Compensated model-free adaptive tracking control scheme for autonomous underwater vehicles via extended state observer. (1st December 2020)

• Record Type: Journal Article
• Authors: Li, Xiaohan; Ren, Chao; Ma, Shugen; Zhu, Xinshan
• Is Part Of: Ocean engineering. Volume 217(2020)
• Publication Date: 2020-12-01
• Subjects: MFAC Model-free adaptive control -- SLESO Structure-improved linear extended state observer -- DLD Dynamic linearization data -- PJM Pseudo Jacobian matrix -- IAE Integral of absolute error -- MAE Maximum absolute error; Data-driven -- Anti-disturbance -- Parameter perturbation -- Model-free adaptive control -- Extended state observer -- Autonomous underwater vehicle; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2020.107976
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: This paper investigates the problem of precise trajectory tracking control of an autonomous underwater vehicle (AUV) under parameter perturbations and external disturbances. To design a data-driven control scheme with high tracking accuracy and strong robustness, a compensated model-free adaptive control (MFAC) scheme is proposed by combining an extended state observer (ESO). Specifically, a data-driven structure-improved linear ESO (SLESO) is derived to online estimate the model approximation error generated by pseudo Jacobian matrix estimation in the typical MFAC scheme. Another problem of the typical MFAC is that it cannot be directly applied to robotic systems with rotation in the inertial frame. To tackle this issue, a two-loop controller architecture is used to design the proposed SLESO-MFAC scheme. In addition, the structure of the mathematical model of AUVs and force analysis are used in MFAC design for the first time, thus providing a straightforward initial value setting method with explicit physical interpretation and helping to judge if the assumptions in the MFAC scheme can be satisfied. Furthermore, the stability analysis of the designed control system is given. Finally, the robustness and effectiveness of the proposed SLESO-MFAC scheme are substantiated via simulations and comparisons using a realistic dynamic model of the Falcon AUV. Highlights: A compensated MFAC scheme is proposed to suppress uncertainties and disturbances. A SLESO is derived to estimate the model approximation error in PJM estimation. Design guidance of MFAC is first provided for all robotic systems with rotation. A straightforward initial value setting method for PJM estimation is first proposed.