Application of adaptive neural model-based control

Previous work extending identification using neural networks to the online adaptive case has resulted in poor performance. The work presented in this paper demonstrates a powerful method for implementing an adaptive neural network model of nonlinear process dynamics for control. This adaptive method has been amalgamated with a multistep nonlinear predictive control technique. The performance of this controller is demonstrated, and evaluated, using two simulated realistic processes.<>