Regular Paper

International Journal of Control, Automation and Systems 2023; 21(4): 1108-1118

Published online March 2, 2023

https://doi.org/10.1007/s12555-021-0882-6

© The International Journal of Control, Automation, and Systems

Output-feedback Robust Tracking Control of Uncertain Systems via Adaptive Learning

Jun Zhao and Yongfeng Lv*

Taiyuan University of Technology

Abstract

This paper presents an adaptive learning method to achieve the output-feedback robust tracking control of systems with uncertain dynamics, which uses the techniques developed for optimal control. An augmented system is first constructed using the system state and desired output trajectory. Then, the robust tracking control problem is equivalent to the optimal tracking control problem with an appropriate cost function. To design the output-feedback optimal tracking control, an output tracking algebraic Riccati equation (OTARE) is then constructed, which can be used in the online learning process. To obtain the solution of the derived OTARE, an online adaptive learning method is proposed, where the input gain matrix is removed. In this learning algorithm, only the system output information is required and the observers widely used in the output-feedback optimal control design are removed. Simulations based on the power system are given to test the proposed method.

Keywords Adaptive learning, optimal control, output-feedback robust control, robust tracking control.

Article

Regular Paper

International Journal of Control, Automation and Systems 2023; 21(4): 1108-1118

Published online April 1, 2023 https://doi.org/10.1007/s12555-021-0882-6

Copyright © The International Journal of Control, Automation, and Systems.

Output-feedback Robust Tracking Control of Uncertain Systems via Adaptive Learning

Jun Zhao and Yongfeng Lv*

Taiyuan University of Technology

Abstract

This paper presents an adaptive learning method to achieve the output-feedback robust tracking control of systems with uncertain dynamics, which uses the techniques developed for optimal control. An augmented system is first constructed using the system state and desired output trajectory. Then, the robust tracking control problem is equivalent to the optimal tracking control problem with an appropriate cost function. To design the output-feedback optimal tracking control, an output tracking algebraic Riccati equation (OTARE) is then constructed, which can be used in the online learning process. To obtain the solution of the derived OTARE, an online adaptive learning method is proposed, where the input gain matrix is removed. In this learning algorithm, only the system output information is required and the observers widely used in the output-feedback optimal control design are removed. Simulations based on the power system are given to test the proposed method.

Keywords: Adaptive learning, optimal control, output-feedback robust control, robust tracking control.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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