GENERALIZED POLE PLACEMENT SELF-TUNING CONTROL WITH NEURAL NETWORK COMPENSATION

The Controlled plant is identified using normal linear model, and then the deviation identified by linear model is compensated via a neural network. The identification model is composed of a linear model and a neural network. Based on this model, an explicit generalized pole placement self-tuning control algorithm with neural network compensation is proposed. This algorithm is suitable for nonlinear system, and has higher precision, faster convergent speed and stronger robustness.