Fusion of reinforcement learning and analytical controller design theory for a class of nonlinear

Fuse, Ikuya; Yokoyama, Makoto

doi:10.1299/jsmedmc.2020.548

2020

DOI: 10.1299/jsmedmc.2020.548

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Fusion of reinforcement learning and analytical controller design theory for a class of nonlinear

Ikuya Fuse

Makoto Yokoyama

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2021

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Minimum-time feedback control of a rotary pendulum using deep learning

Sago

Ueyama

Harada

2021

Transactions of the JSME (In Japanese)

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This paper investigates the utility of nonlinear optimal feedback control for a rotary pendulum based on deep learning. The neural network trained with supervised learning using solutions of the minimum-time optimal control problem provides the feedback controller, which generates control variables by current state variables. The utility of the proposed idea is verified by the numerical simulation and experiment considering the pendulum angular velocity constraint. Results demonstrate the feasibility of commanding the rotary pendulum with the proposed nonlinear feedback controller.

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Minimum-time feedback control of a rotary pendulum using deep learning

Sago

Ueyama

Harada

2021

Transactions of the JSME (In Japanese)

View full text Add to dashboard Cite

show abstract

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Fusion of reinforcement learning and analytical controller design theory for a class of nonlinear

Cited by 1 publication

References 0 publications

Minimum-time feedback control of a rotary pendulum using deep learning

Minimum-time feedback control of a rotary pendulum using deep learning

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