| Autor: | Kuu-Young Young, Cheng-Peng Kuan |
|---|---|
| Rok vydání: | 1998 |
| Předmět: |
Engineering
Artificial neural network business.industry Mechanical Engineering Control engineering Motion control Robot learning Industrial and Manufacturing Engineering Artificial Intelligence Control and Systems Engineering Control theory Control system Reinforcement learning Robot Electrical and Electronic Engineering Robust control business Software Simulation |
| Zdroj: | Journal of Intelligent and Robotic Systems. 23:165-182 |
| ISSN: | 0921-0296 |
| DOI: | 10.1023/a:1008083631190 |
| Popis: | The complexity in planning and control of robot compliance tasks mainly results from simultaneous control of both position and force and inevitable contact with environments. It is quite difficult to achieve accurate modeling of the interaction between the robot and the environment during contact. In addition, the interaction with the environment varies even for compliance tasks of the same kind. To deal with these phenomena, in this paper, we propose a reinforcement learning and robust control scheme for robot compliance tasks. A reinforcement learning mechanism is used to tackle variations among compliance tasks of the same kind. A robust compliance controller that guarantees system stability in the presence of modeling uncertainties and external disturbances is used to execute control commands sent from the reinforcement learning mechanism. Simulations based on deburring compliance tasks demonstrate the effectiveness of the proposed scheme. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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