康复外骨扭矩控制基于PSO-RBFNN优化PSO-RBFNN优化
Jiayi Li1, Yuanzheng Tai1, Fanwei Meng1
1School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China.
PloS one
|August 8, 2023
概括
本研究介绍了一种使用粒子群优化 (PSO) 和RBF神经网络 (RBFNN) 的优化外骨控制方法. 改进后的系统表现出更好的速度,稳定性,准确性和抗干扰性,以实现更安全的患者康复.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物医学工程 生物医学工程
- 控制系统 控制系统
背景情况:
- 外骨架在医疗康复中至关重要,但在控制精度方面面临挑战.
- 不准确的控制可能导致患者事故,强调需要提高性能.
- 非线性动力学和外部干扰使外骨控制复杂化.
研究的目的:
- 增强外骨扭矩控制,使患者康复更安全,更有效.
- 为了解决控制非线性外骨模型的困难.
- 为了减轻外骨使用时遇到的干扰问题.
主要方法:
- 开发了一种结合粒子群优化 (PSO) 和辐射基函数神经网络 (RBFNN) 的新型控制方法.
- 应用了PSO-RBFNN方法来优化非线性外骨架关节的扭矩控制.
- 该研究的重点是分析运动轨迹和控制性能.
主要成果:
- 在PSO-RBFNN优化的外骨展现出更快的控制速度.
- 观察到增强的稳定性,提高的准确性和更强的抗干扰能力.
- 优化的控制有效地解决了非线性和外部干扰.
结论:
- 在PSO-RBFNN方法显著改善外骨控制性能.
- 这种优化提高了医疗康复应用中的安全性和有效性.
- 这种方法提供了一个强大的解决方案,用于控制外部干扰的非线性外骨.
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