基于自适应RBF神经网络的工业操纵器的跟踪控制与局部模型近似
Guirong Han1,2, Cai Huang1, Wei Xiao1
1School of Mechanical & Electrical Engineering, Wuhan Institute of Technology, Wuhan, 430205, Hubei, China.
Scientific reports
|January 9, 2026
概括
本研究介绍了一种用于工业机器人的新型自适应控制算法,提高了不需要确切模型的精度和适应性. 该方法确保了稳定性,并有效地补偿了使用RBF神经网络和粒子群优化的系统不确定性.
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:
- 工业机器人需要精确的控制来完成复杂的任务.
- 现有的控制方法通常依赖于准确的数学模型,这些模型很难用于现实世界的系统.
- 系统的非线性和不确定性对实现高精度轨迹跟踪构成重大挑战.
研究的目的:
- 为工业机器人开发一个Lyapunov稳定性保证的局部模型自适应控制算法.
- 为了实现实时学习和补偿系统非线性和不确定性,没有一个确切的植物模型.
- 为了实现机器人操纵器的高精度轨迹跟踪控制.
主要方法:
- 提出了一个局部模型自适应的辐射基函数 (RBF) 神经网络控制算法.
- 实施了适应性控制法,用于在线调整神经网络参数.
- 利用粒子群集优化 (PSO) 来优化RBF基础宽度参数.
- 使用MATLAB Simscape和ADAMS共仿真中的ABB IRB1600工业机器人验证了算法.
主要成果:
- 证明了有效的实时轨迹跟踪控制.
- 与传统方法相比,大大减少了跟踪错误.
- 展示了控制系统的增强强性和适应性.
- 在利亚普诺夫框架内保持保证的稳定.
结论:
- 拟议的自适应RBF神经网络控制算法是有效的工业机器人轨迹跟踪.
- 该算法成功地在实时中补偿了系统的不确定性和非线性.
- 该方法为高精度机器人控制提供了强大,稳定和可适应的解决方案.
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