Design and Verification of Heading and Velocity Coupled Nonlinear Controller for Unmanned Surface Vehicle

Jin, Jiucai; Zhang, Jie; Liu, Deqing

doi:10.3390/s18103427

Cited by 19 publications

(19 citation statements)

References 26 publications

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“…Recently, USVs have been utilized worldwide in both civil and military fields, such as spot cruises in ocean ranching and multi-point water quality monitoring and sampling in vast water, due to the benefits of reduced casualty risk and increased mission efficiency [34,35,36]. A USV is typically equipped with a motion control unit, a sensor unit, a communications unit and an arming system.…”

Section: Simulations and Experimental Resultsmentioning

confidence: 99%

Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Xin

Sheng

et al. 2019

Sensors

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Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion.

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Section: Simulations and Experimental Resultsmentioning

confidence: 99%

Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Xin

Sheng

et al. 2019

Sensors

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“…In addition to the kinematic model, it is also necessary to analyze the force of the USV to set up the kinetic model. When designing the control system of the USV, there are clear advantages using the vectorial model [5] instead of the component forms of the Taylor-series expansions in the Abkowitz model [8,9]. The main reasons are that system properties such as symmetry, skew-symmetry and positiveness of matrices can be incorporated into the stability analysis [16].…”

Section: Three-dof Dynamic Model and Propeller Thrust Modelmentioning

confidence: 99%

“…It plays an essential role in some marine applications, such as seawater monitoring for fixed points, water sampling for fixed paths and even tracking control of the ordinary merchant ship [5,6,7]. For effective motion control of USVs, it is necessary to establish a mathematical model about the kinematics and kinetics performance of the USVs [8,9], which is the basis for realizing intelligent control and maneuverability prediction.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Experimental Testing of an Unmanned Surface Vehicle with Rudderless Double Thrusters

Jiang

Duan

et al. 2019

Sensors

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Motion control of unmanned surface vehicles (USVs) is a crucial issue in sailing performance and navigation costs. The actuators of USVs currently available are mostly a combination of thrusters and rudders. The modeling for USVs with rudderless double thrusters is rarely studied. In this paper, the three degrees of freedom (DOFs) dynamic model and propeller thrust model of this kind of USV were derived and combined. The unknown parameters of the propeller thrust model were reduced from six to two. In the three-DOF model, the propulsion of the USV was completely provided by the resultant force generated by double thrusters and the rotational moment was related to the differential thrust. It combined the propeller thrust model to represent the thrust in more detail. We performed a series of tests for a 1.5 m long, 50 kg USV, in order to obtain the model parameters through system identification. Then, the accuracy of the modeling and identification results was verified by experimental testing. Finally, based on the established model and the proportional derivative+line of sight (PD+LOS) control algorithm, the path-following control of the USV was achieved through simulations and experiments. All these demonstrated the validity and practical value of the established model.

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“…However, in a ship with double thrust, it is necessary to adjust the speed of the double propellers to achieve course control. At this time, the course control and speed control are coupled to control [52].…”

Section: Course Controlmentioning

confidence: 99%

State-of-the-Art Research on Motion Control of Maritime Autonomous Surface Ships

Wang

Liu

et al. 2019

JMSE

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At present, with the development of waterborne transport vehicles, research on ship faces a new round of challenges in terms of intelligence and autonomy. The concept of maritime autonomous surface ships (MASS) has been put forward by the International Maritime Organization in 2017, in which MASS become the new focus of the waterborne transportation industry. This paper elaborates on the state-of-the-art research on motion control of MASS. Firstly, the characteristics and current research status of unmanned surface vessels in MASS and conventional ships are summarized, and the system composition of MASS is analyzed. In order to better realize the self-adaptability of the MASS motion control, the theory and algorithm of ship motion control-related systems are emphatically analyzed under the condition of classifying ship motion control. Especially, the application of intelligent algorithms in the ship control field is summarized and analyzed. Finally, this paper summarizes the challenges faced by MASS in the model establishment, motion control algorithms, and real ship experiments, and proposes the composition of MASS motion control system based on variable autonomous control strategy. Future researches on the accuracy and diversity of developments and applications to MASS motion control are suggested.

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Design and Verification of Heading and Velocity Coupled Nonlinear Controller for Unmanned Surface Vehicle

Cited by 19 publications

References 26 publications

Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Modeling and Experimental Testing of an Unmanned Surface Vehicle with Rudderless Double Thrusters

State-of-the-Art Research on Motion Control of Maritime Autonomous Surface Ships

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