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Improved Data-driven Adaptive Control Structure Against Input and Output Saturation

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Abstract

This article presents an improved data-driven adaptive control structure to address the problem of input and output saturation in unknown nonlinear systems with multiple inputs and multiple outputs. In the suggested structure, a virtual model of the controlled system is initially built utilizing a multi-layered group method of data handling neural network. The control signal is then applied to this virtual model to predict the output before being applied to the system. If the predicted output is saturated, the control signals are readjusted to prevent saturation and are then applied to the system. By using this proposed structure, the performance of model-free adaptive control against input/output saturation phenomena is improved and the occurrence of saturation is prevented. Based on Lyapunov’s theory, the stability of the suggested structure is proven. The controller has been applied to an interconnected three-tank system and a subway train which results clearly illustrate the advantages of the suggested method over the traditional form of model-free adaptive control design.

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Authors and Affiliations

  1. Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

    Yasin Asadi & Malihe Maghfouri Farsangi

  2. Department of Electrical Engineering, Yazd University, Yazd, Iran

    Mohammad Hadi Rezaei

Authors
  1. Yasin Asadi
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  2. Malihe Maghfouri Farsangi
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  3. Mohammad Hadi Rezaei
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Correspondence to Yasin Asadi.

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The authors have declared that they do not have any conflicting financial interests or personal relationships that could have potentially influenced the findings presented in this study.

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Yasin Asadi obtained his M.Sc. degree in control science and engineering from Amir Kabir University in Tehran, Iran, in 2015 and he received his Ph.D. degree in control science from Shahid Bahonar University in Kerman, Iran, in 2022. His research interests include load frequency control, model-free adaptive control, and data-driven control.

Malihe Maghfouri Farsangi received her B.S. and Ph.D. degrees in electrical engineering from Ferdowsi University and Brunel Institute of Power Systems, Brunel University, UK, respectively. Her research interests include data-driven control, networked control systems, and computational intelligence.

Mohammad Hadi Rezaei received his M.Sc. and Ph.D. degrees in control engineering from Tehran University and Amirkabir University, Tehran, Iran, in 2010 and 2018, respectively. His research interests include multi-agent systems and data-driven control.

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Asadi, Y., Farsangi, M.M. & Rezaei, M.H. Improved Data-driven Adaptive Control Structure Against Input and Output Saturation. Int. J. Control Autom. Syst. (2024). https://doi.org/10.1007/s12555-023-0437-0

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  • DOI: https://doi.org/10.1007/s12555-023-0437-0

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