MPC using Nonlinear Models Generated by Genetic Programming

摘要

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, based on this model, is expected to be good. The genetic programming approach and the nonlinear MPC strategy are briefly described, and demonstrated by simulation on a multivariable process.