Intelligent Adaptive Dynamic Surface Control System with Recurrent Wavelet Elman Neural Networks for DSP-Based Induction Motor Servo Drives

In this paper, an intelligent adaptive dynamic surface control system (IADSCS) with recurrent wavelet Elman neural network (RWENN) for induction motor (IM) servo drive is proposed. The IADSCS comprises a dynamic surface controller (DSC), a recurrent wavelet Elman neural network (RWENN) uncertainty observer and a robust controller. First, a computed torque controller (CTC) is designed to stabilize the IM servo drive. Then, a nonlinear disturbance observer (NDO) is designed to estimate the nonlinear lumped parameter uncertainties existed in the CTC law. However, the IM servo drive performance is degraded by the NDO error due to the parameter uncertainties. To improve the robustness of the IM servo drive due to external load disturbances and parameter uncertainties, an IADSCS is designed to achieve this purpose. In the IADSCS, the DSC is used to overcome the explosion of the complexity in the backstepping design technique and the RWENN identifier is used to approximate the lumped parameter uncertainties and compounded disturbances. In addition, the robust controller is designed to recover the approximation error of the RWENN. The stability of the closed loop system is guaranteed by the Lyapunov stability theory. All control algorithms are implemented using dSPACE1104 DSP-based control computer. The simulation and experimental results show the superiority of the proposed IADSCS in external load disturbance suppression and parameter uncertainties.