This article presents a robust adaptive neural control algorithm for the wing-sail-assisted vehicle to track the desired waypoint-based route, where the event-triggered mechanism is with the multiport form. The main features of the proposed algorithm are three-fold: 1) the communication burden, in the channel from the sensor to the controller as well as the actuator, has been reduced for the merits of the multiport event-triggered approach. The feedback error signals and the control input will be updated only on the event-triggered time point; 2) for the wing-sail-assisted vehicle, the thrust force is provided by devices with the propeller and the sail. From this consideration, the proper sail force compensation is derived on the basis of information about the current heading angle and the wind direction. The corresponding control law can guarantee the energy-saving for the propeller; and 3) in the algorithm, the system uncertainties are remodeled by the neural-network approximator. Furthermore, by fusion of the robust neural damping and dynamic surface control (DSC) techniques, the corresponding gain-related adaptive law is developed to address constraints of the gain uncertainty and the environmental disturbances. Through the Lyapunov theorem, all signals of the closed-loop control system have been proved to be with the semiglobal uniform ultimate bounded (SGUUB) stability, including the triggered time point and the intermediate triggered interval. Finally, the numerical simulation and the practical experiment are illustrated to verify the effectiveness of the proposed strategy.
|Number of pages||13|
|Journal||IEEE Transactions on Cybernetics|
|State||Published - Dec 1 2022|
Bibliographical noteFunding Information:
The work of Guoqing Zhang, Jiqiang Li, and Cheng Liu was supported in part by the National Natural Science Foundation of China under Grant 51909018; in part by the Natural Science Foundation of Liaoning Province under Grant 20170520189 and Grant 20180520039; in part by the Science and Technology Innovation Foundation of Dalian City under Grant 2019J12GX026; in part by the Fundamental Research Funds for the Central Universities under Grant 3132021132 and Grant 3132020124; and in part by the National Postdoctoral Program for Innovative Talents under Grant BX201600103.
© 2013 IEEE.
- Adaptive neural control
- event-triggered control
- marine vehicle
- path following
ASJC Scopus subject areas
- Control and Systems Engineering
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering