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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Published in:International journal of robust and nonlinear control Vol. 31; no. 6; pp. 1885 - 1904
Main Authors: Guo, Linyuan, Rizvi, Syed Ali Asad, Lin, Zongli
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 01.04.2021
Subjects:
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
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  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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