Use of state estimation for inferential nonlinear MPC: a case study

Abstract

Model predictive control (MPC) has become very popular in process industry and academia because it is an optimizing control technique which can handle hard constraints as well as time delays and mild nonlinearities. Linear MPC may control nonlinear processes by obtaining a linearized model of the plant, however, this approach is only valid in a limited region. In the presence of marked nonlinearities, a substantial improvement can be achieved by using the whole knowledge of the process dynamics. The use of a nonlinear model for MPC involves the knowledge of the complete state vector and the most significative perturbations in order to obtain the best performance. However, this information may not be directly available through measurement. In this paper, we propose the use of a nonlinear estimator to update the state vector and to infer the unmeasured perturbations. All the development herein presented is in the context of the control of an open-loop unstable nonlinear reactor with a measurement delay in the controlled variable.