Model Predictive Control of Nonlinear Processes in the Presence of Constraints

Abstract Nonlinear Model Predictive Control (NMPC), a strategy for constrained, feedback control of nonlinear processes, has been developed previously by Patwardhan and co-workers (1989). The algorithm uses a simultaneous solution and optimization approach to determine the open-loop optimal manipulated variable trajectory at each sampling instant. Feedback is incorporated via an estimator, which uses process measurements to infer unmeasured state and disturbance values. These are used by the controller to determine the future optimal control policy. The advantages of NMPC were found to be its robustness to modeling error, and its ability to deal with constraints in an explicit manner without appreciable degradation in the quality of control, or speed of response. In the present paper, we study (i) the effect of manipulated variable bounds on robustness, (ii) robustness to error in model structure, and (iii) ability of NMPC to reject unmeasured disturbances. In each instance, comparison is made with a discrete linear controller tuned using the IMC approach.