This paper proposes an intelligent hybrid control system (IHCS) for identification and control of micro-permanent-magnet synchronous motor (micro-PMSM) servo drive to achieve high precision tracking performance. The proposed control scheme incorporates a computed torque controller (CTC) based on the sliding-mode technique, a Petri recurrent-fuzzy-neural-network (PRFNN) controller (PRFNNC) and a PRFNN identifier (PRFNNI). First, a CTC is designed to stabilize the micro-PMSM servo drive system. However, particular information about the uncertainties of the micro-PMSM servo drive is required in the CTC law so that the corresponding control performance can not influenced seriously. Then, to improve the robustness of the servo drive system an IHCS is proposed. In the IHCS, the PRFNNC is used as the main tracking controller to mimic the CTC law and to preserve favorable model-following characteristics while the PRFNNI is utilized to identify the sensitivity information of the micro-PMSM servo drive system required for the PRFNNC. The online adaptive control laws are derived based on the Lyapunov stability theorem, the Taylor linearization technique and the back propagation method so that the stability of the micro-PMSM servo drive system can be guaranteed under occurrence of servo drive uncertainties. A computer simulation is developed to demonstrate the effectiveness of the proposed IHCS. The dynamic performance of the servo drive has been studied under load changes and parameters uncertainties. Accurate tracking response can be obtained due to the powerful on-line learning capability of PRFNN. In addition, the position tracking performance is significantly improved using the proposed IHCS and robustness to external disturbances can be obtained as well. Finally, the simulation results confirm that the IHCS grants robust performance and precise response regardless of load disturbances and micro-PMSM servo drive system parameter uncertainties.
Key-Words: Computed torque control, intelligent control, Lyapunov stability theorem, micro-permanent-magnet synchronous motor, Petri net (PN), recurrent-fuzzy-neural-network, sliding-mode control.
Fayez F. M. El-Sousy, "Intelligent Hybrid Controller for Identification and Control of Micro Permanent-Magnet Synchronous Motor Servo Drive System Using Petri Recurrent-Fuzzy-Neural-Network", WSEAS Transactions on Systems and Control, vol. 9, pp. 336-355, July 2014.
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