標題: Robot control optimization using reinforcement learning
作者: Song, KT
Sun, WY
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: artificial neural network;dynamic control;reinforcement learning;robot control
公開日期: 1-三月-1998
摘要: Conventional robot control schemes are basically model-based methods. However, exact modeling of robot dynamics poses considerable problems and faces various uncertainties in task execution. This paper proposes a reinforcement learning control approach for overcoming such drawbacks. An artificial neural network (ANN) serves as the learning structure, and an applied stochastic real-valued (SRV) unit as the learning method. Initially, force tracking control of a two-link robot arm is simulated to verify the control design. The simulation results confirm that even without information related to the robot dynamic model and environment states, operation rules for simultaneous controlling force and velocity are achievable by repetitive exploration. Hitherto, however, an acceptable performance has demanded many learning iterations and the learning speed proved too slow for practical applications. The approach herein, therefore, improves the tracking performance by combining a conventional controller with a reinforcement learning strategy. Experimental results demonstrate improved trajectory tracking performance of a two-link direct-drive robot manipulator using the proposed method.
URI: http://hdl.handle.net/11536/32779
ISSN: 0921-0296
期刊: JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
Volume: 21
Issue: 3
起始頁: 221
結束頁: 238
顯示於類別:期刊論文


文件中的檔案:

  1. 000072494900002.pdf