An efficient nonlinear predictive control algorithm with neural models based on multipoint on-line linearisation

This paper describes a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm and its application to a polymerisation reactor. A neural model of the process is used on-line to determine a local linearisation and a nonlinear free trajectory. Multipoint linearisation method is used, for each sampling instant within the prediction horizon one independent linearised model is obtained taking into account the current state of the process and the optimal input and output trajectory found at the previous sampling instant. In comparison with general nonlinear MPC technique, which hinges on nonlinear, usually non-convex optimisation, the presented structure is far more reliable and less computationally demanding because it results in a quadratic programming problem, whereas its closed-loop performance is similar.