4.7 Article

Adaptive quantized sliding mode attitude tracking control for flexible spacecraft with input dead-zone via Takagi-Sugeno fuzzy approach

期刊

INFORMATION SCIENCES
卷 587, 期 -, 页码 746-773

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2021.11.002

关键词

Flexible spacecraft; T-S fuzzy model; Sliding mode control; Actuator input dead-zone; Signal quantization

资金

  1. China Scholarship Council [201906120101]
  2. National Natural Science Foundation of China [61833009, 61690212,11972130]
  3. Science Center Program of National Natural Science Foundation of China [62188101]
  4. Special Scientific Research Plan Project of Shaanxi Province Education Department [21JK0905]

向作者/读者索取更多资源

This paper presents a sliding mode control design method based on Takagi-Sugeno fuzzy model for the flexible spacecraft attitude tracking control system. By quantization and adaptive mechanism, the effects of external disturbances, actuator nonlinearity, and quantization errors are effectively addressed, and the finite-time reachability of the sliding surface is guaranteed.
This paper addresses the sliding mode control design problem for the flexible spacecraft attitude tracking control system with Takagi-Sugeno fuzzy model based method. In this design, the external disturbance and actuator input dead-zone are considered simultaneously, and the signals between sensors and controller are quantized before transmission over digital communication channels. A quantized adaptive integral sliding mode control law is proposed to ensure that the sliding motion is globally asymptotically stable, and the effects of unknown external disturbances, actuator nonlinearity and quantization errors are compensated via the online adaptive mechanism simultaneously. Moreover, under the proposed digital control law the finite-time reachability of the designed sliding surface can be guaranteed strictly. Finally, simulation results are presented to illustrate the feasibility and effectiveness of the developed quantized spacecraft attitude control method. (c) 2021 Elsevier Inc. All rights reserved.

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