A decentralized adaptive sliding mode control method for reconfigurable modular robots considering joint friction

The main challenge of the decentralized control method for reconfigurable modular robots lies in the handling of interconnection terms and joint friction torque. In this paper, an adaptive terminal sliding mode decentralized control method is proposed for the trajectory tracking problem of reconfigurable modular robots. Firstly, the robot dynamics equations are rewritten in the form of decentralized dynamics equations, and a radial basis neural network is used to fit the approximation for the local information term; for the interconnection terms, its bound function is transformed into a nonlinear function of local joint information and an adaptive compensation term is designed to compensate it; the LuGre friction model is used to accurately describe the joint friction torque, and a robust control term is designed to compensate the effect of the joint friction torque. The convergence of the control law is proved based on Lyapunov theory. Finally, the effectiveness of the control algorithm is verified by combining with simulation experiments.