逆摆的控制策略:对线性,非线性和人工智能方法的比较分析
Saqib Irfan1, Liangyu Zhao1, Safeer Ullah2
1School of Aerospace Engineering, Beijing Institute of Technology, Beijing, China.
PloS one
|March 7, 2024
概括
控制非线性反转 (IP) 系统是复杂的. 一项比较研究表明,基于滑动模式控制的神经网络 (SMCNN) 提供了卓越的稳定性,优于线性和其他非线性方法.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 非线性动力学是一种非线性动力学.
- 在工程领域的人工智能.
背景情况:
- 倒挂 (IP) 是一个经典的低值系统,具有固有的非线性动力学.
- 为像IP这样的不稳定系统开发有效的控制策略是控制工程的一个重大挑战.
研究的目的:
- 为了提供反转控制系统的概述.
- 比较分析各种控制策略,包括线性,非线性和基于人工智能的方法.
- 评估IP稳定不同控制方法的性能和复杂性权衡.
主要方法:
- 一个全面的文献综述和基于模拟的对抗策略的比较.
- 对线性二次调节器 (LQR),滑动模式控制 (SMC),后退 (BS),模糊逻辑控制器 (FLC) 和基于SMC的神经网络 (SMCNN) 的分析.
- 基于诸如结算时间,超标和稳定状态错误等参数的性能评估.
主要成果:
- 非线性和基于人工智能的方法有效地减轻了反转的摆形的非线性和不稳定性.
- 基于SMC的神经网络 (SMCNN) 控制器表现出比LQR,SMC,FLC和BS更优异的性能.
- 在结算时间,超标和稳定状态错误方面,SMCNN取得了更好的结果.
结论:
- 该SMCNN控制器为反向摆动稳定提供最佳性能,超越传统和其他先进的控制技术.
- 虽然SMCNN提供了卓越的结果,但与更简单的方法相比,它涉及更高的系统复杂性.
- 这项研究强调了神经网络,特别是SMCNN在解决不稳定的系统中复杂的控制挑战方面的潜力.
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