在未建模的不确定性条件下,通过神经网络辅助的基于模型的液压操纵器的优化运动控制
Manzhi Qi1, Yangxiu Xia1, Shizhao Zhou2
1State Key Laboratory of Ocean Sensing, Zhejiang University, Hangzhou, 310058, China; ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China; Ocean College, Zhejiang University, Zhoushan, 316021, China.
ISA transactions
|January 7, 2026
概括
本研究介绍了用于液压操纵器的神经网络控制方法,通过处理系统不确定性来提高精度. 这种方法可以提高重型任务的控制性能,而不需要精确的模型.
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:
- 多度自由度 (多DoF) 液压操纵器为重型应用提供高功率密度.
- 由于固有的非线性和不确定的动态,对这些系统的精确控制具有挑战性.
- 传统的基于模型的控制需要准确的模型,复杂的设计和增加计算.
研究的目的:
- 为多DoF液压操纵器开发一种适应性强大的控制策略.
- 通过补偿未建模的动态来减少对精确动态模型的依赖.
- 在存在系统不确定性的情况下,提高控制准确性和稳定性.
主要方法:
- 使用辐射基函数神经网络 (RBFNNs) 来近似不确定的系统动态.
- 使用K-means++算法优化RBFNN结构,以提高准确性和效率.
- 采用所需信号而不是测量信号来降低噪声灵敏度.
主要成果:
- 保证闭环系统的稳定性和非对称的跟踪性能.
- 与传统方法相比,在控制性能方面取得了显著的改进.
- 通过对液压操纵器的实验结果验证了有效性.
结论:
- 拟议的神经网络辅助的自适应性强有力的控制有效地解决了液压操纵器控制方面的挑战.
- 该方法提高了精度和稳定性,同时减少了对模型的依赖.
- 这种方法为重型机器人应用中的精确控制提供了有希望的解决方案.
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