Adaptive immersion and invariance induced optimal robust control of unmanned surface vessels with structured/unstructured uncertainties. (1st November 2021)

• Record Type: Journal Article
• Authors: Taghavifar, Hamid; Qin, Yechen; Hu, Chuan
• Is Part Of: Ocean engineering. Volume 239(2021)
• Publication Date: 2021-11-01
• Subjects: I&I -- PSO -- Autonomous vehicles -- Controls; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2021.109792
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: This paper addresses the path-tracking performance improvement of unmanned surface vessels (USVs) through a novel adaptive immersion and invariance (I&I) based robust adaptive control algorithm. The substantial drawback related to the control of USVs is the uncertain dynamics of the vessel, and unexpected environmental disturbances due to surges or crosswinds that promote the instability. The asymptotic stability of the closed-loop USV system is guaranteed based on the I&I stability theory, and the adaptation laws for the uncertain parameters are derived accordingly. The design variables of I&I algorithm are varied in a convex optimization problem with constraints on the control inputs. The robustness of the proposed algorithm is further assessed for the target USV subject to uncertain hydrodynamic damping in surge, sway and yaw. The performance of the proposed control algorithm is tested against benchmark schemes of disturbance observer-based composite nonlinear feedback (DO-CNF) and robust LQR (RLQR). The results are suggestive of the improved performance of the USV to follow the intended trajectory while guaranteeing optimality in the sense of actuation constraints. Highlights: A novel Adaptive Immersion and Invariance Induced Optimal Robust Control is proposed for USV tracking control. The entire Structured/Unstructured Uncertainties are included in developing the model dynamics. Disturbance observer-based composite nonlinear feedback and robust LQR were employed for benchmarking. A Chaotic-enhanced PSO algorithms was employed in the optimal control design stage.