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 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: 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 controller performance monitoring in a process control system. A level of disturbance associated with the process system is determined, and at least one value identifying a stability measure of a controller in the process system is determined using the determined level of disturbance and operating data associated with operation of the process system. The at least one value is compared to at least one threshold value, and a problem with the controller is identified based on the comparison. As an example, the process system could represent a product production system. Also, the operating data could include at least one of: measurement data from one or more sensors and control data for one or more actuators. The controller may be operable to receive the measurement data and generate the control data.

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 controller performance monitoring in a process control system. A level of disturbance associated with the process system is determined, and at least one value identifying a stability measure of a controller in the process system is determined using the determined level of disturbance and operating data associated with operation of the process system. The at least one value is compared to at least one threshold value, and a problem with the controller is identified based on the comparison. As an example, the process system could represent a product production system. Also, the operating data could include at least one of: measurement data from one or more sensors and control data for one or more actuators. The controller may be operable to receive the measurement data and generate the control data.