Robust adaptive control for spacecraft final proximity maneuvers with safety constraint and input quantization

Li, Qi; Sun, Chong; Song, Shuo; Gou, Qiuxiong; Niu, Zhiqi

doi:10.1016/j.isatra.2020.10.064

Cited by 27 publications

(5 citation statements)

References 42 publications

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“…Although input quantization has superior data transmission performance and can save communication resource, the strong nonlinearity and quantization errors are inevitably produced, which limits the implementation of input quantization. Extensive efforts have been put on the application of input quantization, such as multi-agent systems, [30][31][32] aerospace engineering, [33][34][35] and manipulator systems. [36][37][38] Motivated by the aforementioned analyses, this paper develops an adaptive backstepping sliding mode control (ABSMC) scheme for AUVs subject to the dynamic uncertainty, external disturbance and quantization error.…”

Section: Doi: 101002/adts202100445mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Backstepping Sliding Mode Tracking Control For Autonomous Underwater Vehicles With Input Quantization

Wang

et al. 2022

Advcd Theory and Sims

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This paper proposes an adaptive backstepping sliding mode control (ABSMC) scheme for autonomous underwater vehicles (AUVs) subject to the dynamic uncertainty, external disturbance and quantization error. The control input signals including control forces and moment are quantized by a hybrid quantizer which is the combinination of a logarithmic quantizer and a uniform quantizer. The kinematic controller is designed by the backstepping control technique and the dynamic controller is developed using the sliding mode control method. In order to further improve the robustness of the closed-loop system, an adaptive law is employed to estimate the upper bound of the total uncertainties in real time. The stability of the closed-loop system is proved based on the Lyapunov theory and indicates that the proposed control method can force the AUV to track the desired trajectory. Simulation results demonstrate the effectiveness of the proposed control strategy.

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Section: Doi: 101002/adts202100445mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping Sliding Mode Tracking Control For Autonomous Underwater Vehicles With Input Quantization

Wang

et al. 2022

Advcd Theory and Sims

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“…Thus, scholars have proposed a variety of methods to deal with it. Based on the adaptive method, the control laws considering input constraints are designed for hypersonic vehicles, 22 spacecraft, 23 and robots. 24 Another idea is to design auxiliary systems to compensate for the input.…”

Section: Introductionmentioning

confidence: 99%

Distributed containment formation control for multiple unmanned aerial vehicles with parameter optimization based on deep reinforcement learning

Liu

Guo

2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper devotes to addressing the distributed containment formation control problem for multi-UAVs with collision avoidance and external disturbances. The proposed communication structure design algorithm enables the followers to form the pre-defined formation based on the containment control. Then, based on the information of the desired position for the followers, a novel Lyapunov function is designed to achieve global collision avoidance, and an adaptive backstepping containment control law is proposed. Moreover, by taking the advantage of deep reinforcement learning, a parameter optimization method is presented to balance the value of input signals and the performance of the controller. Finally, the simulation results demonstrate the superiority and effectiveness of the proposed algorithms.

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“…To the best of the authors' knowledge, few studies have been conducted from the perspective of constructing a quantized controller for spacecraft final proximity maneuver with finite-time convergence. Although Li et al [39] proposed an adaptive controller based on sliding mode methodology and logarithmic quantizer for the RVD problem, the controller has a defect of asymptotic convergence. Accordingly, a quantized control method in conjunction with finite-time and nonsingular TSMS for spacecraft final proximity maneuver is needed.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Finite-Time Control on SE(3) for Spacecraft Final Proximity Maneuvers with Input Quantization

Zhang

Yang

2021

International Journal of Aerospace Engineering

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In this paper, a bounded finite-time control strategy is developed for the final proximity maneuver of spacecraft rendezvous and docking exposed to external disturbance and input quantization. To realize the integrated control for spacecraft final proximity operation, the coupling kinematics and dynamics of attitude and position are modeled by feat of Lie group SE 3 . With a view to improving the convergence rate and reducing the chattering, an adaptive finite-time controller is proposed for the error tracking system with one-step theoretical proof of stability. Meanwhile, the hysteresis logarithmic quantizer is implemented to effectively reduce the frequency of data transmission and the quantization errors are reduced under the proposed controller. The algorithms outlined above are based on an integrated model expressed by SE 3 and denoted by uniform motion states, which can simplify the design progress and improve control precision. Finally, simulations are provided to exhibit the effectiveness and advantages of the designed strategy.

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Robust adaptive control for spacecraft final proximity maneuvers with safety constraint and input quantization

Cited by 27 publications

References 42 publications

Adaptive Backstepping Sliding Mode Tracking Control For Autonomous Underwater Vehicles With Input Quantization

Adaptive Backstepping Sliding Mode Tracking Control For Autonomous Underwater Vehicles With Input Quantization

Distributed containment formation control for multiple unmanned aerial vehicles with parameter optimization based on deep reinforcement learning

Adaptive Finite-Time Control on SE(3) for Spacecraft Final Proximity Maneuvers with Input Quantization

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