Segmented hybrid event‐triggered control for underactuated autonomous underwater vehicles with an asymmetrical prescribed performance constraint

• Published in: International journal of robust and nonlinear control Vol. 34; no. 12; pp. 7722 - 7745
• Main Authors: Su, Ziyi, Huang, Bing, Miao, Jianming, Lin, Xiaogong
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.08.2024
• Subjects: Actuators; Adaptive control; asymmetrical prescribed performance control; Asymmetry; Autonomous underwater vehicles; Control theory; Design parameters; Dynamic characteristics; Enhanced vision; minimum and maximum triggering intervals; Ocean currents; segmented hybrid event‐triggered mechanism; Tracking errors; Tracking problem; underactuated autonomous underwater vehicles
• ISSN: 1049-8923; 1099-1239
• DOI: 10.1002/rnc.7363

In this technical article, the trajectory‐tracking problem of event‐based adaptive prescribed performance control for underactuated autonomous underwater vehicles (AUVs) is considered. The primary innovation of this article is the proposal of a segmented event‐triggered mechanism (SETM) that incorporates preselected convergence time. This mechanism allows for the regulation of communication frequency in the control channel (controller‐to‐actuator) according to the dynamic characteristics of the AUV system at different stages. In addition to presenting new design scheme based on SETM, an enhanced vision known as segmented hybrid event‐triggered mechanism (SHETM) is introduced. Notably, the minimum and maximum triggering intervals (MITI and MATI) for these two ETMs can be calculated from the corresponding resettable dynamic variables presented in the trigger conditions. Subsequently, by utilizing the proposed asymmetrical prescribed performance control (APPC) strategy, the system's output tracking error behaviors in both transient and steady‐state stages can be qualitatively predetermined through design parameters within the boundary function. Moreover, the minimum learning parameter (MLP) based RBFNN adaptive control law is developed to counteract the effects of model uncertainties and ocean current perturbations. Finally, rigorous theoretical analysis and simulation results confirm the viability of the proposed scheme.