具有输入输出限制的不确定的机器人系统的自适应性基于安全的跟踪控制:基于神经网络的增强高阶控制屏障功能方法
IEEE transactions on cybernetics
|July 9, 2025
概括
本研究介绍了一种基于神经网络的新型增强高阶控制屏障功能 (NN-AHoCBF),用于不确定的机器人系统. 该方法确保了安全的轨迹跟踪,尽管扭矩和位置的限制.
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:
- 机器人系统经常面临不确定的动力学,有限的控制扭矩和关节位置限制的挑战.
- 确保安全运行,同时实现精确的轨迹跟踪对于实际的机器人应用至关重要.
研究的目的:
- 为不确定的机器人系统开发一种新的控制策略,以解决输入-输出限制并确保安全.
- 在存在系统不确定性和物理限制的情况下,提高轨迹跟踪性能.
主要方法:
- 建议基于神经网络的增强高阶控制屏障函数 (NN-AHoCBF) 来估计和补偿系统的不确定性.
- 神经网络近似误差和权重的自适应边界被纳入控制屏障函数的高阶时间导数中.
- 辅助系统旨在调整时间变化的功能,在NN-AHoCBF框架内放松控制输入约束.
- 基于安全的自适应性跟踪控制方法是在二次编程 (QP) 框架内制定的.
主要成果:
- 该NN-AHoCBF方法有效估计不确定性,并适应系统动态.
- 拟议的控制策略同时满足输入-输出约束,确保系统安全.
- 控制器表现出强大的性能和准确的轨迹跟踪能力.
- 在双DOF机器人操纵器上的模拟验证了控制器的有效性.
结论:
- 开发的NN-AHoCBF为有约束的不确定机器人系统的轨迹跟踪控制提供了有效的解决方案.
- 基于自适应性安全的方法提高了稳定性和跟踪精度,同时保证了系统安全.
- 这种方法为面对复杂操作条件的先进机器人控制应用提供了有前途的方向.
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