Independent vs. joint estimation in multi-agent iterative learning control
Independent vs. joint estimation in multi-agent iterative learning control
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DOI：
10.1109/cdc.2010.5717888
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发表时间：
2010-12
期刊：
影响因子：
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通讯作者：
Angela P. Schoellig;Javier Alonso-Mora;R. D’Andrea
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文献类型：
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作者：
Angela P. Schoellig;Javier Alonso-Mora;R. D’Andrea
本文研究了多智能体框架下的迭代学习控制（ILC），即一组智能体同时重复执行相同的任务。代理通过使用从以前的执行中获得的知识来提高它们的性能。假设智能体之间的相似性，我们研究智能体之间的信息交换是否能提高个体的学习表现。也就是说，个体代理是否能从其他代理的经验中获益？我们认为多智能体迭代学习问题是一个两步过程：首先，估计每个智能体的重复干扰；第二，纠正错误。我们比较了(I)独立估计和（II）联合估计两种情况下智能体的干扰估计，其中每个智能体只能访问自己的测量值，（II）联合估计，其中所有智能体的信息都是全局可访问的。我们解析地导出了由联合估计引起的性能改进的上界。得到了两种极限情况的结果：(i)纯过程噪声，和（ii）纯测量噪声。在(i)中，信息共享的好处可以忽略不计。对于（ii），当代理之间的高度相似性得到保证时，可以观察到性能的提高。
This paper studies iterative learning control (ILC) in a multi-agent framework, wherein a group of agents simultaneously and repeatedly perform the same task. The agents improve their performance by using the knowledge gained from previous executions. Assuming similarity between the agents, we investigate whether exchanging information between the agents improves an individual's learning performance. That is, does an individual agent benefit from the experience of the other agents? We consider the multi-agent iterative learning problem as a two-step process of: first, estimating the repetitive disturbance of each agent; and second, correcting for it. We present a comparison of an agent's disturbance estimate in the case of (I) independent estimation, where each agent has access only to its own measurement, and (II) joint estimation, where information of all agents is globally accessible. We analytically derive an upper bound of the performance improvement due to joint estimation. Results are obtained for two limiting cases: (i) pure process noise, and (ii) pure measurement noise. The benefits of information sharing are negligible in (i). For (ii), a performance improvement is observed when a high similarity between the agents is guaranteed.