A comparative study of sliding-mode control and fuzzy neural network (FNN) control on the motor-toggle servomechanism is presented. The toggle mechanism is driven by a permanent-magnet synchronous servomotor. The rod and crank of the toggle mechanism are assumed to be rigid. First, Hamilton's principle and Lagrange multiplier method are applied to formulate the equation of motion. Then, based on the principles of the sliding-mode control, a robust controller is developed to control the position of a slider of the motor-toggle servomechanism. Furthermore, an FNN controller with adaptive learning rates is implemented to control the motor-toggle servomechanism for the comparison of control characteristics. Simulation and experimental results show that both the sliding-mode and FNN controllers provide high-performance dynamic characteristics and are robust with regard to parametric variations and external disturbances. Moreover, the FNN controller can result in small control effort without chattering.
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