A neuro-predictive based self-tuning controller

A self-tuning controller for a class of processes with predictable dynamics variations using model predictive scheme is presented in this paper. The self-tuning controller design is based on the optimization of a cost function subject to constraints over a finite prediction horizon in time, and, it uses a neural process model. The performance of this new self-tuning controller is substantiated by experiments on a pH control system. Simulation results show how the model predictive scheme is involved in the self-tuning control of nonlinear systems.