Dual-mode Model Predictive Control For Constrained Nonlinear Systems

Nonlinear model predictive control(NMPC) has received great attention in both academic and industrial societies because of its ability to explicitly handle constraints and multivariable nonlinear systems. Due to using the receding horizon principle, closed-loop stability of NMPC cannot, in general, be guaranteed automatically by itself. To this end, many schemes have been proposed to address the stability issue of NMPC for the last two decades. One of the main schemes used to ensure the stability of NMPC is the dual-mode control strategy. In conventional dual-mode NMPC, the terminal region, terminal control law and predictive horizon coupled between each other. As a result, the design of the dual-mode NMPC gets to be complex and the obtained performance has a more conservativeness. Therefore, it is necessary from the viewpoints of theory and applications to study some new dual-mode NMPC schemes for constrained nonlinear systems.Based on the existing achievements of NMPC stability, the dissertation uses the control lyapunov functions(CLF) technique and the multi-objective optimization method to study dual-mode NMPC and muti-objective dual-mode NMPC algorithms for the constrained nonlinear system with guaranteed recursive feasibility and stability. The main achievements and innovations of this dissertation are as follows:(1) A dual-mode NMPC algorithm is proposed for nonlinear systems subject to state and control constraints. The(local) control Lyapunov functions of nonlinear systems are exploited to design a local control law with some free-parameters. By picking the free-parameters makes the terminal region as large as possible, which will increase the size of the maximal feasible invariant set of the NMPC. By comparing to the conventional dual-mode NMPC strategy, the advantages of this algorithm were illustrated using a numerical example.(2) Considering multi-objective optimization control of nonlinear systems, I propose a new stabilizing multi-objective dual-mode tracking NMPC strategy. The concepts of the steady-state utopia point and compromise solution are used to reconcile the confliction of the multiple objectives. The multi-objective NMPC controller is computed by minimizing the distance of its cost vector to the utopia point, which is shown to stabilize the closed-loop system to the compromise solution using the dual-mode control principle. By comparing the conventional multi-objective utopia-tracking NMPC strategy, the advantages of this algorithm were illustrated using a numerical example.(3) The multi-objective optimization control of polymerization processes is considered. Using the dual-mode predictive control strategy, a multi-objective NMPC controller is presented for the polymerization process. By the linearized model of the polymerization nonlinear process at economically optimal operation set-point, a local control Lyapunov function is computed for the polymerization process. Then applying the proposed dual-mode NMPC strategy, the multiobjective NMPC controller of the polymerization process is designed. Finally, a free-radical polymerization process is used to illustrate the effectiveness of the designed NMPC controller.