用强化学习控制倒立摆，实现从摇起至稳定的一体化控制。
Technical Report on Reinforcement Learning Control on the Lucas-Nülle Inverted Pendulum
- 将强化学习计算分离到外部硬件，通过CAN总线通信。
- 在无模型情况下完成倒立摆的摇起与稳定控制。
- 加入安全算法保障训练过程中的设备安全。
自动控制领域正越来越多地采用源自机器学习的概念，其中强化学习（RL）因其天然适合序列决策且无需系统模型即可应用于最优控制问题而日益重要。为推动控制工程教育，本文针对Lucas-Nülle公司提供的教育型硬件，构建一个可应用的强化学习框架，实现倒立摆的完整控制——包括摇起与稳定两个阶段，并采用统一设计方法。实际学习过程通过将计算任务从实时控制计算机中分离并外置于另一硬件来实现。该分布式架构要求组件间通信，采用CAN总线完成。实验验证了该方案的可行性，并引入保护算法，在试错训练阶段防止设备受到损害。
原文摘要 · Abstract (English)
The discipline of automatic control is making increased use of concepts that originate from the domain of machine learning. Herein, reinforcement learning (RL) takes an elevated role, as it is inherently designed for sequential decision making, and can be applied to optimal control problems without the need for a plant system model. To advance education of control engineers and operators in this field, this contribution targets an RL framework that can be applied to educational hardware provided by the Lucas-Nülle company. Specifically, the goal of inverted pendulum control is pursued by means of RL, including both, swing-up and stabilization within a single holistic design approach. Herein, the actual learning is enabled by separating corresponding computations from the real-time control computer and outsourcing them to a different hardware. This distributed architecture, however, necessitates communication of the involved components, which is realized via CAN bus. The experimental proof of concept is presented with an applied safeguarding algorithm that prevents the plant from being operated harmfully during the trial-and-error training phase.
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