Heading MFA control for unmanned surface vehicle with angular velocity guidance

Li, Ye; Wang, Leifeng; Jiang, Quanquan; Pan, Kaiwen

doi:10.1016/j.apor.2018.08.015

Cited by 31 publications

(13 citation statements)

References 17 publications

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“…Taking the Dolphin-I prototype USV as the experimental object, the formation simulation platform of five unmanned surface vehicles was established to verify the adaptive nullspace-based behavior formation control method proposed in this paper. The simulation platform was built using MATLAB software, and the PID control method [24] was adopted to control the course and speed.…”

Section: Numerical Simulation Of Musvs Formation Testmentioning

confidence: 99%

Formation Control of Multiple Unmanned Surface Vehicles Using the Adaptive Null-Space-Based Behavioral Method

et al. 2019

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This paper presents an adaptive null-space-based behavioral (NSB) method to deal with the problems of saturation planning and lack of adaptability when the traditional NSB method is applied to the formation control of multiple unmanned surface vehicles (MUSVs). First, the NSB method is analyzed, and the matrix theory is introduced to propose a behavior priority theory determination method based on a vector graph. Second, consider the maneuverability of the unmanned surface vehicle (USV), variable coefficients with physical significances are introduced to redefine the behavioral motion model, making the speed limit solved in each working condition within the maneuvering range of the USV and effectively improving the formation ability of MUSVs. Third, a logical priority collision avoidance strategy between the MUSVs is proposed, aiming at the problem that when the USVs judge each other as obstacles, both of them adopt the obstacle avoidance behavior resulting in two vehicles' courses deviating from the direction of the target point. Finally, a simulation platform for the formation control of MUSVs was established by taking the Dolphin-I prototype USV as the experimental object, and the feasibility of the proposed method was verified by a simulation test. INDEX TERMS Multiple unmanned surface vehicles (MUSVs), adaptability formation control, null-spacebased behavior (NSB), behavior priority, cooperative collision avoidance.

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Section: Numerical Simulation Of Musvs Formation Testmentioning

confidence: 99%

Formation Control of Multiple Unmanned Surface Vehicles Using the Adaptive Null-Space-Based Behavioral Method

et al. 2019

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“…Proportion integral derivative (PID) algorithm and various improved PID algorithm are the most applied algorithms in UMV heading control, at present, many researchers have done relevant research and obtained rich research results [13]- [23]. Many improved algorithms, such as Kalman filter [13], H-infinity control [14], self adaptive fuzzy control [15], [16], backstepping control [17], constrained self-tuning control [18], expert control and S surface control [19], adaptive control and hybrid control [20]- [23] are integrated into PID algorithm to improve the reliability, stability and accuracy of the heading control system and the whole UMV control system. A numerical method for minimum time heading control of an fixed speed UMV is proposed by Rhoads et al [24], while a Sugeno fuzzy inference system and Kalman filter are integrated into the heading control system by Toe et al [25], and a self-tuning fuzzy control algorithm is proposed by Fang et al [26].…”

Section: Introductionmentioning

confidence: 99%

Heading Control of Unmanned Marine Vehicles Based on an Improved Robust Adaptive Fuzzy Neural Network Control Algorithm

et al. 2019

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A robust adaptive fuzzy neural network control (RAFNNC) algorithm is proposed based on a generalized dynamic fuzzy neural network (GDFNN), proportion-integral-differential (PID), and improved bacterial foraging optimization (BFO) algorithm, for heading the control of the unmanned marine vehicle (UMV) in the presence of a complex environment disturbance. First, the inverse dynamic model of the motion control of UMV is established based on the GDFNN for the uncertain disturbance caused by the complex environment disturbance. Then, the adaptive rate of the fuzzy neural network is designed based on the error between the real UMV heading angle and designed reference heading angle, so as to further adjust the weight parameter of the GDFNN, and then, the output control value of the neural network is obtained. In order to further reduce the computation amount and computation time of the RAFNNC, the parameters of the PID control algorithm were optimized in advance by using the improved BFO algorithm. The fractal dimension step size and the intelligent probe operation are integrated into the BFO algorithm, in order to optimize the operation time and accuracy of the algorithm. Stability of the designed RAFNNC algorithm for the heading control of the UMV in the presence of complex marine environment disturbance is proved by the Lyapunov stability theory, and the effectiveness and accuracy of the control algorithm proposed are verified by semi-physical simulation experiment carried out in our laboratory. INDEX TERMS Unmanned marine vehicle (UMV), heading control, robust adaptive fuzzy neural network control (RAFNNC), generalized dynamic fuzzy neural network (GDFNN), bacterial foraging optimization (BFO).

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“…Unmanned surface vehicle (USV), which has attracted significant research attention in recent years, is used for dangerous and inhospitable missions [1,2]. At present, a USV mainly consists of a motion control system [3], sensor system, and communication system.…”

Section: Introductionmentioning

confidence: 99%

Second Path Planning for Unmanned Surface Vehicle Considering the Constraint of Motion Performance

Fan

Liao

et al. 2019

JMSE

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When utilizing the traditional path planning method for unmanned surface vehicles (USVs), ‘planning-failure’ is a common phenomenon caused by the inflection points of large curvatures in the planned path, which exceed the performances of USVs. This paper presents a second path planning method (SPP), which is an initial planning path optimization method based on the geometric relationship of the three-point path. First, to describe the motion performance of a USV in conjunction with the limited test data, a method of integral nonlinear least squares identification is proposed to rapidly obtain the motion constraint of the USV merely by employing a zig zag test. It is different from maneuverability identification, which is performed in combination with various tests. Second, the curvature of the planned path is limited according to the motion performance of the USV based on the traditional path planning, and SPP is proposed to make the maximum curvature radius of the optimized path smaller than the rotation curvature radius of the USV. Finally, based on the ‘Dolphin 1’ prototype USV, comparative simulation experiments were carried out. In the experiment, the path directly obtained by the initial path planning and the path optimized by the SPP method were considered as the tracking target path. The artificial potential field method was used as an example for the initial path planning. The experimental results demonstrate that the tracking accuracy of the USV significantly improved after the path optimization using the SPP method.

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Heading MFA control for unmanned surface vehicle with angular velocity guidance

Cited by 31 publications

References 17 publications

Formation Control of Multiple Unmanned Surface Vehicles Using the Adaptive Null-Space-Based Behavioral Method

Formation Control of Multiple Unmanned Surface Vehicles Using the Adaptive Null-Space-Based Behavioral Method

Heading Control of Unmanned Marine Vehicles Based on an Improved Robust Adaptive Fuzzy Neural Network Control Algorithm

Second Path Planning for Unmanned Surface Vehicle Considering the Constraint of Motion Performance

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