Stochastic Control of Population Dynamics Using Kalman Filtering with Applications to Artificial Muscle Recruitment

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.