Recurrent-fuzzy-neural-network sliding-mode controlled motor-toggle servomechanism

In this study, the dynamic responses of a recurrent-fuzzy-neural-network (RFNN) sliding-mode controlled motor-toggle servomechanism are described. The servomechanism is a toggle mechanism actuated by a permanent magnet (PM) synchronous servo motor. First, a newly designed total sliding-mode control system, which is insensitive to uncertainties including parameter variations and external disturbance in the whole control process, is introduced. The total sliding-mode control comprises the baseline model design and the curbing controller design. In the baseline model design a computed torque controller is designed to cancel the nonlinearity of the nominal plant. In the curbing controller design an additional controller is designed using a new sliding surface to ensure the sliding motion through the entire state trajectory. Therefore, in the total sliding-mode control system the controlled system has a total sliding motion without a reaching phase. Then, to overcome the two main problems with sliding-mode control, i.e., the assumption of known uncertainty bounds and chattering phenomenon in the control effort, a RFNN sliding-mode control system is investigated to control the motor-toggle servomechanism. In the RFNN sliding-mode control system a RFNN bound observer is utilized to adjust the uncertainty bounds real time. To guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Simulated and experimental results due to periodic sinusoidal command show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.