Model-Free Semi-Global Output Regulation for Discrete-Time Linear Systems Subject to Input Amplitude Saturation
In this paper, a data-driven method is developed based on off-policy reinforcement learning to solve the semi-global output regulation of discrete-time linear systems with input saturation. Algebraic Riccati equation based method is used to design a family of state feedback laws for the constrained output regulation problem. In contrast to the existing methods, complete knowledge of the system dynamics is no longer required in this paper. Instead, the data collected from on-line is efficiently utilized to obtain the adaptive optimal control policy. It is shown that the presented method can find feedback control inputs with constraint of amplitude saturation and the ability to stabilize a given linear system with all its poles inside or on the unit circle. Finally, a simulation example is carried out to demonstrate the conclusions of the whole paper.
Y. Yang et al., "Model-Free Semi-Global Output Regulation for Discrete-Time Linear Systems Subject to Input Amplitude Saturation," Proceedings of the 33rd Youth Academic Annual Conference of Chinese Association of Automation (2018, Nanjing, China), pp. 150-155, Institute of Electrical and Electronics Engineers (IEEE), May 2018.
The definitive version is available at https://doi.org/10.1109/YAC.2018.8406363
33rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2018 (2018: May 18-20, Nanjing, China)
Electrical and Computer Engineering
Intelligent Systems Center
Keywords and Phrases
Feedback control; Linear systems; Optimal control systems; Reinforcement learning; Riccati equations; State feedback; Adaptive optimal control; Algebraic Riccati equations; Amplitude saturation; Discrete time linear systems; Input saturation; Model free; Output regulation; Output regulation problem; Algebra
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 May 2018