Motion planning and adaptive neural sliding mode tracking control for positioning of uncertain planar underactuated manipulator

Abstract The research on the robust control of uncertain planar underactuated manipulators is almost nonexistent due to the lack of actuator and the uncontrollability at equilibrium points. Taking an uncertain planar three-link passive-active-active underactuated manipulator as an example, this paper develops a robust control scheme including motion planning and adaptive tracking control to realize its position control. According to the constraints between the passive link and active links and the target position of the manipulator, differential evolution algorithm is used to solve the target angles and ratio between the angular velocities. Then, a set of motion trajectories is planned based on the target values. Considering the uncertain parameter perturbation and external disturbance exist, we use RBF neural network to online approximate the uncertainty. Meanwhile, we develop a set of fast terminal sliding mode controllers to track the planned trajectories, and design adaptive laws to guarantee the stability and convergence of the tracking system. Next, an online iteration algorithm is presented to correct the deviations of all link angles caused by the parameter perturbation, which makes the manipulator gradually approach to its target position. Finally, the simulation results verify the effectiveness of the proposed method.