Adaptive neural network control with predictive compensation for uncertain nonlinear systems

The paper proposes an adaptive neural network control method. The proposed controller is based on the Generalized predictive control (GPC) algorithm, and a recurrent neural network (NN) is used to approximate the unknown nonlinear plant. To provide good accuracy in identification of unknown model parameters, an online adaptive law is proposed to adapt the consequent part of the NN. The stability of closed-loop control system is studied and proved via the Lyapunov stability theory. A nonlinear process is used to validate and demonstrate the performance of the proposed control. The simulation results show that the proposed method has good performance and disturbance rejection capacity in industrial processes and outperforms the PID and the classical GPC controllers.