Optimal control of a two‐wheeled self‐balancing robot by reinforcement learning

Summary This article concerns optimal control of the linear motion, tilt motion, and yaw motion of a two‐wheeled self‐balancing robot (TWSBR). Traditional optimal control methods for the TWSBR usually require a precise model of the system, and other control methods exist that achieve stabilization i...

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Vydané v:	International journal of robust and nonlinear control Ročník 31; číslo 6; s. 1885 - 1904
Hlavní autori:	Guo, Linyuan, Rizvi, Syed Ali Asad, Lin, Zongli
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Bognor Regis Wiley Subscription Services, Inc 01.04.2021
Predmet:	Algorithms Balancing Control methods Control systems Decoupling Machine learning Mathematical models Optimal control Output feedback Parameter uncertainty Q‐learning reinforcement learning Riccati equation Robot control robustness State feedback two‐wheeled self‐balancing robot Yaw
ISSN:	1049-8923, 1099-1239
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Abstract	Summary This article concerns optimal control of the linear motion, tilt motion, and yaw motion of a two‐wheeled self‐balancing robot (TWSBR). Traditional optimal control methods for the TWSBR usually require a precise model of the system, and other control methods exist that achieve stabilization in the face of parameter uncertainties. In practical applications, it is often desirable to realize optimal control in the absence of the precise knowledge of the system parameters. This article proposes to use a new feedback‐based reinforcement learning method to solve the linear quadratic regulation (LQR) control problem for the TWSBR. The proposed control scheme is completely online and does not require any knowledge of the system parameters. The proposed input decoupling mechanism and pre‐feedback law overcome the commonly encountered computational difficulties in implementing the learning algorithms. Both state feedback optimal control and output feedback optimal control are presented. Numerical simulation shows that the proposed optimal control scheme is capable of stabilizing the system and converging to the LQR solution obtained through solving the algebraic Riccati equation.
AbstractList	This article concerns optimal control of the linear motion, tilt motion, and yaw motion of a two‐wheeled self‐balancing robot (TWSBR). Traditional optimal control methods for the TWSBR usually require a precise model of the system, and other control methods exist that achieve stabilization in the face of parameter uncertainties. In practical applications, it is often desirable to realize optimal control in the absence of the precise knowledge of the system parameters. This article proposes to use a new feedback‐based reinforcement learning method to solve the linear quadratic regulation (LQR) control problem for the TWSBR. The proposed control scheme is completely online and does not require any knowledge of the system parameters. The proposed input decoupling mechanism and pre‐feedback law overcome the commonly encountered computational difficulties in implementing the learning algorithms. Both state feedback optimal control and output feedback optimal control are presented. Numerical simulation shows that the proposed optimal control scheme is capable of stabilizing the system and converging to the LQR solution obtained through solving the algebraic Riccati equation. Summary This article concerns optimal control of the linear motion, tilt motion, and yaw motion of a two‐wheeled self‐balancing robot (TWSBR). Traditional optimal control methods for the TWSBR usually require a precise model of the system, and other control methods exist that achieve stabilization in the face of parameter uncertainties. In practical applications, it is often desirable to realize optimal control in the absence of the precise knowledge of the system parameters. This article proposes to use a new feedback‐based reinforcement learning method to solve the linear quadratic regulation (LQR) control problem for the TWSBR. The proposed control scheme is completely online and does not require any knowledge of the system parameters. The proposed input decoupling mechanism and pre‐feedback law overcome the commonly encountered computational difficulties in implementing the learning algorithms. Both state feedback optimal control and output feedback optimal control are presented. Numerical simulation shows that the proposed optimal control scheme is capable of stabilizing the system and converging to the LQR solution obtained through solving the algebraic Riccati equation.
Author	Guo, Linyuan Rizvi, Syed Ali Asad Lin, Zongli
Author_xml	– sequence: 1 givenname: Linyuan surname: Guo fullname: Guo, Linyuan organization: University of Virginia – sequence: 2 givenname: Syed Ali Asad orcidid: 0000-0003-1412-8841 surname: Rizvi fullname: Rizvi, Syed Ali Asad organization: University of Virginia – sequence: 3 givenname: Zongli orcidid: 0000-0003-1589-1443 surname: Lin fullname: Lin, Zongli email: zl5y@virginia.edu organization: University of Virginia
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Snippet	Summary This article concerns optimal control of the linear motion, tilt motion, and yaw motion of a two‐wheeled self‐balancing robot (TWSBR). Traditional... This article concerns optimal control of the linear motion, tilt motion, and yaw motion of a two‐wheeled self‐balancing robot (TWSBR). Traditional optimal...
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SubjectTerms	Algorithms Balancing Control methods Control systems Decoupling Machine learning Mathematical models Optimal control Output feedback Parameter uncertainty Q‐learning reinforcement learning Riccati equation Robot control robustness State feedback two‐wheeled self‐balancing robot Yaw
Title	Optimal control of a two‐wheeled self‐balancing robot by reinforcement learning
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