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  • 1.
    Hu, X.
    et al.
    School of Mathematics and Statistics Science, Ludong University, Yantai, Shandong 264025, China..
    Zhu, G.
    Marine College, Zhejiang Ocean University, Zhoushan 316022, China..
    Ma, Y.
    Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China..
    Li, Z.
    Faculty of Mechanical Engineering, Opole University of Technology, 45-758 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.
    School of Mathematics and Statistics Science, Ludong University, Yantai, Shandong 264025, China..
    Event-Triggered Adaptive Fuzzy Setpoint Regulation of Surface Vessels With Unmeasured Velocities Under Thruster Saturation Constraints2022In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 23, no 8, p. 13463-13472Article in journal (Refereed)
    Abstract [en]

    This article investigates the event-triggered adaptive fuzzy output feedback setpoint regulation control for the surface vessels. The vessel velocities are noisy and small in the setpoint regulation operation and the thrusters have saturation constraints. A high-gain filter is constructed to obtain the vessel velocity estimations from noisy position and heading. An auxiliary dynamic filter with control deviation as the input is adopted to reduce thruster saturation effects. The adaptive fuzzy logic systems approximate vessel's uncertain dynamics. The adaptive dynamic surface control is employed to derive the event-triggered adaptive fuzzy setpoint regulation control depending only on noisy position and heading measurements. By the virtue of the event-triggering, the vessel's thruster acting frequencies are reduced such that the thruster excessive wear is avoided. The computational burden is reduced due to the differentiation avoidance for virtual stabilizing functions required in the traditional backstepping. It is analyzed that the event-triggered adaptive fuzzy setpoint regulation control maintains position and heading at desired points and ensures the closed-loop semi-global stability. Both theoretical analyses and simulations with comparisons validate the effectiveness and the superiority of the control scheme. 

  • 2.
    Huang, H.
    et al.
    Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing 210013, China.
    Hu, C.
    Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing 210013, China..
    Zhu, J.
    School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210013, China..
    Wu, M.
    Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing 210013, 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).
    Stochastic Task Scheduling in UAV-Based Intelligent On-Demand Meal Delivery System2022In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 23, no 8, p. 13040-13054Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the dynamic task scheduling problem with stochastic task arrival times and due dates in the UAV-based intelligent on-demand meal delivery system (UIOMDS) to improve the efficiency. The objective is to minimize the total tardiness. The new constraints and characteristics introduced by UAVs in the problem model are fully studied. An iterated heuristic framework SES (Stochastic Event Scheduling) is proposed to periodically schedule tasks, which consists of a task collection and a dynamic task scheduling phases. Two task collection strategies are introduced and three Roulette-based flight dispatching approaches are employed. A simulated annealing based local search method is integrated to optimize the solutions. The experimental results show that the proposed algorithm is robust and more effective compared with other two existing algorithms.

  • 3.
    Ma, Yong
    et al.
    Hubei Key Laboratory of Inland Shipping Technology, Wuhan, China; School of Navigation, Wuhan University of Technology, Wuhan, China.
    Nie, Zongqiang
    Hubei Key Laboratory of Inland Shipping Technology, Wuhan, China; School of Navigation, Wuhan University of Technology, Wuhan, China.
    Hu, Songlin
    Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China.
    Li, Zhixiong
    Department of Marine Engineering, Ocean University of China, Tsingdao, China; School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW, Australia.
    Malekian, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Sotelo, M.
    Department of Computer Engineering, University of Alcalá, Alcalá de Henares, Spain.
    Fault Detection Filter and Controller Co-Design for Unmanned Surface Vehicles Under DoS Attacks2021In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 22, no 3, p. 1422-1434Article in journal (Refereed)
    Abstract [en]

    This paper addresses the co-design problem of a fault detection filter and controller for a networked-based unmanned surface vehicle (USV) system subject to communication delays, external disturbance, faults, and aperiodic denial-of-service (DoS) jamming attacks. First, an event-triggering communication scheme is proposed to enhance the efficiency of network resource utilization while counteracting the impact of aperiodic DoS attacks on the USV control system performance. Second, an event-based switched USV control system is presented to account for the simultaneous presence of communication delays, disturbance, faults, and DoS jamming attacks. Third, by using the piecewise Lyapunov functional (PLF) approach, criteria for exponential stability analysis and co-design of a desired observer-based fault detection filter and an event-triggered controller are derived and expressed in terms of linear matrix inequalities (LMIs). Finally, the simulation results verify the effectiveness of the proposed co-design method. The results show that this method not only ensures the safe and stable operation of the USV but also reduces the amount of data transmissions.

  • 4.
    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.

  • 5.
    Zhao, Yujiao
    et al.
    Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan, China.
    Qi, Xin
    School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.
    Ma, Yong
    Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan, China.
    Li, Zhixiong
    School of Engineering, Ocean University of China, Tsingtao, China; School of Mechanical, Materials, Mechatronics, and Biomedical Engineering, University of Wollongong, Wollongong, NSW, Australia.
    Malekian, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Angel Sotelo, Miguel
    University of Alcalá, Alcalá de Henares, Spain.
    Path Following Optimization for an Underactuated USV Using Smoothly-Convergent Deep Reinforcement Learning2021In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 22, no 10, p. 6208-6220Article in journal (Refereed)
    Abstract [en]

    This paper aims to solve the path following problem for an underactuated unmanned-surface-vessel (USV) based on deep reinforcement learning (DRL). A smoothly-convergent DRL (SCDRL) method is proposed based on the deep Q network (DQN) and reinforcement learning. In this new method, an improved DQN structure was developed as a decision-making network to reduce the complexity of the control law for the path following of a three-degree of freedom USV model. An exploring function was proposed based on the adaptive gradient descent to extract the training knowledge for the DQN from the empirical data. In addition, a new reward function was designed to evaluate the output decisions of the DQN, and hence, to reinforce the decision-making network in controlling the USV path following. Numerical simulations were conducted to evaluate the performance of the proposed method. The analysis results demonstrate that the proposed SCDRL converges more smoothly than the traditional deep Q learning while the path following error of the SCDRL is comparable to existing methods. Thanks to good usability and generality of the proposed method for USV path following, it can be applied to practical applications.

  • 6.
    Zhu, G.
    et al.
    Maritime College, Zhejiang Ocean University, Zhoushan 316022, China..
    Ma, Y.
    Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China.
    Li, Z.
    School of Engineering, Ocean University of China, Qingdao 266110, China, and also with the Yonsei Frontier Lab, Yonsei University, Seoul 03722, Republic of Korea.
    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 Alcalá, 28806 Alcalá de Henares, Spain.
    Event-Triggered Adaptive Neural Fault-Tolerant Control of Underactuated MSVs With Input Saturation2022In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 23, no 7, p. 7045-7057Article in journal (Refereed)
    Abstract [en]

    This paper investigates the tracking control problem of marine surface vessels (MSVs) in the presence of uncertain dynamics and external disturbances. The facts that actuators are subject to undesirable faults and input saturation are taken into account. Benefiting from the smoothness of the Gaussian error function, a novel saturation function is introduced to replace each nonsmooth actuator saturation nonlinearity. Applying the hand position approach, the original motion dynamics of underactuated MSVs are transformed into a standard integral cascade form so that the vector design method can be used to solve the control problem for underactuated MSVs. By combining the neural network technique and virtual parameter learning algorithm with the vector design method, and introducing an event triggering mechanism, a novel event-triggered indirect neuroadaptive fault-tolerant control scheme is proposed, which has several notable characteristics compared with most existing strategies: 1) it is not only robust and adaptive to uncertain dynamics and external disturbances but is also tolerant to undesirable actuator faults and saturation; 2) it reduces the acting frequency of actuators, thereby decreasing the mechanical wear of the MSV actuators, via the event-triggered control (ETC) technique; 3) it guarantees stable tracking without the a priori knowledge of the dynamics of the MSVs, external disturbances or actuator faults; and 4) it only involves two parameter adaptations--a virtual parameter and a lower bound on the uncertain gains of the actuators--and is thus more affordable to implement. On the basis of the Lyapunov theorem, it is verified that all signals in the tracking control system of the underactuated MSVs are bounded. Finally, the effectiveness of the proposed control scheme is demonstrated by simulations and comparative results. 

  • 7.
    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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