Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems

Adedapo Odekunle; Weinan Gao; Yebin Wang

doi:10.1109/JAS.2019.1911678

Volume 6 Issue 5

Sep. 2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(5): 1108-1115

Adedapo Odekunle, Weinan Gao and Yebin Wang, "Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1108-1115, Sept. 2019. doi: 10.1109/JAS.2019.1911678

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Adedapo Odekunle, Weinan Gao and Yebin Wang, "Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1108-1115, Sept. 2019. doi: 10.1109/JAS.2019.1911678

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Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems

doi: 10.1109/JAS.2019.1911678

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Author Bio:
Adedapo Odekunle (M’18) received the B.Sc. degree in computer engineering from University of Lagos, Lagos, Nigeria in 2010. He is currently enrolled for his M.S degree and also working as a Research Assistant in the Department of Electrical and Computer Engineering, Georgia Southern University. His research interests are reinforcement learning, adaptive dynamic programming, optimal control, adaptive control, connected and autonomous vehicles and output regulation theory

Weinan Gao (M’18) received the B.Sc. degree in automation from Northeastern University, Shenyang, China, in 2011, the M.Sc. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2013, and the Ph.D. degree in electrical engineering from New York University, in 2017. He is currently an Assistant Professor of Electrical and Computer Engineering with Allen E. Paulson College of Engineering and Computing, Georgia Southern University. His research interests include reinforcement learning, adaptive dynamic programming (ADP), optimal control, cooperative adaptive cruise control (CACC), intelligent transportation systems, sampled-data control systems, and output regulation theory. He is the recipient of the best paper award in IEEE International Conference on Real-time Computing and Robotics (RCAR) in 2018, and David Goodman Research Award at New York University in 2019.Dr. Gao serves as an Associate Editor of Neurocomputing and a member of Editorial Board of Neural Computing and Applications

Yebin Wang (M’09–SM’16) received the B.Eng. degree in mechatronics engineering from Zhejiang University, Hangzhou, China, in 1997, M.Eng. degree in control theory and control engineering from Tsinghua University, Beijing, China, in 2001, and the Ph.D. in electrical engineering from the University of Alberta, Edmonton, AB, Canada, in 2008. From 2001 to 2003, he was a Software Engineer, Project Manager, and R&D Manager in automation industries.Since 2009, he has been with Mitsubishi Electric Research Laboratories in Cambridge, MA, USA, and is now a Senior Principal Research Scientist. His research interests include nonlinear control and estimation, optimal control, adaptive and learning systems, and their applications including mechatronic systems
Corresponding author: A. Odekunle and W. Gao are with the Department of Electrical and Computer Engineering, Allen. E. Paulson College of Engineering and Computing, Georgia Southern University, Statesboro, GA 30460 USA (e-mail: ao02108@georgiasouthern.edu; wgao@georgiasouthern.edu)
Received Date: 2019-02-20
Revised Date: 2019-04-16
Accepted Date: 2019-05-30

Available Online: 2019-06-22

Abstract

Abstract

In this paper, a data-driven control approach is developed by reinforcement learning (RL) to solve the global robust optimal output regulation problem (GROORP) of partially linear systems with both static uncertainties and nonlinear dynamic uncertainties. By developing a proper feedforward controller, the GROORP is converted into a global robust optimal stabilization problem. A robust optimal feedback controller is designed which is able to stabilize the system in the presence of dynamic uncertainties. The closed-loop system is ensured to be input-to-output stable regarding the static uncertainty as the external input. This robust optimal controller is numerically approximated via RL. Nonlinear small-gain theory is applied to show the input-to-output stability for the closed-loop system and thus solves the original GROORP. Simulation results validates the efficacy of the proposed methodology.
- Robust control,
- output regulation,
- reinforcement learning,
- small-gain theory

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References

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Highlights

A novel data-driven control approach is proposed to solve global robust optimal regulation problems.
Considering the presence Both static and dynamic uncertainties in the partially linear systems, a robust optimal controller is developed in terms of reinforcement learning and small gain theory.
It is rigorously ensured that the system in closedloop with the developed controller is robust to the uncertainties.

Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems

doi: 10.1109/JAS.2019.1911678

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通讯作者: 陈斌, bchen63@163.com

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