Adaptive Control of an Inverted Pendulum by a Reinforcement Learning-based LQR Method
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.
Code (0)
등록된 구현이 없습니다.
Tasks
BenchmarkingReinforcement Learning (RL)Similar Papers 제목 키워드 기반
A Novel Recurrent Adaptive Backstepping Optimal Control Strategy for a Single Inverted Pendulum System
In this paper, a novel recurrent adaptive backstepping optimal control strategy for a single inverted pendulum system is studied. By this method, an inverted pendulum is stabilized using projection recurrent neural netwo…
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum
Using the policy gradient algorithm, we train a single-hidden-layer neural network to balance a physically accurate simulation of a single inverted pendulum. The trained weights and biases can then be transferred to a ph…
reinforcement-learningReinforcement Learning (RL)A Q-learning approach to the continuous control problem of robot inverted pendulum balancing
This study evaluates the application of a discrete action space reinforcement learning method (Q-learning) to the continuous control problem of robot inverted pendulum balancing. To speed up the learning process and to o…
continuous-controlContinuous ControlQ-Learningreinforcement-learning+1A Comparison of Reward Functions in Q-Learning Applied to a Cart Position Problem
Growing advancements in reinforcement learning has led to advancements in control theory. Reinforcement learning has effectively solved the inverted pendulum problem and more recently the double inverted pendulum problem…
PositionQ-Learningreinforcement-learningReinforcement Learning+1Designing and Analyzing the PID and Fuzzy Control System for an Inverted Pendulum
The inverted pendulum is a non-linear unbalanced system that needs to be controlled using motors to achieve stability and equilibrium. The inverted pendulum is constructed with Lego and using the Lego Mindstorm NXT, whic…