Research on fixed time sliding mode control strategy based on RBF neural network

In order to achieve high-precision tracking control of the end of manipulator, a fixed-time sliding mode tracking control strategy based on radial basis function (RBF) neural network is proposed. First, the dynamic model of the manipulator is established. Then the RBF neural network is combined with the fixed-time sliding mode surface to design the RBF fixed-time sliding mode controller to achieve high-precision control of the end trajectory of the manipulator. And the theoretical feasibility of the designed controller is proved by the Lyapunov stability theory. Finally, a simulation experiment is carried out with the two-joint manipulator as the research object. The results show that the fixed-time sliding mode tracking control strategy of the RBF neural network can estimate the uncertain parameters in the model, effectively improve the control effect, and make the controller have fixed-time convergence characteristics, which improves the convergence speed of the manipulator.