A predefined-time radial basis function (RBF) neural network tracking control method considering actuator faults for a new type of spraying robot

Abstract. A small-range fine-spraying collaborative robot (SFSC) for vehicle surface repair has been designed, which has 4 degrees of freedom. Conventional control methods, such as sliding mode control (SMC) have difficulty meeting the accuracy requirements when the end of the attitude adjustment robotic arm control is spraying. Focusing on the problem of tracking control of a multi-joint robot with uncertain information, such as modeling uncertainty and random interference, a predefined-time radial basis function (RBF) neural network tracking control (PRC) method considering actuator fault is proposed for a new spraying robot. Firstly, the dynamics equations of the n-joint manipulator are derived using the Euler–Lagrange equation. Then, a new predefined-time sliding mode surface is designed based on the stability theory of PRC. Combined with the Euler–Lagrange dynamics model of the two-joint manipulator, a nonsingular PRC controller is designed according to the uncertainty in model parameters and external interference. Stability of the system is proven based on Lyapunov theory. The simulation results show that the designed controller can ensure that the state convergence of the system does not depend on the initial conditions and has a faster convergence rate, shorter convergence time and good robustness.