Title: | Policy search for active fault diagnosis with partially observable state |
Authors: | Král, Ladislav Punčochář, Ivo |
Citation: | KRÁL, L. PUNČOCHÁŘ, I. Policy search for active fault diagnosis with partially observable state. International Journal of Adaptive Control and Signal Processing, 2022, roč. 36, č. 9, s. 2190-2216. ISSN: 0890-6327 |
Issue Date: | 2022 |
Publisher: | Wiley |
Document type: | článek article |
URI: | 2-s2.0-85131886551 http://hdl.handle.net/11025/50799 |
ISSN: | 0890-6327 |
Keywords in different language: | approximate dynamic programming;fault detection;neural networks;reinforcement learning;state estimation |
Abstract in different language: | The article deals with a novel design of an active fault detector (AFD) for a nonlinear stochastic system with a partially observable state. The imperfect state information problem is converted to a perfect state information problem using a state estimator. Subsequently, the problem is decomposed into separate tasks of an optimal fault detector design and an approximate input generator design using a dynamic programming technique. While the former task is straightforward, the latter represents a nonlinear functional optimization problem. The input generator is approximated by a multi-layer perceptron neural network, and its unknown parameters are found using the policy search method. Effectiveness of the proposed AFD design is demonstrated numerically on a pendulum system and a heating/cooling system. |
Rights: | Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © Wiley |
Appears in Collections: | Články / Articles (NTIS) OBD |
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article_IJACSP22_KrPu.pdf | 2,49 MB | Adobe PDF | View/Open Request a copy |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/50799
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