视觉预测控制机器人与RBF-EKF合状态干扰估计和以任务为导向的K-意味着集群
Peng Ji1, Hongyu Wang1, Weina Ren2
1School of Information and Automation Engineering, Shandong Key Laboratory of Key Technologies and Systems for Humanoid Robots, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究通过使用视觉预测控制框架提高了机器人视觉伺服 (IBVS) 的稳定性. 该方法集成了辐射基函数 (RBF) 网络和扩展卡尔曼波器 (EKF) 以实现可靠的控制和干扰估计.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 计算机视觉 计算机视觉
背景情况:
- 基于图像的视觉伺服 (IBVS) 系统面临着来自噪音,建模错误和干扰的不稳定性挑战.
- 现有的方法往往难以同时有效地解决这些复杂的问题.
研究的目的:
- 开发一个强大的视觉预测控制 (VPC) 框架,以提高IBVS的稳定性和跟踪精度.
- 整合先进的估计技术,以改善不确定性下的系统性能.
主要方法:
- 一个反线性化模型预测控制 (MPC) 法律旨在管理非线性和约束.
- 采用扩展卡尔曼波器 (EKF) 来抑制噪音和辐射基函数 (RBF) 网络来学习干扰的结合状态干扰估计机制.
- 以任务为导向的K-means集群被用来优化RBF中心选择以提高效率.
主要成果:
- 利亚普诺夫分析证实了拟议系统的统一最终约束 (UUB) 稳定性.
- 与传统方法相比,模拟显示估计误差显著减少,跟踪精度提高.
- 综合方法证明了机器人视觉服务的卓越稳定性和实用性.
结论:
- 拟议的视觉预测控制框架有效地提高了IBVS系统的稳定性和性能.
- 控制和估计策略的深度协调为现实世界机器人应用提供了实际的解决方案.
- 这项研究有助于通过提高稳定性和准确性来推进机器人控制领域.
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