Abstract
In this study, a robust control system using an Takagi–Sugeno–Kang (TSK) type fuzzy-neural-network (FNN) is proposed to control a nonlinear toggle mechanism that is driven by a permanent magnet (PM) synchronous servo motor. First, the dynamic model of a motor-toggle servomechanism is introduced. Then, a robust control system is proposed to control the motor-mechanism coupling system for periodic motion. The design procedure of the proposed robust control system is described in detail. In the robust control system, the FNN controller is the main tracking controller, which is used to mimic a perfect control law, and the compensated controller is proposed to compensate the difference between the perfect control law and the FNN controller. Moreover, an on-line parameter training methodology, which is derived using the Lyapunov stability theorem and the backpropagation method, is proposed to increase the learning capability of the FNN. In addition, to relax the requirement for the bound of minimum approximation error, a simple bound estimation algorithm is investigated to control the motor-mechanism coupling system. The effectiveness of the proposed control scheme is verified by both numerical simulation and experimental results.
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