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.

Bio-inspired intelligence with applications to robotics: a survey

“…The proposed control considered the model input error that consequently have impacts on the tracking error, which was further reduced using fuzzy logic to incorporate with the bio-inspired backstepping control. [95] .…”

“…The mobile robot usually works in a complicated environment, which system and measurement noises can affect its tracking accurate. Therefore, an enhanced a bio-inspired backstepping control was proposed to generate the smooth, accurate velocity and torque command for mobile robots, respectively [95] . The total control Incorporated bio-inspired backstepping controller with unscented Kalman and Kalman filters that were suitable in real-world applications.…”

“…The comparisons of the traditional backstepping control and bio-inspired backstepping control. A: tracking a straight line; B: linear velocity estimates of tracking a straight line; C: peak linear velocity comparison of tracking straight line; D: tracking a circular line; E: linear velocity estimates of tracking a circular line; F: peak linear velocity comparison of tracking a circular line[95] .…”

A Model-based Parameter Optimization Control Strategy for Trajectory Tracking with Torque and Velocity Constraints

“…Xu et al [15] presented a tracking control strategy integrating a biologically inspired backstepping controller and a torque controller with an unscented Kalman filter and Kalman filter to avoid and reduce the velocity jumps and overshoots and provide smooth velocity commands. The upper and lower bounds of the speed are set by the B and D parameters in the biologically inspired neural network, and the control constraints are realized.…”

“…Compared with the control method proposed in [11]- [15], [17], [19], which can not achieve the error-free tracking well or the settling time is too long, the model-based control method presented in [16] can quickly realize the error-free track of the position vector, and the control method is simple. The controller designed in [18] only carried out a comparative discussion of several different control methods and did not provide the experimental curve diagram and data of the position vector tracking of the reference trajectory.…”