Model predictive control of nonlinear dynamical systems based on genetic programming

Model predictive control (MPC) requires an explicit dynamic model to predict values of the output variable, so the accuracy of the model significantly affects the quality of control. Unfortunately, it's hard to obtain the explicit expression of unknown nonlinear systems in MPC applications. This paper describes the use of genetic programming (GP) to generate an empirical dynamic model of a process, and to improve the performance in providing accuracy and suitability support for MPC strategy. GP derives both a model structure and its parameter values in such a way that the process trajectory is predicted accurately. Consequently, the performance of the MPC strategy is expected to improve on the performance obtained models. Experimental results show that the GP based predictive controller can obtain satisfactory performance.