Reinforcement Learning for Cart Pole Inverted Pendulum System

Surriani, Atikah; Wahyunggoro, Oyas; Cahyadi, Adha Imam

doi:10.1109/ieacon51066.2021.9654440

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…More recent results are established on the data-driven technique of learning-based control. The algorithm of Deep Reinforcement Learning (DRL) allows for online extraction of the system model [9,10] or real-time identification of the optimal parameters [11,12] from the collected data. Therefore, the DRL method has significant advantages in providing an adaptive controller by which seeking optimal solutions and improving the robustness to time-varying disturbances are possible.…”

Section: Introductionmentioning

confidence: 99%

Robust and Exponential Stabilization of a Cart–Pendulum System via Geometric PID Control

Zhang,

Fang,

Fei

et al. 2024

Symmetry

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This paper addresses the robust stabilization problem of a cart–pole system. The controlled dynamics of this interconnected system are deduced by following the analytic framework of Lagrangian mechanics, and the residual terms are formulated as a bias depending on the angle and angular velocity. A geometric definition of Proportional–Integral–Derivative (PID) control algorithm is proposed, and a Lyapunov function is explicitly constructed through two stages of variable change. Local exponential stability of the stable equilibrium is proved, and a criterion for parameter tuning is provided by ensuring an exponential decrease in the Lyapunov function. Enlarging the control parameters to infinity allows for the extension of attraction region almost to the half circle. The effectiveness of geometric PID controller and the local exponential stability of the resulting close system are verified by simulating a numerical example.

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Section: Introductionmentioning

confidence: 99%

Robust and Exponential Stabilization of a Cart–Pendulum System via Geometric PID Control

Zhang,

Fang,

Fei

et al. 2024

Symmetry

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“…Many numerical studies have implemented an inverted pendulum virtual environment as a benchmark to test RL algorithms [15][16][17][18][19][20][21][22], but to our knowledge, there is no study that provides successful RL implementations in experiments. First, except for a few studies that have discussed non ideal systems [16,17], most of these numerical implementations discard the effects associated to realistic (and thus more complex) control methods: in experiments, the control of the cart is subject to delay, hysteresis, biases and noise that can significantly alter the learning process.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Israilov

Sánchez-Rodríguez

et al. 2023

PLoS ONE

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Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical and less intuitive for students than classical model-based methods. This is particularly the case for reinforcement learning, an approach that does not require any mathematical model to drive a system inside an unknown environment. This lack of intuition can be an obstacle to design experiments and implement this approach. Reversely there is a need to gain experience and intuition from experiments. In this article, we propose a general framework to reproduce successful experiments and simulations based on the inverted pendulum, a classic problem often used as a benchmark to evaluate control strategies. Two algorithms (basic Q-Learning and Deep Q-Networks (DQN)) are introduced, both in experiments and in simulation with a virtual environment, to give a comprehensive understanding of the approach and discuss its implementation on real systems. In experiments, we show that learning over a few hours is enough to control the pendulum with high accuracy. Simulations provide insights about the effect of each physical parameter and tests the feasibility and robustness of the approach.

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“…Israilov et al [29] used two model-free RL algorithms to control targets and proposed a general framework to reproduce successful experiments and simulations based on the inverted pendulum. In addition, there are still many studies of this kind, for example [30][31][32]. However, the results of these studies focused more on reducing the time to reach equilibrium, without fully considering uncertainty in the system.…”

Section: Introductionmentioning

confidence: 99%

Robust Control of An Inverted Pendulum System Based on Policy Iteration in Reinforcement Learning

Ma,

Xu,

Huang

et al. 2023

Applied Sciences

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This paper is primarily focused on the robust control of an inverted pendulum system based on policy iteration in reinforcement learning. First, a mathematical model of the single inverted pendulum system is established through a force analysis of the pendulum and trolley. Second, based on the theory of robust optimal control, the robust control of the uncertain linear inverted pendulum system is transformed into an optimal control problem with an appropriate performance index. Moreover, for the uncertain linear and nonlinear systems, two reinforcement-learning control algorithms are proposed using the policy iteration method. Finally, two numerical examples are provided to validate the reinforcement learning algorithms for the robust control of the inverted pendulum systems.

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Reinforcement Learning for Cart Pole Inverted Pendulum System

Cited by 8 publications

References 8 publications

Robust and Exponential Stabilization of a Cart–Pendulum System via Geometric PID Control

Robust and Exponential Stabilization of a Cart–Pendulum System via Geometric PID Control

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Robust Control of An Inverted Pendulum System Based on Policy Iteration in Reinforcement Learning

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