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The possibility of mismatch between prior uncertainty modeling assumptions and reality is a problem both for robust control and for model-based adaptive control algorithms that aim to use real-time data to adaptively identify and correct such problems. Mismatches between prior model assumptions may fool adaptive algorithms intended to improve robustness into persistently preferring destabilizing controllers over stabilizing ones even when the instability is patently obvious to the eyes of the most casual observer. To eliminate all possibility of being thusly fooled, the assumption-free unfalsified control concept was introduced in the early 1990s and has since been developed to the point where it now provides a unifying overarching theoretical framework for understanding the relationships, benefits, and weakness of various adaptive control methods. We review how the theory allows one to parsimoniously sift the individual elements of accumulating evidence and experimental data to determine precisely which of the elements falsify a given performance level and very briefly discuss recent research on cost-detectable fading-memory cost functions for time-varying plants. Copyright (c) 2016 John Wiley & Sons, Ltd.
Keyword:
data driven
robust control
unfalsified
learning
adaptive control
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