Trajectory tracking of multi-legged robot based on model predictive and sliding mode control

Abstract

To address the problem of achieving accurate tracking control of multi-legged robots, which is difficult due to their strong coupling structure, this paper decomposes the trajectory tracking of the whole-body into body-level and limb-level sub-control systems. First, a tracking controller based on the model predictive control strategy is designed for the body-level subsystem, and the general projection neural network method is used to quickly solve for the optimal control signal that satisfies stride constraints. Then, a robust adaptive terminal sliding mode controller is designed for the limb-level subsystem to resist the influence of model uncertainties and external disturbances on tracking performance. The designed gait mode generator and stride solver modules serve as two bridges connecting the body- and limb-level subsystems, thus coalescing the entire control system into a closed loop, which greatly improves the tracking accuracy of the whole system. Finally, two cases, the directed curved-trajectory tracking with tripod gait and the undirected polyline-trajectory tracking with quadruped gait, are given to illustrate the omnidirectional mobility of the multi-legged robot, and to demonstrate the effectiveness and practicability of the proposed control strategy.