Energy optimal guidance for proximity approach with obstacle avoidance

Ye, Dong; Sun, Jianye; Xiao, Yan; Sun, Zhiyong

doi:10.1016/j.ast.2022.107949

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…The command filter backstepping control method was employed to address the limitations of other methods such as dynamic surface control and tracking-differentiator-based control. On the other hand, it is important to acknowledge that unmodeled dynamics are inherent in real-world systems owing to various factors such as modeling inaccuracies, external disturbances, and measurement noise [14][15][16][17][18]. These unmodeled dynamics can significantly degrade system performance and even cause instability.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Fixed-Time Control for Nonlinear Systems with Unmodeled Dynamics

Luo,

Zhang,

et al. 2024

Symmetry

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This article concentrates on the problem of fixed-time tracking control for a certain class of nonlinear systems with unmodeled dynamics. Unmodeled dynamics are prevalent in practical engineering systems, such as axially symmetric systems like robotic arms, spacecraft, and missiles. In this paper, the fuzzy-logic systems (FLSs) are implemented to address the challenge of accurately approximating the unknown nonlinear terms that arise during the derived control algorithm process. By employing fixed-time command filters (FTCF), the “explosion of complexity” issues encountered in traditional backstepping methods will be effectively resolved. Moreover, error compensation mechanisms are derived to effectively mitigate the filtering errors that may arise from the FTCFs. The computational burden associated with FLSs is reduced through the utilization of the weight vector estimation method based on the maximal norm and an adaptive approach. A fixed-time adaptive fuzzy tracking controller is developed within the backstepping control framework to ensure the boundedness of all signals and achieve fixed-time convergence of the tracking error for the controlled system. Illustrative examples are conducted to illustrate the viability of the derived controller.

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Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Fixed-Time Control for Nonlinear Systems with Unmodeled Dynamics

Luo,

Zhang,

et al. 2024

Symmetry

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“…Over the past decade, finite-time control and backstepping control have emerged as two prominent research focuses in nonlinear systems. They have found widespread application in diverse practical engineering systems, including robot control systems, spacecraft control systems, unmanned marine vehicles, and multi-unmanned aerial vehicles [1][2][3][4][5]. In contrast to asymptotic control strategies, finite-time control strategies offer several advantages, including enhanced disturbance rejection capabilities, higher control precision, and faster convergence rates [6,7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Fixed-Time Control for Uncertain Nonlinear Systems with Mismatched Disturbances

Luo,

Zhang,

2024

Symmetry

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This paper focuses on addressing the adaptive fuzzy fixed-time issue for a class of nonlinear systems with uncertainty functions and mismatched disturbances. Fuzzy logical systems are utilized for identifying unknown functions. Additionally, to tackle challenges posed by mismatched disturbances, disturbance observers are constructed based on the backstepping method. Utilizing the adding one power integrator approach and the fixed-time control method, this paper introduces a fixed-time adaptive fuzzy control algorithm. Notably, this algorithm accommodates the presence of unknown mismatched disturbances and nonlinear functions. The paper establishes, through the application of the Lyapunov stability theory, that the designed adaptive fixed-time fuzzy control algorithm ensures practical fixed-time stability for the resulting closed-loop systems. Finally, the effectiveness of the derived strategy is demonstrated through an illustrative example involving two cases.

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A joint guidance and control framework for autonomous obstacle avoidance in quadrotor formations under model uncertainty

Wang

Chen

2023

Aerospace Science and Technology

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Energy optimal guidance for proximity approach with obstacle avoidance

Cited by 7 publications

References 37 publications

Adaptive Fuzzy Fixed-Time Control for Nonlinear Systems with Unmodeled Dynamics

Adaptive Fuzzy Fixed-Time Control for Nonlinear Systems with Unmodeled Dynamics

Adaptive Fuzzy Fixed-Time Control for Uncertain Nonlinear Systems with Mismatched Disturbances

A joint guidance and control framework for autonomous obstacle avoidance in quadrotor formations under model uncertainty

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