Redefined Output Model-Free Adaptive Control Method and Unmanned Surface Vehicle Heading Control

Liao, Yulei; Jiang, Quanquan; Du, Tingpeng; Jiang, Wen

doi:10.1109/joe.2019.2896397

Cited by 74 publications

(36 citation statements)

References 18 publications

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“…As shown in Figure 14b, given that the SPP method is optimized based on the initial path; it retains the fitness function standard of the initial planning and reduces the oscillation of the initial planning path through optimization. , and the trajectory tracking adopted the pure tracking (PP) method [35][36][37]. In the simulation experiment, when the time exceeded 50.0 s, the target point was replaced, although the vehicle had not reached the distance threshold of the target in the current tracking stage.…”

Section: J Mar Sci Eng 2019 7 X For Peer Reviewmentioning

confidence: 99%

“…On the Simulink simulation platform, a USV motion model was developed. The iterative step length and control cycle were 0.15 s. The parameters of the course PID controller were set as p = 1.0, i = 0.001, d = 1.0, the parameters of the speed controller were p = 2.0, i = 0.001, d = 0.001, and the trajectory tracking adopted the pure tracking (PP) method [35][36][37]. In the simulation experiment, when the time exceeded 50.0 s, the target point was replaced, although the vehicle had not reached the distance threshold of the target in the current tracking stage.…”

Section: J Mar Sci Eng 2019 7 X For Peer Reviewmentioning

confidence: 99%

“…, and the trajectory tracking adopted the pure tracking (PP) method [35][36][37]. In the simulation experiment, when the time exceeded 50.0 s, the target point was replaced, although the vehicle had not reached the distance threshold of the target in the current tracking stage.…”

Section: J Mar Sci Eng 2019 7 X For Peer Reviewmentioning

confidence: 99%

See 2 more Smart Citations

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

Fan

Liao

et al. 2019

JMSE

Self Cite

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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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Section: J Mar Sci Eng 2019 7 X For Peer Reviewmentioning

confidence: 99%

Section: J Mar Sci Eng 2019 7 X For Peer Reviewmentioning

confidence: 99%

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

Fan

Liao

et al. 2019

JMSE

Self Cite

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“…MFAC, on the other hand, is a recursive algorithm in which the control signal is calculated online directly from the inputoutput data without the need of prior knowledge of the controller structure. MFAC has been applied effectively to control various non-linear time-varying systems such as wide-area power systems [27], brushless DC motors [28], interlinked AC/DC microgrids [29], induction traction systems [30], fuel cells [31], microwave heating process [32], road traffic network [33], spacecraft launch vehicle [34], unmanned surface vehicles [35], autonomous cars [36] and life-critical implantable heart pump system [37]. In addition to the aforementioned model-free control approaches, reinforcement learning paradigm has been recently adopted for wind turbines with DFIG where the online controller was implemented using a single layer actor-critic neural network [38].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the performance of wind energy conversion systems using unified power flow controller

Mosaad

Alenany

Abu-Siada

2020

IET Generation, Transmission & Distribution

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“…In 2019, because the heading subsystem of a USV does not satisfy the quasi-linear assumption, Liao et al [34] redefined the output of a USV as the linear sum of the heading and angular velocity, based on which a redefined compact format model-free adaptive control (RO-CFDL-MFAC) method was proposed. However, the system's robustness is poor and the control performance is too sensitive to the redefined output gain when the RO-CFDL-MFAC method is applied to a USV's heading control.…”

Section: Introductionmentioning

confidence: 99%

Heading Control of Unmanned Surface Vehicle With Variable Output Constraint Model-Free Adaptive Control Algorithm

Jiang

Liao

et al. 2019

IEEE Access

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Based on model-free adaptive control (MFAC) theory, this paper presents a variable output constraint MFAC (VOC-MFAC) algorithm to enhance the robustness of an unmanned surface vehicle's (USV's) heading subsystem. The contributions of this paper are as follows. First, a controller output constraint function is proposed to solve the system's control performance sensitivity to the redefined output gain when the redefined compact format model free adaptive control (RO-CFDL-MFAC) method is used to control an unmanned surface vehicle's heading. Second, the compact format dynamic linearization data models for a USV's angular velocity subsystem and heading subsystem are established, and the convergence of the closed-loop system under environmental disturbances is proven through rigorous theoretical analysis. Finally, the control algorithm proposed in this paper is simulated and tested in the field using the ''Dolphin IB'' unmanned surface vehicle platform developed by our research group, and the effectiveness of the VOC-MFAC algorithm is verified by the experimental results. INDEX TERMS Unmanned surface vehicle, heading control, model-free adaptive control, output constraint function, field trial.

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Redefined Output Model-Free Adaptive Control Method and Unmanned Surface Vehicle Heading Control

Cited by 74 publications

References 18 publications

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

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

Enhancing the performance of wind energy conversion systems using unified power flow controller

Heading Control of Unmanned Surface Vehicle With Variable Output Constraint Model-Free Adaptive Control Algorithm

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