Event-Triggered Composite Adaptive Fuzzy Output-Feedback Control for Path Following of Autonomous Surface Vessels
Yingjie Deng, Xianku Zhang
Abstract
This article investigates the adaptive fuzzy output-feedback path-following control of autonomous surface vessels (ASVs) via the event-triggered mechanism, in which the fuzzy logic systems (FLSs) are employed to approximate the uncertainties. By transforming the estimation errors, an adaptive fuzzy observer is established to estimate the kinematic states of the ASV, and a serial-parallel estimation model is fabricated based on this observer. By combining the tracking errors with the prediction errors between the observer and the serial-parallel estimation model, the composite adaptive laws are devised for the weights of the FLSs. Then, the event-triggered controller is developed in the channel of sensor to controller, in which the estimated kinematic states, the positional coordinates, and the heading angle of the ASV are sampled only when the triggering condition is violated. In this article, we put forward the problem of “jumps of virtual control laws” at the triggering instants, which commonly exists in the backstepping-based event-triggered control (ETC) in the channel of sensor to controller. To solve this problem, the dynamic surface filters are employed to generate the continuous substitutes for the virtual control laws. The proposed scheme can guarantee at least the similar control performance compared with its continuous alternatives while it importantly reduces the communication burden through the ETC and can enhance the estimation accuracy of the observer by applying the composite learning. All the errors are ensured to be semiglobally uniformly ultimately bounded in the closed-loop system. Finally, the effectiveness of the proposed scheme is verified through the numerical experiment.