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Adaptive control for input-constrained linear systems

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Abstract

This paper proposes a direct model reference adaptive control method for linear systems with unknown parameters in the presence of input constraints. First, we used the well-known linear quadratic regulator (LQR) technique to develop a modified reference model, which is the optimal model under input constraints. Second, a model reference adaptive controller, which tracked the modified reference model instead of the reference model, was designed to compensate for parametric uncertainties. Using Lyapunov stability theory, we proved that the modified reference model tracking error converges to zero. Simulation results demonstrate the effectiveness of the proposed controller.

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Authors and Affiliations

  1. Department of Electronic Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju, 501-759, Korea

    Bong Seok Park

  2. Department of Electrical and Electronic Engineering, Yonsei University, Seodaemun-gu, Seoul, 120-749, Korea

    Jae Young Lee & Jin Bae Park

  3. Department of Electronic Engineering, Kyonggi University, Suwon-si, Kyonggi-do, Korea

    Yoon Ho Choi

Authors
  1. Bong Seok Park
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  2. Jae Young Lee
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  3. Jin Bae Park
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  4. Yoon Ho Choi
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Corresponding author

Correspondence to Jin Bae Park.

Additional information

Recommended by Editorial Board member Hamid Reza Karimi under the direction of Editor Ju Hyun Park.

Bong Seok Park received his B.S., M.S., and Ph.D. degrees in Electrical and Electronic Engineering from Yonsei University in 2005, 2008, and 2011, respectively. Since 2012, he has been with the Department of Electronic Engineering, Chosun University, where he is currently an Assistant Professor. His research interests include nonlinear control, adaptive control, formation control, and the control of robots.

Jae Young Lee received his B.S. degree in Information & Control Engineering in 2006 from Kwangwoon University, Seoul, Korea. He is currently pursuing a Ph.D. degree at Yonsei University. His major research interests include approximate dynamic programming/reinforcement learning, optimal/adaptive control, nonlinear control theories, neural networks, and power systems applications.

Jin Bae Park received his B.S. degree in Electrical Engineering from Yonsei University, Seoul, Korea, and his M.S. and Ph.D. degrees in Electrical Engineering from Kansas State University, Manhattan, in 1977, 1985, and 1990, respectively. Since 1992, he has been with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, where he is currently a professor. His major interest is the field of robust control and filtering, nonlinear control, intelligent mobile robots, fuzzy logic control, and neural networks. He served as the Editor-in-Chief (2006–2010) for the International Journal of Control, Automation, and Systems (IJCAS). He is serving as the President-Elect for the ICROS (2012-present).

Yoon Ho Choi received his B.S., M.S., and Ph.D. degrees in Electrical Engineering from Yonsei University, Seoul, Korea, in 1980, 1982, and 1991, respectively. Since 1993, he has been with the Department of Electronic Engineering, Kyonggi University, Suwon, Korea, where he is currently a Professor. His research interests include adaptive nonlinear control, intelligent control, multi-legged and mobile robots, networked control systems, and ADP based control. He is serving as the Vice-President for the ICROS (2012-present).

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Park, B.S., Lee, J.Y., Park, J.B. et al. Adaptive control for input-constrained linear systems. Int. J. Control Autom. Syst. 10, 890–896 (2012). https://doi.org/10.1007/s12555-012-0504-4

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  • DOI: https://doi.org/10.1007/s12555-012-0504-4

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