Nonlinear Model Predictive Control Controller Research Articles

Dual Mode NMPC for Regulating the Concentration of Exothermic Reactor under Parametric Uncertainties

This paper addresses the implementation of a dual mode nonlinear model predictive control strategy integrating a nonlinear model predictive control (NMPC) with a local linear control method to control the product concentration in a continuous stirred tank reactor (CSTR), where a single exothermic reaction is carried out. For safe reactor operation, a reactor temperature path constraint is also considered in the NMPC formulation. Two well-developed linear controllers: state feedback linearizing control (SFC) and proportional-integral-derivative control (PID) technique are studied within the dual mode control framework. An Extended Kalman Filter (EKF) is incorporated into the dual mode NMPC to on-line estimate the unmeasured concentration of substances in the CSTR as well as uncertain key model parameters using an available temperature measurement. It has been found via simulation studies that the proposed dual mode NMPC, using either SFC or PID controller, connected with the EKF provides satisfactory control performance for set point tracking problem. The robustness of the controller in the presence of parametric uncertainties is also enhanced by the inclusion of the EKF. Moreover, the on-line computational time required by the dual mode NMPC is substantially decreased compared to that of an original NMPC.

Output feedback stabilization of constrained systems with nonlinear predictive control

AbstractWe present an output feedback stabilization scheme for uniformly completely observable nonlinear MIMO systems combining nonlinear model predictive control (NMPC) and high‐gain observers. The control signal is recalculated at discrete sampling instants by an NMPC controller using a system model for the predictions. The state information necessary for the prediction is provided by a continuous time high‐gain observer. The resulting ‘optimal’ control signal is open‐loop implemented until the next sampling instant. With the proposed scheme semi‐global practical stability is achieved. That is, for initial conditions in any compact set contained in the region of attraction of the NMPC state feedback controller, the system states will enter any small set containing the origin, if the high‐gain observers is sufficiently fast and the sampling time is small enough. In principle the proposed approach can be used for a variety of state feedback NMPC schemes. Copyright © 2003 John Wiley & Sons, Ltd.

Model Predictive Control of Interconnected Linear and Nonlinear Processes

A plant-wide control strategy based on integration of linear model predictive control (LMPC) and nonlinear model predictive control (NMPC) is proposed. The design method is applicable to plants that can be decomposed according to the nonlinearity properties of the individual unit operations. The basic idea is to apply LMPC and NMPC controllers to the linear and nonlinear subsystems, respectively. A systematic procedure for performing the plant decomposition given nonlinearity information is presented. Because the subsystems are coupled via material and energy flows, a sequential solution procedure that aims to minimize the amount of unknown information in the MPC designs is developed. The plant decomposition and sequential MPC solution algorithms are applied to a large-scale styrene production flowsheet. Three controller coordination strategies are developed to handle the information-exchange problems caused by sequential MPC solution. The methods are shown to be nominally stabilizing for nonlinear plants with a certain triangular structure. A multi-rate extension for plants with time-scale separations is presented. A reaction/separation process with recycle is used to compare the different hybrid MPC approaches.

APPLICATION OF MODEL REDUCTION FOR MODEL PREDICTIVE CONTROL

In this paper model reduction methods are used to obtain a nonlinear process model for designing a model predictive controller (MPC). The corresponding controller and its closed-loop response is then compared with controllers that are determined from the original model and a linearized version of this model. The reduced dimensional nonlinear MPC controller performs almost as well as the nonlinear MPC controller that is based on the original model and considerably better than the linear MPC controller.

NONLINEAR MPC FOR RECOMBINANT ZYMOMONAS MOBILIS FED-BATCH ETHANOL FERMENTATION

This work presents the development of an unstructured kinetic model incorporating the differing degrees of product inhibition on the kinetic rates of recombinant Zymomonas mobilis fermentation. A mechanism for the inhibition of two competing substrates cellular uptake via membrane transport is proposed. The model was utilized in a nonlinear model predictive control (NMPC) algorithm to control product concentration during fed-batch fermentation. Using optimal feeding policy determined online by the NMPC controller, the volumetric productivity of ethanol was improved by 16.6% relative to the equivalent batch operation when the same final ethanol concentration was reached.

A nonlinear industrial model predictive controller using integrated PLS and neural net state-space model

Model predictive control (MPC) technology has been well developed and successfully applied in the refinery and petrochemical process industries over the last 20 years. Recent development has been focused on nonlinear MPC and robust MPC technologies because new challenges have been encountered in the polymer and chemical industries where many processes show strong nonlinearity and uncertainty. This paper presents a nonlinear industrial model predictive controller, recently developed by Aspen Technology Inc. This MPC controller uses a nonlinear, state-space, integrated partial least-squares (PLS) and neural net model (Zhao, Guiver and Sentoni, American control conference, Philadelphia, PA, USA, 1998), and a multi-step, constrained, Newton-type optimization algorithm (Oliveira and Biegler, Automatica, 31 (2) (1995) 281–286). It results in a robust and cost-effective industrial nonlinear MPC controller. A pH reactor example and a successful industrial application in NO x emission control of a power plant are presented to demonstrate the capability of this controller.

A Nonlinear Industrial Model Predictive Controller Using Integrated PLS and Neural Net State Space Model

Model predictive control (MPC) teclmology has been well developed and successfully applied in the refinery and petrochemical process industries over last 20 years. Recent development has been focused on nonlinear MPC and robust MPC technologies because new challenges have been encountered in the polymer and chemical industries where many processes show strong nonlinearity and uncertainty. This paper presents a nonlinear industrial model predictive controller, recently developed by Aspen Teclmology, Inc. This MPC controller uses a nonlinear, state space, integrated PLS and Neural Net model (Zhao et al., 1998), and a multi-step, constrained- Newton-type optimization algorithm (Oliveira and Biegler. 1995). It results in a robust and cost-effective industrial nonlinear MPC controller. A pH reactor example and a successful industrial application in NOx emission control of a power plant are presented to demonstrate the capability of this controller.