Approaches to the design of model predictive controllers for linear, piecewise linear and nonlinear systems

Abstract

The thesis is concerned with improving the performance and robustness of model predictive control (MPC) controllers for (1) constrained linear systems with bounded disturbances (LBD systems); (2) constrained piecewise linear systems with bounded disturbances (PWLBD systems); (3) constrained nonlinear systems. A multi-mode MPC controller is proposed for constrained LBD systems that guarantees constraint satisfaction and robust closed-loop stability. For constrained PWLBD systems, two approaches are proposed under the time optimal control (TOC) and MPC frameworks. The resulting control laws of these two approaches can guarantee robust closed-loop stability and can also be determined off-line. For constrained nonlinear systems, an approach is proposed to approximate the terminal set and the terminal cost off-line using support vector machine (SVM).