复合自适应控制器的设计,具有多边自适应学习机制
Chao Niu1, Yumei Yao2, Zengliang Zhang3
1School of Electrical Engineering, Henan Mechanical and Electrical Vocational College, Zhengzhou, 451191, China. epsilonlh2@qq.com.
Scientific reports
|November 26, 2025
概括
本研究引入了一种合作适应性学习机制,以改善不确定性非线性系统的轨迹跟踪. 这种新方法提高了参数的趋同性,并且性能优于传统的自适应控制器.
科学领域:
- 控制系统工程 控制系统工程
- 非线性动力学是一种非线性动力学.
- 机器学习 机器学习
背景情况:
- 相似的非线性系统在轨迹跟踪方面存在挑战,原因是参数不确定性.
- 参数收往往很难实现,特别是在间隔激发条件下.
- 现有的自适应控制方法可能会与复杂的不确定性和振荡作斗争.
研究的目的:
- 开发一种新的多边合作适应性学习机制,以加强轨迹跟踪.
- 为了提高相关非线性系统中的参数收率和准确性.
- 为了解决参数不确定性,并抑制自适应控制中的振荡.
主要方法:
- 提出一个复合学习自适应控制器,利用多边学习输出来估计系统的不确定性.
- 适应性更新法是基于参数估计和近似误差而设计的.
- 使用和函数来限制重量变化速率,减轻振荡.
主要成果:
- 拟议的机制显著提高了轨迹跟踪性能.
- 即使在间隔激发下,也可以证明改善了参数收.
- 在一个倒置的摆形系统上的实验验证显示,与传统控制器相比,其性能优越.
结论:
- 多边合作自适应学习机制为不确定非线性系统的轨迹跟踪提供了强大的解决方案.
- 控制器有效地处理参数不确定性并减少振荡.
- 这种方法为先进的自适应控制应用提供了一个有希望的方向.
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