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Asymptotic Output Tracking Control of a Class of Linear Systems by Finite-and-Quantized Output Feedback

Jian Guo, Yanjun Zhang§, ( )Ji-Feng Zhang, 
Key Lab of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
School of Mathematics Sciences, University of Chinese Academy of Sciences, Bejing 100149, China
School of Automation, Beijing Institute of Technology, Beijing 100081, China
State Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China

*This paper is dedicated to the 60th birthday of Professor Zongli Lin.

This paper was recommended for publication in its revised form by Special Issue Editors: Jie Chen, Ben M. Chen and Jie Huang.

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Abstract

This paper presents a novel finite-and-quantized output feedback asymptotic tracking control method for a general class of continuous-time linear time-invariant systems. First, we construct a finite quantizer with time-varying thresholds and design a pole placement control law that exclusively utilizes the finite-and-quantized output signal and an external reference signal. Then, we establish the boundedness of all closed-loop signals and prove the asymptotic convergence of the output tracking error to zero. The proposed method combines the advantages of classical pole placement control technique and finite quantization feedback technique. It not only reduces the requirement for feedback information compared with existing tracking control methods but also effectively handles unstable poles and zeros in controlled systems, thereby achieving asymptotic output tracking. Finally, we provide a representative example to validate the effectiveness and new features of our proposed method.

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Unmanned Systems
Pages 249-260

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Cite this article:
Guo J, Zhang Y, Zhang J-F. Asymptotic Output Tracking Control of a Class of Linear Systems by Finite-and-Quantized Output Feedback. Unmanned Systems, 2024, 12(2): 249-260. https://doi.org/10.1142/S2301385024410085

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Received: 31 August 2023
Revised: 22 November 2023
Accepted: 23 November 2023
Published: 29 January 2024
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