particular, MPC controller performance is enhanced by adding a tunable integration block to the MPC controller that develops an integral component indicative of the prediction or other control error, and adds this component to the output of an MPC controller algorithm to provide for faster or better control in the presence of model mismatch, which is the ultimate reason for the prediction error in the first place. This technique enables the MPC controller to react more quickly and to provide better set point change and load disturbance performance in the presence of model mismatch, without decreasing the robustness of the MPC controller.

Abstract: An MPC adaptation and tuning technique integrates feedback control performance better than methods commonly used today in MPC type controllers, resulting in an MPC adaptation/tuning technique that performs better than traditional MPC techniques in the presence of process model mismatch. The MPC controller performance is enhanced by adding a controller adaptation/tuning unit to an MPC controller, which adaptation/tuning unit implements an optimization routine to determine the best or most optimal set of controller design and/or tuning parameters to use within the MPC controller during on-line process control in the presence of a specific amount of model mismatch or a range of model mismatch.

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: 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: An interface or display routine is provided for use in viewing and configuring a function block that performs integrated optimization and control within a process control system. The interface routine may enable a user to view or configure variables, values or other parameters associated with the integrated optimization and control block within the process control system. For example, the interface routine may display the current operating state of the integrated function block, may enable a user to select inputs and output of the function block for use in providing integrated optimization and control, may enable a user to select a particular or desired optimization function for use in the function block, etc. The interface routine may also display the multiple input output curves associated with the optimizer and the controller sections of the integrated function block in a manner that provides ease of view and selection of these curves as part of the algorithm used by the integrated function block.

Abstract: A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained.

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: 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: 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 for tuning a process control loop includes a tuner module for receiving an error signal representative of the difference between a set point and a process variable, the module generating a first process control signal for controlling the process. The system further includes a controller module for receiving the error signal and a parameter signal from a nonlinear module to generate a second process control signal for controlling the process, wherein the nonlinear module applies a nonlinear procedure to generate the parameter signal. The system further includes a switching means coupled to the tuner module and the controller module to select the appropriate process control signal for controlling the process. The system provided uses nonlinear techniques in the nonlinear module to approximate the desired controller tuning parameters. The nonlinear techniques include neural network tuning, fuzzy logic tuning and nonlinear functions, including sigmoid tuning.

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: An interface or display routine is provided for use in viewing and configuring a function block that performs integrated optimization and control within a process control system. The interface routine may enable a user to view or configure variables, values or other parameters associated with the integrated optimization and control block within the process control system. For example, the interface routine may display the current operating state of the integrated function block, may enable a user to select inputs and output of the function block for use in providing integrated optimization and control, may enable a user to select a particular or desired optimization function for use in the function block, etc. The interface routine may also display the multiple input output curves associated with the optimizer and the controller sections of the integrated function block in a manner that provides ease of view and selection of these curves as part of the algorithm used by the integrated function block.

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: An advanced control block that implements multiple-input/multiple-output control, such as model predictive control, within a process control system is initiated by creating an initial control block having generic control logic and desired control inputs and control outputs communicatively connected to process outputs and process inputs within a process control routine. A waveform generator within the control block systematically upsets each of the process inputs via the control block outputs using excitation waveforms designed for use in developing a process model. At the same time, a data collection routine collects data indicating the response of each of the process outputs to the waveforms delivered at each of the process inputs. After sufficient data has been collected, a process modeling routine generates a process model from the collected data and a control logic parameter creation routine creates control logic parameters for the control logic from the process model.