基于同步强化学习的联合控制算法,用于自平衡自行车机器人的自行车机器人
Lei Guo1, Hongyu Lin1, Jiale Jiang1
1School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
ISA transactions
|November 25, 2023
概括
本研究提出了一种新的控制策略,用于平衡自行车机器人,使用反线性化和演员关键神经网络. 该方法确保稳定的机器人运动和最佳的控制政策,通过模拟和实验验证.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 自行车机器人平衡控制由于固有的非线性动力学而具有挑战性.
- 现有的方法通常依赖于机械调节器或外部支持.
- 自主直线运动需要强大的平衡维护.
研究的目的:
- 为没有外部稳定器的自行车机器人开发平衡控制策略.
- 为了建立机器人的非精细非线性动力学的模型.
- 为了确保在直线运动期间稳定和最佳的控制.
主要方法:
- 开发了一个基于时刻平衡的输入非非非非非线性动力学模型.
- 将该系统转换为使用等效控制的亲缘非线性系统.
- 设计了一个反线性化控制器和同步策略代算法.
- 利用关键演员神经网络 (NN) 来控制法律的实施.
- 拟议的NN重量调整法律,通过利亚普诺夫分析确保稳定性.
主要成果:
- 在近似直线运动中实现了自行车机器人的平衡控制.
- 演示了非非线性系统的转化为非非线性系统的转化.
- 保证闭环稳定性和NN重量趋同.
- 验证了控制政策的最佳性.
- 通过模拟和现实世界的实验验验证算法的有效性.
结论:
- 提出的基于演员-关键的反线性化控制器有效地管理自行车机器人平衡.
- 该方法确保了系统稳定性和最佳控制政策.
- 该方法得到了验证,并适用于现实世界的机器人系统.
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