Active Disturbance Rejection Terminal Sliding Mode Control for Tele-Aiming Robot System Using Multiple-Model Kalman Observers

This study proposes a tele-aiming control strategy for the ground reconnaissance robot to track the maneuvering target rapidly in the presence of dynamic uncertainties, sensory measurement noises, and time-varying external disturbances. First, the tele-aiming control trajectory generated by human–computer interaction (HCI) device is filtered with a tracking differentiator and a recursive average filter. Second, the inertial impact force disturbance generated by maneuvering tele-aiming control jointly with the other uncertainties (e.g., internal friction, modeling error, etc.) is considered as a lumped disturbance, and then a novel multiple-model augmented-state extended Kalman observer (MEKO) is designed, capable of filtering out the joint measurement noises and estimating the lumped disturbance simultaneously. Lastly, a nonsingular terminal sliding mode controller is applied to eliminate the lumped disturbance and control the joints to track the corresponding desired joint trajectory. To verify the tele-aiming control performance, the random trajectory tracking experiments are designed to simulate the tele-aiming tracking control of maneuvering targets. As indicated from the experimental results, the proposed control strategy is capable of significantly suppressing the effect of inertial impact force disturbance and joint measurement noises, and achieving fast and stable tele-aiming control.