Fast and Accurate Trajectory Tracking Control of an Autonomous Surface Vehicle With Unmodeled Dynamics and Disturbances

Wang, Weining; Lv, Shuailin; Er, Meng Joo; Chen, Wenhua

doi:10.1109/tiv.2017.2657379

Cited by 123 publications

(36 citation statements)

References 40 publications

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“…Then, the "trivial" risk assessment result can be obtained whereas the "moderate" risk result is in the second order. Through survey, the subjective factors of 0.0000 0.0000 0.7793 0.0137 0.0000 0.2069 x 13 0.6400 0.0400 0.0000 0.0000 0.0000 0.3200 x 14 0.0000 0.0400 0.6400 0.0000 0.0000 0.3200 x 21 0.0000 0.0000 0.0000 0.4900 0.0900 0.4200 x 22 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 x 23 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 x 24 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 x 31 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 x 32 0.0000 0.0000 0.3600 0.1600 0.0000 0.4800 x 33 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 x 34 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 For the assessment efficiency, as a powerful criterion, the computational complexity is considered in depth. In Figure 3, we note that the larger the number of indicators , the higher the complexity O when the level of risks jY j is fixed.…”

Section: Numerical Study and Discussionmentioning

confidence: 99%

“…Subject to both fully unknown dynamics and complex input nonlinearities of autonomous vehicle, a Nussbaum-based adaptive fuzzy algorithm was further proposed in the homogeneity-based finite-time control framework. 23 In this article, it is our intention to solve the diplex problems: How to assess the navigation risk without any interference from subjective factors? How to make a credible decision based on the proposed intelligent scheme?…”

Section: Introductionmentioning

confidence: 99%

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Navigation risk assessment scheme based on fuzzy Dempster–Shafer evidence theory

2018

International Journal of Advanced Robotic Systems

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In order to reduce the influence of subjective factors on navigation risk assessment, we present a novel scheme based on the fuzzy Dempster-Shafer evidence theory in this article. At first, we deduce the fuzzy set after analyzing various indicators in actual vessel navigation as an example. What's more, the current data can be adaptively transformed into quantitative information on the basis of two-order membership functions. Considering the inherent defects of standard Dempster-Shafer evidence framework, the assessment process is improved to combine the uncertain and conflicting evidences from available indicators to make reliable decision. Further, three propositions are derived to explain the proposed scheme step by step. Finally, the numerical study results indicate that the proposed scheme has satisfactory performance for vessel navigation risk assessment in the complex waters without any subjective factors.

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Section: Numerical Study and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Navigation risk assessment scheme based on fuzzy Dempster–Shafer evidence theory

2018

International Journal of Advanced Robotic Systems

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“…As vehicles are progressing towards greater levels of autonomy, there have been a range of studies that explore various safety and security applications of fully autonomous vehicles. Many of these studies rely heavily on assumptions of correct data transmission and trust among vehicles [34] [38]. Progressing research in ITS security and privacy from a system-wide perspective is of paramount significance to ensure that security is maintained not only within one application sphere, but across multiple application spheres.…”

Section: Smart Vehicle Securitymentioning

confidence: 99%

Security and Privacy Issues in Intelligent Transportation Systems: Classification and Challenges

Hahn

Munir

Behzadan

2021

IEEE Intell. Transport. Syst. Mag.

117

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Intelligent Transportation Systems (ITS) aim at integrating sensing, control, analysis, and communication technologies into travel infrastructure and transportation to improve mobility, comfort, safety, and efficiency. Car manufacturers are continuously creating smarter vehicles, and advancements in roadways and infrastructure are changing the feel of travel. Traveling is becoming more efficient and reliable with a range of novel technologies, and research and development in ITS. Safer vehicles are introduced every year with greater considerations for passenger and pedestrian safety, nevertheless, the new technology and increasing connectivity in ITS present unique attack vectors for malicious actors. Smart cities with connected public transportation systems introduce new privacy concerns with the data collected about passengers and their travel habits. In this paper, we provide a comprehensive classification of security and privacy vulnerabilities in ITS. Furthermore, we discuss challenges in addressing security and privacy issues in ITS and contemplate potential mitigation techniques. Finally, we highlight future research directions to make ITS more safe, secure, and privacy-preserving.

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“…In [10], the method about dynamic output feedback controller for an ASV based on network model is discussed, which considers the packet dropouts between sampler and control station. In [11], based on the finite time control (HFC) framework, the trajectory tracking control for ASV with unknown variables is studied.…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis and T-S Fuzzy Dynamic Positioning Controller Design for Autonomous Surface Vehicles Based on Sampled-Data Control

Zheng

Shuzi

2020

IEEE Access

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This paper investigates the stability analysis and the fuzzy dynamic positioning (DP) controller design for an autonomous surface vehicle (ASV) based on sampled-data. Firstly, the Takagi-Sugeno (T-S) fuzzy model for the ASV with dynamic positioning system (DPS) is established. Then the criteria for asymptotically stability analysis and controller synthesis are provided by means of linear matrix inequalities. And less conservative results can be obtained by introducing convex reciprocal inequalities. Finally, simulation result is shown that the fuzzy sampled-data controller is effective to guarantee that the states of the ASV are stable and have good DP performance under the external disturbance. INDEX TERMS Autonomous surface vehicles, dynamic positioning system, T-S fuzzy model, sampleddata control.

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Fast and Accurate Trajectory Tracking Control of an Autonomous Surface Vehicle With Unmodeled Dynamics and Disturbances

Cited by 123 publications

References 40 publications

Navigation risk assessment scheme based on fuzzy Dempster–Shafer evidence theory

Navigation risk assessment scheme based on fuzzy Dempster–Shafer evidence theory

Security and Privacy Issues in Intelligent Transportation Systems: Classification and Challenges

Stability Analysis and T-S Fuzzy Dynamic Positioning Controller Design for Autonomous Surface Vehicles Based on Sampled-Data Control

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