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 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: An advanced control block that implements multiple-input/multiple-output control, such as model predictive control, within a process control system uses a compensation block or algorithm and a single control model based on a single process model to provide advanced control in a process having widely variable process delay. The compensation block changes the execution period of the advanced control block to account for changes in the one or more process variables responsible for the variable process delay, which eliminates the need to provide different advanced control models or control definitions for different operating regions for a process in the cases in which the delay in a process output is correlated to a measurable process or control variable.

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 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: 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: An adaptive predictive model includes a standard predictive model, such as a neural network or a natural model, constructed to produce an output that predicts a process parameter and a combiner network that combines the output of the predictive model with one or more measured values of the process parameter to produce an adjusted predicted process parameter during operation of a process. The adaptive predictive model reduces or corrects for non-linear as well as linear errors in the prediction of a process variable without having to reform the predictive model itself, while requiring only minor increases in processing power and time.

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 model predictive controller having a set of inputs adapted to receive process output signals and further having a set of outputs adapted to supply process input signals to process control devices is configured to perform integrated optimal model predictive control. The controller provides integrated optimal control using a feedback path that couples a selected output to an input having a setpoint that is equal to the constraint limit of the selected output. The controller maintains the selected output at its constraint limit until one of the outputs, other than the selected output, is going to reach or exceed the associated constraint limit for that output. When the output is going to reach or exceed the associated constraint limit for that output, the controller relaxes the setpoint until the output is no longer going to reach or exceed the associated constraint limit for that output.

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 diagnostic tool automatically collects and stores data indicative of a variability parameter, a mode parameter, a status parameter and a limit parameter for a multi-variable function block associated with one or more devices or loops within a process control system, processes the collected data to determine which devices, loops or function blocks have problems that result in reduced performance of the process control system, displays a list of detected problems to an operator and then suggests the use of other, more specific diagnostic tools to further pinpoint or correct the problems. When the diagnostic tool recommends and executes a data intensive application as the further diagnostic tool, it automatically configures a controller of the process control network to collect the data needed for such a tool.

Abstract: An advanced control block that implements multiple-input/multiple-output control, such as model predictive control, within a process control system uses a compensation block or algorithm and a single control model based on a single process model to provide advanced control in a process having widely variable process delay. The compensation block changes the execution period of the advanced control block to account for changes in the one or more process variables responsible for the variable process delay, which eliminates the need to provide different advanced control models or control definitions for different operating regions for a process in the cases in which the delay in a process output is correlated to a measurable process or control variable.

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 diagnostic tool automatically collects and stores data indicative of a variability parameter, a mode parameter, a status parameter and a limit parameter associated with each of the different devices, loops or function blocks within a process control system, processes the collected data to determine which devices, loops or function blocks have problems that result in reduced performance of the process control system, displays a list of detected problems to an operator and then suggests the use of other, more specific diagnostic tools to further pinpoint or correct the problems. When the diagnostic tool recommends and executes a data intensive application as the further diagnostic tool, it automatically configures a controller of the process control network to collect the data needed for such a tool.

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 simulation procedure simulates the response of a process control loop having a controller tuned according to a set of tuning parameters to illustrate, for example, the overshoot, oscillation, response time, etc. of the controller as tuned. As part of the simulation procedure, a robustness map, such as a plot illustrating phase margin versus gain margin or other robustness qualities, is created and the robustness of the simulated control loop may be plotted as a point on the robustness map. During the creation of the robustness map, different sets of tuning parameters for a region in which the process control loop is stable are determined and stored and the corresponding robustness qualities of the loop having a controller tuned according to these tuning parameters are also determined and stored. The robustness map is then displayed to a user showing the stable region.

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.

Abstract: An auto-tuner for use in tuning a control element in a process control network having distributed control functions includes a first tuning element located in the field device in which the control element is operating and a second tuning element located in a different device that communicates with the first device via a communication network. The first tuning element controls the operation of the control element during the dynamic data capture phase of an auto-tuning procedure, collects data during this phase of the auto-tuning procedure and determines one or more process characteristics from the collected data. The first tuning element then communicates the determined process characteristics to the second tuning element via the communication network.

Abstract: A diagnostic tool automatically collects and stores data indicative of a variability parameter, a mode parameter, a status parameter and a limit parameter associated with each of the different devices, loops or function blocks within a process control system, processes the collected data to determine which devices, loops or function blocks have problems that result in reduced performance of the process control system, displays a list of detected problems to an operator and then suggests the use of other, more specific diagnostic tools to further pinpoint or correct the problems. When the diagnostic tool recommends and executes a data intensive application as the further diagnostic tool, it automatically configures a controller of the process control network to collect the data needed for such a tool.