Robust Coordinated Tracking Control of Multiple Robots System Under Bounded Inputs

Abstract

The coordinated tracking control of multiple robots system with input saturation and obstacle avoidance is investigated in this paper. Additionally, a robust hierarchical outer-inner layer controller is proposed. In order to avoid obstacles and realize coordination tracking control, null-space-based behavioural (NSB) control is applied in the outer layer where it generates the velocity required by the inner layer. An improved adaptive radical basis function neural networks (RBFNNS) proportional derivative-sliding mode control (IRPD-SMC) method is proposed in the inner layer to achieve robust tracking, null steady-state errors, and bounded inputs. Finally, in an environment with obstacles, all robots can track the target at a fixed distance and distribute around it evenly. The convergence and stability of the system are certified by Lyapunov stability theory. Simulation results in the 2D and 3D space show that the proposed IRPD-SMC is effective, by comparing the performances of this method with those of proportional derivative-sliding mode control (PD-SMC), adaptive proportional derivative-sliding mode control (APD-SMC), and adaptive sliding mode control (ASMC).