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research-article

Online Policy-Iteration based Tracking Control of Four Wheeled Omni-Directional Robots

[+] Author and Article Information
Arash Sheikhlar

Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
arash.sheikhlar@qiau.ac.ir

Ahmad Fakharian

Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
ahmad.fakharian@qiau.ac.ir

1Corresponding author.

ASME doi:10.1115/1.4039287 History: Received February 19, 2017; Revised January 27, 2018

Abstract

In this paper, online policy iteration reinforcement learning algorithm is proposed for motion control of four wheeled omni-directional robots. The algorithm solves the linear quadratic tracking (LQT) problem in an online manner using real-time measurement data of the robot. This property enables the tracking controller to compensate the alterations of dynamics of the robot's model and environment. The online policy iteration based tracking method is employed as low level controller. On the other side, a proportional derivative (PD) scheme is performed as supervisory planning system (high level controller). In this study, the followed paths of online and offline policy iteration algorithms are compared in a rectangular trajectory in presence of slippage drawback and motor heat. Simulation and implementation results of the methods demonstrate the effectiveness of the online algorithm compared to offline one in reducing the command trajectory tracking error and robot's path deviations. The novelty of this paper is proposition of a simple-structure learning based adaptive optimal scheme that tracks the desired path, optimizes the energy consumption and solves the uncertainty problem in omni-directional wheeled robots.

Copyright (c) 2018 by ASME
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