Optimal control of a two‐wheeled self‐balancing robot by reinforcement learning. (27th July 2020)

• Record Type: Journal Article
• Authors: Guo, Linyuan; Rizvi, Syed Ali Asad; Lin, Zongli
• Other Names: Wan Yan guestEditor.; Yang Tao guestEditor.; Yuan Ye guestEditor.; Lewis Frank L. guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 6(2021)
• Page Start: 1885
• Page End: 1904
• Publication Date: 2020-07-27
• Subjects: optimal control -- Q‐learning -- reinforcement learning -- robustness -- two‐wheeled self‐balancing robot; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5058
• Languages: English
• ISSNs: 1049-8923

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.