Hybrid fuzzy neural-network control for nonlinear motor-toggle servomechanism

This study addresses the application of a hybrid fuzzy neural-network control (HFNNC) system to control a nonlinear mechanism system. First, the design procedures of the proposed HFNNC system are described in detail In the HFNNC system, a fuzzy neural-network (FNN) controller is the main tracking controller, which is used to mimic a perfect control law, and a compensated controller is proposed to compensate the difference between the perfect control law and the FNN controller. Then, an on-line parameter training methodology, which is derived using the Lya-punov stability theorem and the gradient descent method, is proposed to increase the learning capability of the FNN. Moreover, a toggle mechanism, which is driven by a permanent magnet (PM) synchronous, motor, is studied as an example to demonstrate the effectiveness of the proposed control technique. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. In addition, the advantages of the proposed control system are indicated in comparison with the traditional computed torque control system.