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Fuzzy Neural Network Control for Robot Manipulator Directly Driven by Switched Reluctance Motor

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  • Baoming Ge

    (Beijing Jiaotong University, China, and Michigan State University, USA)

  • Aníbal T. de Almeida

    (University of Coimbra, Portugal)

Abstract

Applications of switched reluctance motor (SRM) to direct drive robot are increasingly popular because of its valuable advantages. However, the greatest potential defect is its torque ripple owing to the significant nonlinearities. In this paper, a fuzzy neural network (FNN) is applied to control the SRM torque at the goal of the torque-ripple minimization. The desired current provided by FNN model compensates the nonlinearities and uncertainties of SRM. On the basis of FNN-based current closed-loop system, the trajectory tracking controller is designed by using the dynamic model of the manipulator, where the torque control method cancels the nonlinearities and cross-coupling terms. A single link robot manipulator directly driven by a four-phase 8/6-pole SRM operates in a sinusoidal trajectory tracking rotation. The simulated results verify the proposed control method and a fast convergence that the robot manipulator follows the desired trajectory in a 0.9-s time interval.

Suggested Citation

  • Baoming Ge & Aníbal T. de Almeida, 2011. "Fuzzy Neural Network Control for Robot Manipulator Directly Driven by Switched Reluctance Motor," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 5(3), pages 86-98, July.
    • Handle: RePEc:igg:jcini0:v:5:y:2011:i:3:p:86-98
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