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The unfalsified control concept and learning
Abstract
Without a plant model or other prejudicial assumptions, a theory is developed for identifying control laws which are consistent with performance objectives and past experimental data-possibly before the control laws are ever inserted in the feedback loop. The theory complements model-based methods such as H/sup /spl infin//-robust control theory by providing a precise characterization of how the set of suitable controllers shrinks when new experimental data is found to be inconsistent with prior assumptions or earlier data. When implemented in real time, the result is an adaptive switching controller. An example is included.
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Citations
Cited by
Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems
Hou, Z., & Jin, S. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2176141 · Cited by 685
Lewis, F. L., & Vamvoudakis, K. G. (2011) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2010.2043839 · Cited by 544
From experiment design to closed-loop control
Hjalmarsson, H. (2005) · Automatica · doi:10.1016/j.automatica.2004.11.021 · Cited by 506
Model free adaptive control with data dropouts
Hou, Z., & Bu, X. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.01.158 · Cited by 109
Aangenent, W., Kostic, D., de Jager, B., van de Molengraft, R., & Steinbuch, M. (2005) · Proceedings of the 2005, American Control Conference, 2005. · doi:10.1109/acc.2005.1470171 · Cited by 70
Metadata
- Authors
- Safonov, M. G., & Tsao, T.-C.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1997-06
- Volume
- 42
- Issue
- 6
- Pages
- 843-847
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2112876741
- Cited by
- 595
- References
- 0
- FWCI
- 14.83
- Type
- journal article
- Language
- en
- Open access
- closed
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- Safonov, M. G., & Tsao, T.-C. (1997). The unfalsified control concept and learning. IEEE Transactions on Automatic Control. https://doi.org/10.1109/9.587340
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
