| 侯夕欢,马壮,袁娜.强化学习在小车二阶倒立摆平衡控制中的应用[J].唐山学院学报,2026,39(3):24-30 |
| 强化学习在小车二阶倒立摆平衡控制中的应用 |
| Application of Reinforcement Learning for Balance Control of Second-Order Inverted Pendulum |
| 投稿时间：2025-07-29 |
| DOI：10.16160/j.cnki.tsxyxb.2026.03.005 |
| 中文关键词: 强化学习 小车二阶倒立摆 DQN |
| 英文关键词: reinforcement learning second-order inverted pendulum DQN |
| 基金项目:唐山市基础研究科技计划项目(25130229B);唐山学院博士创新基金项目(BC202213) |
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| 中文摘要: |
| 文章研究了强化学习在小车二阶倒立摆平衡控制中的应用。首先对小车二阶倒立摆进行建模和动力学分析;其次针对小车二阶倒立摆的平衡控制设计了基于DQN(Deep Q-Network)算法的智能体。为使控制效果更直观,基于Matlab/Simulink中的Simscape Multibody模块搭建了二阶倒立摆物理仿真模型。仿真结果表明,基于DQN算法的智能体可以较好地控制小车二阶倒立摆的平衡,由此证明了DQN算法在二阶倒立摆稳定控制中的有效性。 |
| 英文摘要: |
| This paper studies the application of reinforcement learning in the balance control of a second-order inverted pendulum. Firstly, modeling and dynamic analysis of the second-order inverted pendulum are conducted. Then, an intelligent agent based on the Deep Q-Network (DQN) algorithm is designed for its balance control. To make the control effect more intuitive, a physical simulation model of the pendulum is built using the Simscape Multibody module in Matlab/Simulink. The simulation results show that the agent based on the DQN algorithm can effectively control the balance of the second-order inverted pendulum, proving the effectiveness of the DQN algorithm in the stable control of the pendulum. |
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