ADAPTIVE NEURAL-NETWORK PREDICTIVE CONTROL FOR NONMINIMUM-PHASE NONLINEAR PROCESSES

Abstract

An adaptive neural-network predictive control strategy for a class of nonlinear processes, which exhibit input multiplicities and change in the sign of steady-state gains, is presented. According to the graphic-based determination associated with prescribed input/output patterns, the feed-forward neural network (FNN) is attributed to reconstruct dynamic and steady-state characteristics of minimum-phase modes with specified operating ranges. The flexible predictive control strategy using on-line neuro-based adaptation is developed for enhancing the predictive capability of neural network. Finally, the proposed FNN-based implementation is illustrated on simulations of both isothermal and adiabatic CSTR systems.

Keywords:

• Adaptive mechanism
• Feed-forward neural network
• Nonminimum-phase systems
• Predictive control

Acknowledgments

This work was supported by the National Science Council of Taiwan under grant number NSC92-2214-E-224-002.