使用神经网络和干扰观察者神经网络的滑动模式来控制人形操纵器的跟踪控制
Yina Wang1, Yanjun Yu2, Ziteng Wang1
1School of Electrical Engineering, Shenyang University of Technology, Shenyang, 110870, China.
Scientific reports
|November 6, 2025
概括
这项研究引入了护理机器人操纵器的强有力的控制方法,增强了对服务茶等任务的轨迹跟踪. 该方法解决了诸如摩擦和不确定性等挑战,提高了机器人护理能力.
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
- 医疗保健中的人工智能
背景情况:
- 带有6DOF操纵器的护理机器人对于老人和残疾人护理至关重要,需要精确的轨迹跟踪来完成诸如饮料供应等任务.
- 挑战包括非线性合,模型不确定性,关节摩擦和外部干扰,降低操纵器控制性能.
研究的目的:
- 开发一种强大的控制方法,以提高护理机器人操纵器的轨迹跟踪精度和稳定性.
- 同时应对多个控制挑战,包括模型不确定性,摩擦和外部干扰.
主要方法:
- 一个强大的控制策略,结合了滑动模式控制 (SMC),辐射基函数神经网络 (RBFNN) 和非线性干扰观察器 (NDO).
- 一个改进的梯度下降驱动逆动力学 (IGDIK) 模块,用于准确的逆动力学解决方案.
- RBFNN用于补偿模型不确定性,NDO用于处理干扰和摩擦.
主要成果:
- 拟议的方法在模拟和实验中显著提高了轨迹跟踪的准确性和稳定性.
- 综合控制方法有效地解决了非线性合,模型不确定性,摩擦和外部干扰.
- 对目标护理机器人操纵器的控制方法有效性的验证.
结论:
- 开发的强大的控制方法提高了护理机器人操纵器在执行复杂的护理任务时的精度和可靠性.
- 这一进步有助于在医疗保健环境中提供更安全,更有效的机器人协助.
- 该研究验证了将SMC,RBFNN和NDO用于复杂的机器人控制问题的协同效益.
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