Nonlinear generalized predictive control method based on parameter optimization and compensation signal
For the tracking control problem of a class of complex nonlinear controlled processes with disturbances and unknown uncertainties,a compensation signal-driven nonlinear generalized predictive control method is proposed by combining the generalized predictive control and signal compensation techniques.The controlled object is represented by a low-order linear model and an unknown nonlinear term,which represents uncertainties such as system modeling errors and disturbances.A low-order linear model is used to design the generalized predictive controller.The tracking error affected by the unknown nonlinear term can be obtained from the generalized predictive control closed-loop system.A compensation signal is designed by introducing the one-step ahead optimal control to minimize the tracking error and the fluctuation of the control input,thereby eliminating the effect of the unknown nonlinear term on the controlled object and improving the dynamic performance of the system.The proposed method relaxes the previous condition that the unknown nonlinear term is globally bounded to the Lipschitz condition,which proves the stability and convergence of the closed-loop system.To further improve the dynamic performance of the system,an optimization method based on gradient descent for the weighting parameters of the controller is proposed.The effectiveness of the proposed method is verified by simulation comparison experiments.
万方数据
