A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 6 Issue 3
May  2019

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Ding Wang and Xiangnan Zhong, "Advanced Policy Learning Near-Optimal Regulation," IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 743-749, May 2019. doi: 10.1109/JAS.2019.1911489
Citation: Ding Wang and Xiangnan Zhong, "Advanced Policy Learning Near-Optimal Regulation," IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 743-749, May 2019. doi: 10.1109/JAS.2019.1911489

Advanced Policy Learning Near-Optimal Regulation

doi: 10.1109/JAS.2019.1911489
Funds:

the National Natural Science Foundation of China 61773373

the National Natural Science Foundation of China U1501251

the National Natural Science Foundation of China 61533017

More Information
  • Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynamics is developed with the help of policy learning. For addressing the nonaffine nonlinearity, a pre-compensator is constructed, so that the augmented system can be formulated as affine-like form. Different cost functions are defined for original and transformed controlled plants and then their relationship is analyzed in detail. Additionally, an adaptive critic algorithm involving stability guarantee is employed to solve the augmented optimal control problem. At last, several case studies are conducted for verifying the stability, robustness, and optimality of a torsional pendulum plant with suitable cost.

     

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