Adaptive Backstepping Sliding Mode Tracking Control For Autonomous Underwater Vehicles With Input Quantization. Issue 4 (25th January 2022)

• Record Type: Journal Article
• Authors: An, Shun; Wang, Longjin; He, Yan; Yuan, Jianping
• Is Part Of: Advanced theory and simulations. Volume 5:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-25
• Subjects: autonomous underwater vehicles -- backstepping control technique -- input quantization -- sliding mode method -- trajectory tracking; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202100445
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: This paper proposes an adaptive backstepping sliding mode control (ABSMC) scheme for autonomous underwater vehicles (AUVs) subject to the dynamic uncertainty, external disturbance and quantization error. The control input signals including control forces and moment are quantized by a hybrid quantizer which is the combinination of a logarithmic quantizer and a uniform quantizer. The kinematic controller is designed by the backstepping control technique and the dynamic controller is developed using the sliding mode control method. In order to further improve the robustness of the closed‐loop system, an adaptive law is employed to estimate the upper bound of the total uncertainties in real time. The stability of the closed‐loop system is proved based on the Lyapunov theory and indicates that the proposed control method can force the AUV to track the desired trajectory. Simulation results demonstrate the effectiveness of the proposed control strategy. Abstract : A hybrid quantizer is proposed to quantify the control forces and the moment of autonomous underwater vehicles, which ensures reducible communication cost and acceptable quantization errors for better system performances. An adaptive backstepping sliding mode control scheme is presented for trajectory tracking control of autonomous underwater vehicles, where the dynamic uncertainty, external disturbance, and quantization error are simultaneously addressed.