For nonlinear system with random disturbance, common control algorithms such as PI control can not solve the tracking problem precisely. This paper presents nonlinear model predictive self-adaptive control over trajectory tracking of a nonlinear system. The designed controller subjected to Gauss white noise and control input restriction adjusts nonlinear predictive model to improve predictive precision and decide optimum control action by corresponding quadratic performance function during runtime. As the control action is behind the predict, the system becomes more robust compared with common...
