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Decentralized event-triggered cooperative path-following control for multiple autonomous surface vessels under actuator failures
Highlights An adaptive NN-based algorithm is embedded into the proposed fault-tolerant strategy, which do not require priori information of the system dynamics. a concise adaptive law is developed to compensate the bound of the weight matrix rather than the whole weight matrix of NNs. A novel event-triggered decentralized cooperative controller is presented to achieve motion synchronization of multiple ASVs and reduce the inter-vehicle communication burden.
Abstract This paper investigates the problem of cooperative path-following control for underactuated autonomous surface vessels in the presence of the model uncertainties, environmental disturbance, actuator failures and limited communication. A novel intelligent controller is designed for autonomous surface vessels to track priori parameterized reference paths so that the desired formation can be achieved. In the algorithm, the model uncertainties are estimated by neural networks. By virtue of the dynamic surface control technique, the inherent problem ”explosion of complexity” occurred in the traditional Backstepping design framework is avoided. A concise adaptive law is developed to compensate the upper bound of the weight matrix for neural networks, rather than the whole weight matrix. Furthermore, the event-triggered mechanism is embedded into the cooperative controller to reduce the frequency of inter-vehicle communication. The cooperative controller is updated only at the instant when the threshold condition is violated. By using the Lyapunov theory, both the semi-globally uniformly ultimately bounded and fault-tolerance capability of the closed-loop system are guaranteed. Finally, numerical examples are performed to demonstrate the effectiveness and superiority of the proposed algorithm.
Decentralized event-triggered cooperative path-following control for multiple autonomous surface vessels under actuator failures
Highlights An adaptive NN-based algorithm is embedded into the proposed fault-tolerant strategy, which do not require priori information of the system dynamics. a concise adaptive law is developed to compensate the bound of the weight matrix rather than the whole weight matrix of NNs. A novel event-triggered decentralized cooperative controller is presented to achieve motion synchronization of multiple ASVs and reduce the inter-vehicle communication burden.
Abstract This paper investigates the problem of cooperative path-following control for underactuated autonomous surface vessels in the presence of the model uncertainties, environmental disturbance, actuator failures and limited communication. A novel intelligent controller is designed for autonomous surface vessels to track priori parameterized reference paths so that the desired formation can be achieved. In the algorithm, the model uncertainties are estimated by neural networks. By virtue of the dynamic surface control technique, the inherent problem ”explosion of complexity” occurred in the traditional Backstepping design framework is avoided. A concise adaptive law is developed to compensate the upper bound of the weight matrix for neural networks, rather than the whole weight matrix. Furthermore, the event-triggered mechanism is embedded into the cooperative controller to reduce the frequency of inter-vehicle communication. The cooperative controller is updated only at the instant when the threshold condition is violated. By using the Lyapunov theory, both the semi-globally uniformly ultimately bounded and fault-tolerance capability of the closed-loop system are guaranteed. Finally, numerical examples are performed to demonstrate the effectiveness and superiority of the proposed algorithm.
Decentralized event-triggered cooperative path-following control for multiple autonomous surface vessels under actuator failures
Huang, Chenfeng (author) / Zhang, Xianku (author) / Zhang, Guoqing (author)
Applied Ocean Research ; 113
2021-06-04
Article (Journal)
Electronic Resource
English
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