Online greedy identification of linear dynamical systems
2022 IEEE 61st Conference on Decision and Control (CDC)(2022)
Abstract
This work addresses the problem of exploration in an unknown environment. For multi-input multi-output, linear time-invariant dynamical systems, we use an experimental design framework and introduce an online greedy policy where the control maximizes the information of the next step. We evaluate our approach experimentally and compare it with more elaborate gradient-based methods. In a setting with a limited number of observations, our algorithm has low complexity and shows competitive performances.
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Key words
experimental design,linear time-invariant dynamical system,multi-input multi-output system,multiinput multioutput,online greedy identification,online greedy policy,unknown environment
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