DSP-Based Intelligent Adaptive Control System Using Recurrent Functional-Link-Based Petri Fuzzy-Neural-Network for Servo Motor Drive

This paper presents an intelligent adaptive control system (IACS) using a recurrent functional-linkbased Petri fuzzy-neural-network (RFLPFNN) for induction motor (IM) servo drive to achieve high dynamic performance. The proposed IACS comprises a RFLPFNN controller and a robust controller. The RFLPFNN controller is used as the main tracking controller to mimic an optimal control law while the robust controller is proposed to compensate the difference between the optimal control law and the RFLPFNN controller. Moreover, the structure and parameter-learning of the RFLPFNN are performed concurrently. Furthermore, an on-line parameter training methodology, which is derived based on the Lyapunov stability analysis and the back propagation method, is proposed to guarantee the asymptotic stability of the IACS for the IM servo drive. In addition, to relax the requirement for the bound of minimum approximation error and Taylor higher-order terms, an adaptive control law is utilized to estimate the mentioned bounds. A computer simulation is developed and an experimental system is established to validate the effectiveness of the proposed IACS. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the IACS grants robust performance and precise response regardless of load disturbances and IM parameters uncertainties. Key-Words: Functional-link neural-networks (FLNNs), intelligent control, indirect field-orientation control (IFOC), induction motor, Lyapunov satiability theorem, Petri net (PN), fuzzy-neural-network, robust control.