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  • 1.
    Hu, Xin
    et al.
    School of Mathematics and Statistics Science, Ludong University, Yantai, China.
    Zhu, Guibing
    Marine College, Zhejiang Ocean University, Zhoushan, China.
    Ma, Yong
    School of Navigation, Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan, China.
    Li, Zhixiong
    Faculty of Mechanical Engineering, Opole University of Technology, Opole, Poland.
    Malekian, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Sotelo, Miguel Angel
    Department of Computer Engineering, University of Alcalá, Alcalá de Henares, Spain.
    Dynamic Event-Triggered Adaptive Formation With Disturbance Rejection for Marine Vehicles Under Unknown Model Dynamics2023In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 72, no 5, p. 5664-5676Article in journal (Refereed)
    Abstract [en]

    This paper investigates the dynamic event-triggered adaptive neural coordinated disturbance rejection for marine vehicles with external disturbances as the sinusoidal superpositions with unknown frequencies, amplitudes and phases. The vehicle movement mathematical models are transformed into parameterized expressions with the neural networks approximating nonlinear dynamics. The parametric exogenous systems are exploited to express external disturbances, which are converted into the linear canonical models with coordinated changes. The adaptive technique together with disturbance filters realize the disturbance estimation and rejection. By using the vectorial backstepping, the dynamic event-triggered adaptive neural coordinated disturbance rejection controller is derived with the dynamic event-triggering conditions being incorporated to reduce execution frequencies of vehicle's propulsion systems. The coordinated formation control can be achieved with the closed-loop semi-global stability. The dynamic event-triggered adaptive disturbance rejection scheme achieves the disturbance estimation and cancellation without requiring the a priori marine vehicle's model dynamics. Illustrative simulations and comparisons validate the proposed scheme.

  • 2.
    Ma, Yong
    et al.
    Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China, also with the Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572000, China, and also with the Chongqing Research Institute, Wuhan University of Technology, Chongqing 401120, China..
    Zhao, Yujiao
    Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China, also with the Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572000, China, and also with the Chongqing Research Institute, Wuhan University of Technology, Chongqing 401120, China..
    Li, Zhixiong
    Faculty of Mechanical Engineering, Opole University of Technology, 45758 Opole, Poland, and also with the Yonsei Frontier Laboratory, Yonsei University, Seodaemun-gu, Seoul 03722, Republic of Korea.
    Bi, Huaxiong
    Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China, also with the Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572000, China, and also with the Chongqing Research Institute, Wuhan University of Technology, Chongqing 401120, China..
    Wang, Jing
    Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China, also with the Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572000, China, and also with the Chongqing Research Institute, Wuhan University of Technology, Chongqing 401120, China..
    Malekian, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Sotelo, Miguel Angel
    Department of Computer Engineering, University of Alcalá, 28801 Alcalá de Henares, Spain..
    CCIBA*: An Improved BA* Based Collaborative Coverage Path Planning Method for Multiple Unmanned Surface Mapping Vehicles2022In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 23, no 10, p. 19578-19588Article in journal (Refereed)
    Abstract [en]

    The main emphasis of this work is placed on the problem of collaborative coverage path planning for unmanned surface mapping vehicles (USMVs). As a result, the collaborative coverage improved BA* algorithm (CCIBA*) is proposed. In the algorithm, coverage path planning for a single vehicle is achieved by task decomposition and level map updating. Then a multiple USMV collaborative behavior strategy is designed, which is composed of area division, recall and transfer, area exchange and recognizing obstacles. Moverover, multiple USMV collaborative coverage path planning can be achieved. Consequently, a high-efficiency and high-quality coverage path for USMVs can be implemented. Water area simulation results indicate that our CCIBA* brings about a substantial increase in the performances of path length, number of turning, number of units and coverage rate.

  • 3.
    Zhu, Guibing
    et al.
    Zhejiang Ocean Univ, Marine Coll, Zhoushan 316022, Peoples R China..
    Ma, Yong
    Wuhan Univ Technol, Sch Nav, Hubei Key Lab Inland Shipping Technol, Wuhan 430063, Peoples R China..
    Li, Zhixiong
    Ocean Univ China, Sch Engn, Qingdao 266110, Peoples R China.;Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea..
    Malekian, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Sotelo, M.
    Univ Alcala De Henares, Dept Comp Engn, Madrid 28806, Spain..
    Adaptive Neural Output Feedback Control for MSVs With Predefined Performance2021In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 70, no 4, p. 2994-3006Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the problem of trajectory tracking control for marine surface vehicles (MSVs), which are subject to dynamic uncertainties, external disturbances and unmeasurable velocities. To recover the unmeasurable velocities, a novel adaptive neural network-based (NN-based) state observer is constructed. To guarantee the transient and steady-state tracking performance of the system, a novel nonlinear transformation method is proposed by employing a tracking error transformation together with a newly constructed performance function, which is characterized by a user-defined settling time and tracking control accuracy. With the aid of the state observer and the nonlinear transformation method in combination with the adaptive NN technique and vector-backstepping design tool, an adaptive neural output-feedback trajectory tracking control scheme with predefined performance is developed. With regard to the developed control scheme, uncertainties can be reconstructed only by utilizing the position and heading of the MSVs. Independent designs of the state observer and the controller can be achieved, and the position tracking error can be guaranteed to fall into a predefined residual set in the user-defined time frame and remain in the above set. A rigorous stability analysis validates that all signals in the closed-loop trajectory tracking control system for MSVs are uniformly ultimately bounded. Simulation results verify the effectiveness of the developed adaptive neural output-feedback trajectory tracking control scheme.

  • 4.
    Zhu, Guibing
    et al.
    School of Maritime, Zhejiang Ocean University, Zhoushan, China.
    Ma, Yong
    School of Navigation, Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan, China.
    Li, Zhixiong
    Faculty of Mechanical Engineering, Opole University of Technology, Opole, Poland.
    Malekian, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Sotelo, M.
    Department of Computer Engineering, University of Alcal, Alcala de Henares (Madrid), Spain.
    Dynamic Event-Triggered Adaptive Neural Output Feedback Control for MSVs Using Composite Learning2023In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 24, no 1, p. 787-800Article in journal (Refereed)
    Abstract [en]

    This paper investigates the control issue of marine surface vehicles (MSVs) subject to internal and external uncertainties without velocity information. Utilizing the specific advantages of adaptive neural network and disturbance observer, a classification reconstruction idea is developed. Based on this idea, a novel adaptive neural-based state observer with disturbance observer is proposed to recover the unmeasurable velocity. Under the vector-backstepping design framework, the classification reconstruction idea and adaptive neural-based state observer are used to resolve the control design issue for MSVs. To improve the control performance, the serial-parallel estimation model is introduced to obtain a prediction error, and then a composite learning law is designed by embedding the prediction error and estimate of lumped disturbance. To reduce the mechanical wear of actuator, a dynamic event triggering protocol is established between the control law and actuator. Finally, a new dynamic event-triggered composite learning adaptive neural output feedback control solution is developed. Employing the Lyapunov stability theory, it is strictly proved that all signals in the closed-loop control system of MSVs are bounded. Simulation and comparison results validate the effectiveness of control solution.

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