基于延迟反的不确定系统的感知和控制的整合 积极推理
Mingyue Ji1, Kunpeng Pan1, Xiaoxuan Zhang1
1School of Automation, Northwestern Polytechnical University, Xi'an 710129, China.
Entropy (Basel, Switzerland)
|November 27, 2024
概括
延迟反主动推断 (DAIF) 解决了传感器数据滞后,改善了现实世界的系统中的状态估计和预测错误. DAIF增强了主动推理 (AIF) 以更好地控制,特别是在突然的外部刺激时.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 传感器数据传输引入时间滞后,导致信息延迟,偏离当前系统状态.
- 主动推断 (AIF) 经常忽视输入延迟,导致不准确的状态估计和预测错误,特别是在外部干扰的情况下.
研究的目的:
- 提出一个理论框架,延迟反主动推断 (DAIF),以提高AIF在具有时间延迟数据的真实世界系统中的适用性.
- 通过计算输入延迟来提高动态系统中的状态估计和预测准确性.
主要方法:
- 通过将控制分布整合到AIF模型中,定义了DAIF的概率模型.
- 以二次状态,感觉和控制预测错误的总和来表示DAIF的自由能量.
- 引入了从过去状态中得出的预测状态和基于此预测的比例积分 (PI) 类控制机制.
主要成果:
- DAIF包含预测状态和PI类控制,增益与测量准确度变异成反比例.
- 采用二次逆方差精度来适应地补偿外部干扰,取代固定感觉精度.
- 在四旋翼无人机 (UAV) 上进行的模拟表明,与AIF相比,DAIF在轨道跟踪控制方面的表现优越.
结论:
- DAIF有效地弥补了时间滞后的传感器数据,在状态估计和抗干扰性方面表现优于标准AIF.
- 拟议的DAIF框架增强了实时控制系统主动推理的稳定性和适用性.
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