Nonlinear Model Predictive Control Schemes with Guaranteed Stability

Abstract

Model predictive control (MPC), also referred to as moving horizon control or receding horizon control, has become an attractive feedback strategy, especially for linear or nonlinear systems subject to input and state constraints. In general, linear and nonlinear MPC are distinguished. Linear MPC refers to a family of MPC schemes in which linear models are used to predict the system dynamics, even though the dynamics of the closed-loop system is nonlinear due to the presence of constraints. Linear MPC approaches have found successful applications, especially in the process industries (Richalet, 1993). A complete overview on industrial MPC techniques with details and comparisons is given by Qin and Badgwell(1996), where more than 2200 applications in a very wide range from chemicals to aerospace industries are also summarized. By now, linear MPC theory is quite mature. Important issues such as stability are well addressed (see for example (Lee, 1996) for an overview).