Computationally efficient nonlinear predictive control based on state-space neural models

Abstract

This paper describes a computationally efficient nonlinear Model Predictive Control (MPC) algorithm in which a state-space neural model of the process is used on-line. The model consists of two Multi Layer Perceptron (MLP) neural networks. It is successively linearised on-line around the current operating point, as a result the future control policy is calculated by means of a quadratic programming problem. The algorithm gives control performance similar to that obtained in nonlinear MPC, which hinges on non-convex optimisation. © 2010 Springer-Verlag Berlin Heidelberg.