机器人手臂的运动控制策略使用深层级联排功能增强贝叶斯广泛学习系统,具有运动约束
Jiyong Zhou1, Guoyu Zuo1, Xiang Li2
1School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computing Intelligence and Intelligent Systems, Beijing 100124, China.
ISA transactions
|March 14, 2025
概括
本研究介绍了一种新的机器人手臂运动控制策略,即动作受约束的深层级别贝叶斯广义学习系统 (MC-DCBLS),以提高准确性和结合物理限制. 新方法显著减少了位置跟踪错误,使机器人手臂运动更加精确.
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:
- 使用广泛学习系统的机器人手臂现有的智能控制策略往往缺乏准确性,并且不考虑关节运动限制.
- 这些限制可能会对机器人系统的整体控制性能和安全产生负面影响.
研究的目的:
- 提出先进的机器人手臂运动控制策略,提高准确性,并考虑物理关节运动限制.
- 为了提高机器人手臂控制系统的稳定性和可靠性.
主要方法:
- 开发一个深层级级增强特征的贝叶斯广义学习系统 (DCBBLS),以简化建模和提高控制精度.
- 整合了一种运动约束机制,以防止机器人手臂运动超过物理限制.
- 应用Lyapunov理论,以确保控制战略网络参数的稳定性.
主要成果:
- 拟议的MC-DCBLS战略在机器人手臂运动控制精度方面取得了显著的改进.
- 位置跟踪根平均平方误差 (RMSE) 减少到0.038rad.
- 与现有控制技术相比,这代表了61.26%的错误减少.
结论:
- 该MC-DCBLS战略有效地解决了机器人手臂控制中的精度和运动限制挑战.
- 该方法确保机器人手臂在物理界限内的稳定和精确的运动.
- 通过模拟和实验验证,该战略为智能机器人控制提供了实质性的进步.
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