The Design Of Model Predictive Controller For Nonlinear Systems Based On T-S Model

MPC can apply the historical and model's information to optimize the controlled system's performances,and correct the system through the measured outputs.For the hard constraint of the states being handled and controlled the stability of the system by MPC is guaranteed.Therefore model predictive control is widely used in industrial systems.Nonlinearity is a common system dynamic characteristic in industrial process,and there are common phenomena such as time delay and perturbation in nonlinear system.Due to the emergence of the proposed phenomena,the performance of the system significantly decreases.In this thesis,the problem of predictive control for nonlinear systems with time delay and perturbation is addressed by using linear matrix inequality(LMI),Lyapunov stability theory and input-to-state stability theory.The main contents of this thesis are as follows:Aiming at a class of nonlinear discrete systems with time-delay,the T-S fuzzy model is used to describe the systems and the “min-max” algorithm of model predictive control is given.On the basis of predictive control,fuzzy theory and the principle of parallel distributed compensation,at each sampling time,the corresponding state feedback control law is solved by optimizing the performance index of the infinite time domain,and the design problem of the predictive controller is discussed.The solvability of the problem is analyzed,and the algorithm for state feedback controller is given.Model predictive control for a class of nonlinear discrete systems with perturbation and time delay is addressed.The predictive model of the controlled system is given by using the T-S fuzzy model.In term of robust positive invariant set,linear matrix inequality and input-to-state stability theory,the performance optimization problem of the system with time-varying terminal constraint set is solved online,the sufficient conditions for the existence of the state feedback control law are given,and the zero-step predictive control algorithm is presented.Furthermore,the integrated approach of offline design and online optimization is used,the concept of robust N-step set and strategy of dual-mode control are used,and N-step predictive control algorithm is given.The input-to-state stability of the system is realized and the online calculation is reduced obviously.