使用数据驱动的,无模型的自适应滑动模式控制方法控制海道-1ARV的低激活运动控制
Jixu Li1,2,3,4, Yuangui Tang1,2,3, Hongyin Zhao1,2,3
1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
一种新的无模型自适应滑动模式控制 (MFASMC) 方法有效地控制了海杜-1自动研究车辆 (ARV). 这种方法提高了运动控制,尽管不确定性和不足的作用,超过了PID和MFAC方法.
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
- 海洋工程 海洋工程
背景情况:
- 自主研究车辆 (ARVs) 面临着复杂的低激活运动控制挑战.
- 外部干扰和参数变化等不确定性对ARV导航有重大影响.
- 现有的控制方法可能会与动态参数变化和外部扰动作斗争.
研究的目的:
- 开发一个强大的控制策略,用于海杜-1 ARV.
- 在存在显著的不确定性时,解决不足的动作控制问题.
- 为了提高海道-1 ARV的机动能力,以提高速度,定向和深度控制.
主要方法:
- 建议采用数据驱动,无模型的自适应滑动模式控制 (MFASMC) 方法.
- 整合无模型自适应控制 (MFAC) 与滑动模式控制 (SMC).
- 使用实时测量数据而不依赖数学建模信息.
主要成果:
- 通过MFASMC方法,可以有效控制海杜-1 ARV的速度,方向和深度.
- 控制策略在动态参数的广泛变化和外部干扰下被证明是可靠的.
- 与传统的PID和MFAC方法相比,模拟证实了优越的性能.
结论:
- 拟议的MFASMC方法为不足的ARV运动控制提供了一个可行的解决方案.
- 在不确定的条件下,MFASMC提高了ARV导航的准确性和稳定性.
- 这种数据驱动的方法在自动驾驶船舶控制方面取得了重大进展.
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