Stochastic Control of Population Dynamics Using Kalman Filtering with Applications to Artificial Muscle Recruitment
Name
Odhner-2009-Stochastic Control of Population Dynamics Using Kalman Filtering with Applications to Artificial Muscle Recruitment.pdf
Size
1.41 MB
Format
Adobe PDF
Checksum (MD5)
e616eee6301dfc0edd7d72d8c3dd0e5f
Author(s)
Odhner, Lael U.
Asada, Harry
Date Issued
July 2009
Journal
Proceedings of the American Control Conference, 2009
Publisher
Institute of Electrical and Electronics Engineers
Citation
Odhner, L., and H. Asada. “Stochastic control of population dynamics using Kalman filtering with applications to artificial muscle recruitment.” American Control Conference, 2009. ACC '09. 2009. 997-1002. © Copyright 2009 IEEE
Version
Final published version
Abstract
This paper addresses a problem in distributed control: given a large number of identical hybrid-state agents, control the ensemble behavior of the agents assuming that only limited information is available about the agents' states. This process has relevance to a number of biologically-inspired control problems, such as motor recruitment. In this paper, we describe a stochastic control policy capable of achieving convergent control of the distribution of an ensemble of finite state agents in this way. Using techniques developed for the observation of biological population dynamics, we show that it is possible to observe the state distribution of agents under our control policy using a Kalman filter. Look-ahead control laws based on the Kalman filter estimates are used to achieve a high degree of stability and robustness in systems exhibiting large time delays. An example of control over a hybrid-state, recruitment-like controller for an artificial muscle is presented.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/ACC.2009.5160027
