Neural network-based tracking and synchronization control for nonlinear multi-motor driving servomechanism

This paper presents a neural network (NN)-based tracking and synchronization control for four-motor driving servomechanism, where the backlash and friction are considered as the nonlinearities to affect the control performance. Firstly, the desired position signal of each motor is designed to guarantee the precise output tracking. By incorporating the desired position signal and synchronization error, the generalized coupling error is proposed to transform the coupling issue of tracking and synchronization into the convergence of generalized coupling error. Based on the generalized coupling error, the adaptive NN-based integral sliding mode control is investigated to simultaneously achieve load position tracking and motor synchronization, which can successfully address the contradiction between overshoot and rapidness to further provide the little overshoot tracking performance with short settling time. Moreover, the NN is employed to approximate and compensate for the complicated nonlinearity, where the novel switching learning law is proposed to guarantee asymptotic error convergence rather than ultimately uniformly bounded. Finally, the simulation results on four-motor driving servomechanism illustrate the effectiveness of proposed method.