ADP-Based Spacecraft Attitude Control Under Actuator Misalignment and Pointing Constraints

Yang, Haoyang; Hu, Qinglei; Dong, Hongyang; Zhao, Xiaowei

doi:10.1109/tie.2021.3116571

Cited by 32 publications

(9 citation statements)

References 26 publications

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“…It can extract the core information of systems through data/measurements to achieve multiple tasks [107][108][109]. RL techniques have made great contributions to a wide range of fields, such as the control problems of robots [110], spacecraft [111] and autonomous vehicles [112]. In addition, some studies have applied RL methods to wind turbine control [113][114][115][116][117].…”

Section: Fundamentals Of Rlmentioning

confidence: 99%

Wind farm control technologies: from classical control to reinforcement learning

2022

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Wind power plays a vital role in the global effort towards net zero. The recent figure shows that 93GW new wind capacity was installed worldwide in 2020, leading to a 53% year-on-year increase. Control system is the core in wind farm operations and has essential influences on the farm’s power capture efficiency, economic profitability, and operation & maintenance cost. However, wind farms’ inherent system complexities and the aerodynamic interactions among wind turbines bring significant barriers to control systems design. The wind industry has recognized that new technologies are needed to handle wind farm control tasks, especially for large-scale offshore wind farms. This paper provides a comprehensive review of the development and most recent advances of wind farm control technologies. This covers the introduction of fundamental aspects in wind farm control in terms of system modelling, main challenges, and control objectives. Existing wind farm control methods for different purposes, including layout optimization, power generation maximization, fatigue loads minimization, and power reference tracking, are investigated. Moreover, a detailed discussion regarding the differences and connections among model-based, model-free and data-driven wind farm approaches is presented. In addition, highlights are made on the state-of-the-art wind farm control technologies based on reinforcement learning - a booming machine learning technique that has drawn worldwide attention. Future challenges and research avenues in wind farm control are also analysed.

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Section: Fundamentals Of Rlmentioning

confidence: 99%

Wind farm control technologies: from classical control to reinforcement learning

2022

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“…Besides the impact of the above factors, the spacecraft is also subject to the constraints of actuators, 31,32 input saturation, 33 or rates 34 while in the process of on-orbit servicing. High-performance attitude control schemes are of great significance for the attitude alignment and maneuvering accuracy of spacecraft, thus constrained attitude control strategies 3,35,36 which responses timely and guarantees the system stability attract a very wide range of attentions and applications.…”

Section: Introductionmentioning

confidence: 99%

Fixed-time fuzzy disturbance observer–based adaptive integral sliding-mode spacecraft attitude control under time-varying output constraints

Zhu,

Ning,

Wang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article proposes a fixed-time fuzzy disturbance observer–based adaptive integral sliding-mode spacecraft control scheme with consideration of input uncertainties and unknown nonlinearities. First, for the purpose of satisfying the time-varying output constraints and simplifying the computational process, an error transformation function is employed to convert the constrained spacecraft system into an unconstrained one. Second, to ensure superior transient-state performance, an improved sliding manifold integrated with a nonlinear function is designed. Then, for accurately estimating the external disturbances, a fixed-time fuzzy disturbance observer is proposed. Furthermore, the unknown nonlinearities and the input uncertainties are handled by several adaptive laws. The simulation results are presented to verify the superiority and effectiveness of the proposed fixed-time fuzzy disturbance observer–based adaptive integral sliding-mode control scheme.

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“…In recent years, the control strategy based on ADP has been studied in the aerospace field. In reference [5], the attitude control problem with pointing constraints is studied based on ADP. Based on ADP, the integrated guidance and control problem with impact angle and field of view constraints is studied in reference [6].…”

Section: Introductionmentioning

confidence: 99%

An approximate optimal control method for 6-DOF spacecraft

Mao,

Sun,

Lei

2024

J. Phys.: Conf. Ser.

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In this paper, an approximate optimal control method based on adaptive dynamic programming (ADP) is proposed for the proximity operations with six degrees of freedom (6-DOF) relative motion control of spacecraft. Firstly, the dynamic model is normalized and dimension-reduced to avoid the excessive gradient change of the neural network. Then, a nonlinear disturbance observer is designed to deal with the disturbance. Finally, using ADP ‘s reinforcement learning idea, a single-critic neural network is constructed to solve the approximate optimal control strategy and minimize the cost function. The simulation results show that the designed controller can achieve the attitude and orbit maneuvering targets, and the overall energy consumption is approximately optimal.

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ADP-Based Spacecraft Attitude Control Under Actuator Misalignment and Pointing Constraints

Abstract: How to cite:Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 32 publications

References 26 publications

Wind farm control technologies: from classical control to reinforcement learning

Wind farm control technologies: from classical control to reinforcement learning

Fixed-time fuzzy disturbance observer–based adaptive integral sliding-mode spacecraft attitude control under time-varying output constraints

An approximate optimal control method for 6-DOF spacecraft

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