Enhanced Bioinspired Backstepping Control for a Mobile Robot With Unscented Kalman Filter

Abstract

Tracking control has been an important research topic in robotics. It is critical to design controllers that make robotic systems with smooth velocity commands. In addition, the robustness of the robotic system in the presence of system and measurement noises is an important consideration as well. This paper presents a novel tracking control strategy that integrates a biologically inspired backstepping controller and a torque controller with unscented Kalman filter (UKF) and Kalman filter (KF). The bioinspired backstepping controller and torque controller are capable of avoiding and reducing the velocity jumps and overshoots that occur in conventional backstepping control and provide smooth velocity commands. The integration of KF and UKF enables the proposed control strategy capable of providing accurate state estimates. The stability and convergence of tracking errors are guaranteed by Lyapunov stability analysis. The novelty of the proposed bioinspired tracking control strategy is to take the system and measurement noises and robot dynamic constraints into the consideration. The results show that the proposed control strategy provides accurate state estimates and avoids large velocity jumps and overshoot that occurs in conventional backstepping control. This tracking control strategy is suitable for autonomous mobile robots under hard conditions with system and measurement noises.

Keywords

• Trajectory tracking
• bioinspired neural dynamics
• backstepping control
• torque control
• unscented Kalman filter