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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.
    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.

  • 3. Liu, Wanli
    et al.
    Li, Zhixiong
    Malekian, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Angel Sotelo, Miguel
    Ma, Zhenjun
    Li, Weihua
    A Novel Multifeature Based On-Site Calibration Method for LiDAR-IMU System2020In: IEEE Transactions on Industrial Electronics, ISSN 0278-0046, E-ISSN 1557-9948, Vol. 67, no 11, p. 9851-9861Article in journal (Refereed)
    Abstract [en]

    Calibration is an essential prerequisite for the combined application of light detection and ranging (LiDAR) and inertial measurement unit (IMU). However, current LiDAR-IMU calibration usually relies on particular artificial targets or facilities and the intensive labor greatly limits the calibration flexibility. For these reasons, this article presents a novel multifeature based on-site calibration method for LiDAR-IMU system without any artificial targets or specific facilities. This new on-site calibration combines the point/sphere, line/cylinder, and plane features from LiDAR scanned data to reduce the labor intensity. The main contribution is that a new method is developed for LiDAR extrinsic parameters on-site calibration and this method could incorporate two or more calibration models to generate more accurate calibration results. First of all, the calibration of LiDAR extrinsic parameters is performed through estimating the geometric features and solving the multifeature geometric constrained optimization problem. Then, the relationships between LiDAR and IMU intrinsic calibration parameters are determined by the coordinate transformation. Lastly, the full information maximum likelihood estimation (FIMLE) method is applied to solve the optimization of the IMU intrinsic parameters calibration. A series of experiments are conducted to evaluate the proposed method. The analysis results demonstrate that the proposed on-site calibration method can improve the performance of the LiDAR-IMU.

  • 4.
    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.

  • 5.
    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.

  • 6.
    Ma, Yulin
    et al.
    Tsinghua University, Suzhou, China.
    Li, Zhixiong
    Ocean University of China, Tsingtao, China; Yonsei University, Seoul, South Korea.
    Malekian, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Zheng, Sifa
    Tsinghua University, Suzhou, China.
    Angel Sotelo, Miguel
    University of Alcalá, Alcalá de Henares, Spain.
    A novel multi-mode hybrid control method for cooperative driving of an automated vehicle platoon2021In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 8, no 7, p. 5822-5838Article in journal (Refereed)
    Abstract [en]

    A multi-mode hybrid automaton is proposed for setting vehicle platoon modes with velocity, distance, length, lane position and other state information. Based on a vehicle platoon shift movement under different modes, decisions are made based on key conditional actions such as sudden acceleration changes because of vehicle distance changes, emergency braking to avoid collisions and free-lane changing choices adapted to various traffic conditions, so as to ensure effortless movement and safety in multi-mode shift. With a 3-degree (longitudinal, lateral, and yaw directions) of freedom coupled model, a hybrid vehicle platoon controller is proposed using non-singular terminal sliding mode control to ensure fast and steady tracking on the hybrid automaton outputs during the multi-mode shift process. Convergence of the hybrid controller in finite time is also analyzed with the Lyapunov exponential stability. The analysis result proves that the proposed controller not only ensures the stability of the individual vehicle and the vehicle platoon, but also ensures stability of the multi-mode shift movement system. The proposed cooperative driving strategy for vehicle platoon is evaluated using simulations, where varying traffic conditions and the influence of cutting off are considered in conjunction with demonstration simulations of a vehicle platoon’s cruising, following, lane changing, overtaking and moving in/out of garage functions.

  • 7.
    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. 

  • 8.
    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.

  • 9.
    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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