Robust control for motor-toggle servomechanism using fuzzy neural network

A robust control system using a 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. 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 online parameter training methodology, which is derived using the Lyapunov stability theorem and the backpropagation method, is proposed to increase the teaming 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 the simulated and experimental results.