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arXiv 2310.04436eess.SYcs.SY
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基于强化学习LQR方法的倒立摆自适应控制

Adaptive Control of an Inverted Pendulum by a Reinforcement Learning-based LQR Method

Ugur Yildiran

AI总结:

本研究提出一种基于LQR的强化学习方法,用于倒立摆的自适应平衡控制,无需数学模型和大量超参数调优,能快速稳定系统并在线适应参数变化。

摘要:

倒立摆是基准测试控制算法的流行系统之一。已有多种用于该系统控制的方法被提出,其中大多数依赖于数学模型的可用性。然而,使用物理参数或系统辨识技术推导数学模型需要人工努力。此外,如果系统参数发生变化,所设计的控制器可能表现不佳。为了缓解这些问题,最近一些研究使用基于强化学习(RL)的方法来控制倒立摆系统。不幸的是,这些方法存在收敛缓慢和局部极小值问题。此外,它们可能需要超参数调优,这显著使设计过程复杂化。为了减轻这些问题,本研究提出了一种基于LQR的RL方法,用于倒立摆的自适应平衡控制。数值实验表明,该算法能非常快速地稳定系统,而无需数学模型或大量的超参数调优。此外,它能够在线适应参数变化。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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