- Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi
- Cilt: 27 Sayı: 6
- Adaptive Control of an Inverted Pendulum by a Reinforcement Learningbased LQR Method
Adaptive Control of an Inverted Pendulum by a Reinforcement Learningbased LQR Method
Authors : Uğur YILDIRAN
Pages : 1311-1321
Doi:10.16984/saufenbilder.1286391
View : 282 | Download : 161
Publication Date : 2023-12-18
Article Type : Research
Abstract :Inverted pendulums constitute one of the popular systems for benchmarking control algorithms. Several methods have been proposed for the control of this system, the majority of which rely on the availability of a mathematical model. However, deriving a mathematical model using physical parameters or system identification techniques requires manual effort. Moreover, the designed controllers may perform poorly if system parameters change. To mitigate these problems, recently, some studies used Reinforcement Learning (RL) based approaches for the control of inverted pendulum systems. Unfortunately, these methods suffer from slow convergence and local minimum problems. Moreover, they may require hyperparameter tuning which complicates the design process significantly. To alleviate these problems, the present study proposes an LQR-based RL method for adaptive balancing control of an inverted pendulum. As shown by numerical experiments, the algorithm stabilizes the system very fast without requiring a mathematical model or extensive hyperparameter tuning. In addition, it can adapt to parametric changes online.Keywords : Reinforcement learning, LQR, Inverted pendulum, Q-learning, adaptive control