Adaptive neural model-based decentralized predictive control

This paper considers the problem of developing an adaptive neural model-based decentralized predictive controller for general multivariable non-linear processes, where the equations governing the system are unknown. It derives a method for implementing a neural network model for unknown non-linear process dynamics for adaptive control. The performance of this controller is demonstrated and evaluated using a simulated chemical process: multivariable non-linear control of distillation column. The simulation results indicate that the proposed control strategies have good practical potential for adaptive control of multivariable non-linear processes.