Automated Nonlinear Model Predictive Control using Genetic Programming

Abstract This paper describes the use of genetic programming (GP) to generate an empirical dynamic model of a process, and its use in a nonlinear, model predictive control (NMPC) 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 NMPC strategy is expected to improve on the performance obtained using linear models. The GP approach and the nonlinear MPC strategy are described, and demonstrated by simulation on two multivariable process: a mixing tank, which involves only moderate nonlinearities, and the more complex Karr liquid–liquid extraction column.