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Optimal control of a two‐wheeled self‐balancing robot by reinforcement learning
Author(s) -
Guo Linyuan,
Rizvi Syed Ali Asad,
Lin Zongli
Publication year - 2021
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5058
Subject(s) - reinforcement learning , control theory (sociology) , optimal control , linear quadratic regulator , algebraic riccati equation , decoupling (probability) , computer science , linear quadratic gaussian control , riccati equation , motion control , control (management) , robot , control engineering , mathematical optimization , mathematics , engineering , artificial intelligence , differential equation , mathematical analysis
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.