MPC using nonlinear models generated by genetic programming

Abstract

This chapter 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 NMPC strategy are briefly described and demonstrated by simulation on a multivariable process. The application of GP–NMPC on the control of a mixing tank is also discussed. Discrete input–output models are generated to allow the prediction of level and concentration trajectories using the GP. Rapid acquisition of an empirical nonlinear model is achieved efficiently using GP. This model provides reliable prediction of future output trajectories in the NMPC scheme, which also accounts for both process interactions and constraint violations, and thus, allows the computation of improved control moves. Currently, work is in progress on the application of the approach on a more complex multiple-input, multiple-output (MIMO) process, a simulation of a Karr liquid–liquid extraction column.