A fuzzy neural sliding mode control method is proposed for the trajectory tracking control of a multi-joint robot with modeling errors and uncertain interference. This method uses a global fast terminal sliding surface to ensure that the system can reach the sliding surface and equilibrium point from any initial state in a finite time. The fuzzy neural network is used to adaptively compensate for the modeling errors and external interference of the system, ensuring the movement of the sliding mode control on the sliding surface. The control law of the controller and the objective function of the fuzzy neural network are derived using the Lyapunov stability criterion. The chattering of the sliding mode control is weakened through online learning of the fuzzy neural network. The simulation results show its effectiveness.
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