| Title |
Sim-to-Real Reinforcement Learning Techniques for Double Inverted Pendulum Control with Recovery Property |
| Authors |
이태건(Taegun Lee) ; 주도윤(Doyoon Ju) ; 이영삼(Young Sam Lee) |
| DOI |
https://doi.org/10.5370/KIEE.2023.72.12.1705 |
| Keywords |
Reinforcement Learning; Double Inverted Pendulum; Sim-to-Real Learning; Recovery Property |
| Abstract |
In recent years with the rapid advancement of artificial intelligence, there has been extensive research to address control problems, which was previously unsolvable with traditional control techniques, using reinforcement learning-based controllers. This paper discusses a challenge in controlling a double inverted pendulum system. With the commonly used 2-DOF control technique, once the swing-up control is performed and a strong disturbance is applied, the system becomes uncontrollable and fails to perform another swing-up. However, the reinforcement learning-based controller proposed in this paper overcomes this limitation using the Sim-to-Real learning technique. To ensure successful application of Sim-to-Real learning, this paper proposes a design method for the real-world system that minimizes the reality gap, a chronic issue with the Sim-to-Real technique. Utilizing these techniques, we introduce a characteristic termed 'recovery property' denoting the ability to recover from strong disturbances, a feature difficult to achieve with traditional control methods. We design a controller with this characteristic and validate its successful operation in a real-world system. |