Learning and Near-Optimal Control of Underactuated Surface Vessels With Periodic Disturbances

Zhang, Yinyan; Li, Shuai; Weng, Jian

doi:10.1109/tcyb.2020.3041368

Cited by 21 publications

(3 citation statements)

References 46 publications

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“…The smoothness of the trajectory and motion state is guaranteed by the continuity theorem of higher-order derivatives of a curve. Meanwhile, numerical optimization methods are also widely used, such as minimum snap [ 20 ] and near-optimal control [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

A USV-UAV Cooperative Trajectory Planning Algorithm with Hull Dynamic Constraints

Huang

Chen

Wang³

et al. 2023

Sensors

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Efficient trajectory generation in complex dynamic environments remains an open problem in the operation of an unmanned surface vehicle (USV). The perception of a USV is usually interfered by the swing of the hull and the ambient weather, making it challenging to plan optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for a coupled USV-UAV system is proposed to ensure that a USV can execute a safe and smooth path as it autonomously advances through multi-obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role of a flight sensor, providing real-time global map and obstacle information with a lightweight semantic segmentation network and 3D projection transformation. An initial obstacle avoidance trajectory is generated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the effectiveness of the whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.

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Section: Related Workmentioning

confidence: 99%

A USV-UAV Cooperative Trajectory Planning Algorithm with Hull Dynamic Constraints

Huang

Chen

Wang³

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Various elegant control strategies and disturbance observers (DOs) have been presented for the USVs' motion control to make up the unmodeled dynamics of USVs and antidisturbances in working environments [8][9][10][11][12][13][14][15][16][17]. In [9], the path-following (PF) issue of USV was investigated under the actuator saturation and general disturbance uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Finite-Time Tracking Control of Unmanned Surface Vessel Using Neural Network Prescribed Performance

Liu,

Zhao,

Sun

et al. 2024

IEEE Access

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The current paper verifies the event-triggered finite-time tracking control of a fully actuated unmanned surface vessel under unmodeled dynamics and external disturbances. Radial basis function neural networks (RBFNNs), nonlinear disturbance observers (NDO), and the event-triggered mechanism (ETM) are utilized to design a new type of finite time tracking controller (FFTC). The proposed controller utilizes the dynamic surface control (DSC) approach to resolve the "differential explosion" issue of virtual control laws. Prescribed performance functions (PPFs) and the finite-time control (FFC) technique are utilized to specify the efficiency of tracking errors. The designed control laws make the control system of USV semi-globally practically finite-time stable (SGPFS) and make the tracking errors tend to a narrow residual set involving the specified bound in a finite time. In the end, the simulations reflect the presented FFTC's efficiency.

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“…Besides, a kind of ZNN that can suppress complex-valued disturbances was proposed to solve the problem of complexvalued time-varying matrix inversion with various noise conditions in Ref. [26], which made use of integral control techniques from the proportional-integral-derivative (PID) control system [27][28][29][30][31]. Generally speaking, the models studied in the above references are effective to overcome the disturbance problem.…”

Section: Introductionmentioning

confidence: 99%

An advanced discrete‐time RNN for handling discrete time‐varying matrix inversion: Form model design to disturbance‐suppression analysis

Shi

Cao

et al. 2023

CAAI Trans on Intel Tech

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Time‐varying matrix inversion is an important field of matrix research, and lots of research achievements have been obtained. In the process of solving time‐varying matrix inversion, disturbances inevitably exist, thus, a model that can suppress disturbance while solving the problem is required. In this paper, an advanced continuous‐time recurrent neural network (RNN) model based on a double integral RNN design formula is proposed for solving continuous time‐varying matrix inversion, which has incomparable disturbance‐suppression property. For digital hardware applications, the corresponding advanced discrete‐time RNN model is proposed based on the discretisation formulas. As a result of theoretical analysis, it is demonstrated that the advanced continuous‐time RNN model and the corresponding advanced discrete‐time RNN model have global and exponential convergence performance, and they are excellent for suppressing different disturbances. Finally, inspiring experiments, including two numerical experiments and a practical experiment, are presented to demonstrate the effectiveness and superiority of the advanced discrete‐time RNN model for solving discrete time‐varying matrix inversion with disturbance‐suppression.

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Learning and Near-Optimal Control of Underactuated Surface Vessels With Periodic Disturbances

Cited by 21 publications

References 46 publications

A USV-UAV Cooperative Trajectory Planning Algorithm with Hull Dynamic Constraints

A USV-UAV Cooperative Trajectory Planning Algorithm with Hull Dynamic Constraints

Event-Triggered Finite-Time Tracking Control of Unmanned Surface Vessel Using Neural Network Prescribed Performance

An advanced discrete‐time RNN for handling discrete time‐varying matrix inversion: Form model design to disturbance‐suppression analysis

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