Abstract: A method includes obtaining a process model representing an industrial process and obtaining controller specifications for an industrial process controller. The method also includes identifying a controller design having one or more parameters for the industrial process controller using the process model, an uncertainty associated with the process model, and the one or more parameters. The method further includes validating the controller design of the industrial process controller for use in a closed-loop control system and deploying the controller design if validated to the industrial process controller.

Abstract: This disclosure provides adaptive control techniques for pH control or control of other industrial processes. For example, in one method, a robust stability condition (RSC) value is determined during operation of a process controller, and a characteristic of the process controller is adaptively modified based on the RSC value. The RSC value provides an estimate of performance of the process controller in controlling the industrial process. In another method, one of multiple process controllers is selected based on RSC values associated with the process controllers, and one or more control signals are output from the selected process controller to an industrial process in order to control the industrial process. The RSC values provide estimates of performances of the multiple process controllers in controlling the industrial process.

Abstract: This disclosure provides adaptive control techniques for pH control or control of other industrial processes. For example, in one method, a robust stability condition (RSC) value is determined during operation of a process controller, and a characteristic of the process controller is adaptively modified based on the RSC value. The RSC value provides an estimate of performance of the process controller in controlling the industrial process. In another method, one of multiple process controllers is selected based on RSC values associated with the process controllers, and one or more control signals are output from the selected process controller to an industrial process in order to control the industrial process. The RSC values provide estimates of performances of the multiple process controllers in controlling the industrial process.

Abstract: A method includes obtaining a process model representing an industrial process and obtaining controller specifications for an industrial process controller. The method also includes identifying a controller design having one or more parameters for the industrial process controller using the process model, an uncertainty associated with the process model, and the one or more parameters. The method further includes validating the controller design of the industrial process controller for use in a closed-loop control system and deploying the controller design if validated to the industrial process controller.

Abstract: A method includes receiving data associated with operation of an industrial process system. The method also includes identifying a model defining a behavior of the industrial process system using the data, an orthonormal bases function, and an ordinal spline bases function. The orthonormal bases function can be generated using estimated poles of the industrial process system. The ordinal spline bases function can be generated using a specified set of cubic splines. The ordinal spline bases function can also be generated using a distribution of knots and multiple ordinal spline functions associated with the knots. More knots can be associated with a more nonlinear portion of the industrial process system, and fewer knots can be associated with a less nonlinear portion or a linear portion of the industrial process system.

Abstract: A method of dynamic model selection for hybrid linear/non-linear process control includes developing a plurality of process models including at least one linear process model and at least one non-linear process model from inputs including dynamic process data from a processing system that runs a physical process. At least two of the plurality of process models are selected based on a performance comparison based on at least one metric, wherein the selected process models number less than a number of the plurality of process models received. A multi-model controller is generated that includes the selected process models. The physical process is simulated using the multi-model controller by applying the selected process models to obtain closed loop performance test data for each of the selected models. The performance test data is compared. A selected process model is then selected.

Abstract: A system includes a process controller and an equation evaluation apparatus. The equation evaluation apparatus includes an equation editor, a model factory, and an equation evaluation engine. The equation editor is adapted to receive equations describing a process to be controlled by the process controller. The equation editor is also adapted to generate model information representing the equations. The model factory is adapted to receive the model information and generate an equation stack representing the equations. The equation evaluation engine is adapted to receive evaluation information from the process controller, evaluate at least one of the equations using the evaluation information and the equation stack, and send a result of the evaluation to the process controller. The model information could include information representing algebraic equations, differential equations, algebraic states, differential states, inputs, parameters, constants, and/or expressions.

Abstract: A model predictive controller (MPC) for controlling physical processes includes a non-linear control section that includes a memory that stores a non-linear (NL) model that is coupled to a linearizer that provides at least one linearized model, and a linear control section that includes a memory that stores a linear model. A controller engine is coupled to receive both the linearized model and linear model. The MPC includes a switch that in one position causes the controller engine to operate in a linear mode utilizing the linear model to implement linear process control and in another position causes the controller engine to operate in a NL mode utilizing the linearized model to implement NL process control. The switch can be an automatic switch configured for automatically switching between linear process control and NL process control.

Abstract: A model predictive controller (MPC) for controlling physical processes includes a non-linear control section that includes a memory that stores a non-linear (NL) model that is coupled to a linearizer that provides at least one linearized model, and a linear control section that includes a memory that stores a linear model. A controller engine is coupled to receive both the linearized model and linear model. The MPC includes a switch that in one position causes the controller engine to operate in a linear mode utilizing the linear model to implement linear process control and in another position causes the controller engine to operate in a NL mode utilizing the linearized model to implement NL process control. The switch can be an automatic switch configured for automatically switching between linear process control and NL process control.

Abstract: A method of dynamic model selection for hybrid linear/non-linear process control includes developing a plurality of process models including at least one linear process model and at least one non-linear process model from inputs including dynamic process data from a processing system that runs a physical process. At least two of the plurality of process models are selected based on a performance comparison based on at least one metric, wherein the selected process models number less than a number of the plurality of process models received. A multi-model controller is generated that includes the selected process models. The physical process is simulated using the multi-model controller by applying the selected process models to obtain closed loop performance test data for each of the selected models. The performance test data is compared. A selected process model is then selected.

Abstract: A method includes receiving data associated with operation of an industrial process system. The method also includes identifying a model defining a behavior of the industrial process system using the data, an orthonormal bases function, and an ordinal spline bases function. The orthonormal bases function can be generated using estimated poles of the industrial process system. The ordinal spline bases function can be generated using a specified set of cubic splines. The ordinal spline bases function can also be generated using a distribution of knots and multiple ordinal spline functions associated with the knots. More knots can be associated with a more nonlinear portion of the industrial process system, and fewer knots can be associated with a less nonlinear portion or a linear portion of the industrial process system.

Abstract: A method includes obtaining a nonlinear model that represents a pH of a material in a process to be controlled. The model is generated using an orthonormal bases function and an ordinal spline bases function. The method also includes performing non-linear model predictive control of the process using the model. The method could also include generating the model using the orthonormal bases function and the ordinal spline bases function. This could include identifying a distribution of knots and multiple ordinal spline functions, where each ordinal spline function is associated with one of the knots. The ordinal spline bases function can be generated using at least one of the ordinal spline functions.

Abstract: One method includes obtaining a preliminary model associated with a system to be controlled and constructing a weighted model using one or more weighting factors. The method also includes identifying a final model of the system using the preliminary and weighted models, where the final model has a stability margin that is greater than an uncertainty associated with the final model. The method further includes controlling the system using a controller designed based on the final model. Another method includes identifying a first model associated with a system to be controlled, performing model order reduction to identify a second model, and controlling the system using a controller designed based on the second model. Performing the model order reduction includes reducing a weighted coprime factor model uncertainty between the first and second models.

Abstract: An apparatus, method, and computer program are provided for automated closed-loop identification of an industrial process in a process control system. Multiple models (such as multiple model structure-model order combinations) can be identified, where the models are associated with a process to be controlled. One or more metrics (such as a prediction metric or rank) can be determined for each of the models. At least one of the models can be selected based on the one or more metrics. A final model for controlling the process can be provided (such as to a controller), where the final model is based on the at least one selected model. A band pass filter could be designed using some of the identified models. The band pass filter could be used to identify at least one other of the models or to determine at least one of the one or more metrics.

Abstract: A method includes obtaining at least one measurement of one or more controlled variables associated with an industrial process. The method also includes obtaining a linearized approximation of a process model representing the industrial process. The method further includes estimating a state of the industrial process using the at least one measurement, the linearized approximation, and a window-based state estimator. The method also includes generating at least one control signal for adjusting one or more manipulated variables associated with the industrial process. Generating the at least one control signal includes using the estimated state and model predictive control (MPC) logic. In addition, the method includes outputting the at least one control signal.

Abstract: An apparatus, method, and computer program are provided for automated closed-loop identification of an industrial process in a process control system. Multiple models (such as multiple model structure-model order combinations) can be identified, where the models are associated with a process to be controlled. One or more metrics (such as a prediction metric or rank) can be determined for each of the models. At least one of the models can be selected based on the one or more metrics. A final model for controlling the process can be provided (such as to a controller), where the final model is based on the at least one selected model. A band pass filter could be designed using some of the identified models. The band pass filter could be used to identify at least one other of the models or to determine at least one of the one or more metrics.

Abstract: An apparatus, method, and computer program are provided for automated closed-loop identification of an industrial process in a process control system. Multiple models (such as multiple model structure-model order combinations) can be identified, where the models are associated with a process to be controlled. One or more metrics (such as a prediction metric or rank) can be determined for each of the models. At least one of the models can be selected based on the one or more metrics. A final model for controlling the process can be provided (such as to a controller), where the final model is based on the at least one selected model. A band pass filter could be designed using some of the identified models. The band pass filter could be used to identify at least one other of the models or to determine at least one of the one or more metrics.

Abstract: A system includes a process controller and an equation evaluation apparatus. The equation evaluation apparatus includes an equation editor, a model factory, and an equation evaluation engine. The equation editor is adapted to receive equations describing a process to be controlled by the process controller. The equation editor is also adapted to generate model information representing the equations. The model factory is adapted to receive the model information and generate an equation stack representing the equations. The equation evaluation engine is adapted to receive evaluation information from the process controller, evaluate at least one of the equations using the evaluation information and the equation stack, and send a result of the evaluation to the process controller. The model information could include information representing algebraic equations, differential equations, algebraic states, differential states, inputs, parameters, constants, and/or expressions.

Abstract: A method and system for process control using a model predictive controller. The control system can have one or more control devices operably coupled to a processing system for controlling a process of the processing system; a modeling tool to provide a non-linear model based at least in part on the process and to provide a plurality of linearized models based at least in part on the non-linear model, where the plurality of linearized models are linearized at different linearization rates; and a controller operably coupled to the modeling tool. The controller can select one of the plurality of linearized models based on a comparison of the plurality of linearized models with a reference model. The controller can send one or more control signals to at least one of the one or more control devices. The one or more control signals can be determined using the selected one of the plurality of linearized models.

Abstract: A method and system for process control. The control system can be operably coupled to a processing system. The control system can include control devices operably coupled to the processing system; a modeling module to provide a linear model based at least in part on the processing system; a computational module to provide controller algorithms associated with the control devices; a user interface module to present at a user interface controller information based at least in part on the linear model and the controller algorithms; and a separate coordination module for establishing communication between the modeling module, the computational module and the user interface module. One or more control signals can be provided to at least one of the control devices for controlling the processing system. In one embodiment, the modeling module can generate the linear model from a non-linear process.