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Some recent advances in learning and adaptation for uncertain feedback control systems
Author(s) -
Xu Xin,
Wang Cong,
Lewis Frank L.
Publication year - 2014
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2475
Subject(s) - adaptation (eye) , computer science , reinforcement learning , adaptive control , control theory (sociology) , artificial neural network , nonlinear system , control engineering , perspective (graphical) , control (management) , stability (learning theory) , adaptive learning , control system , artificial intelligence , machine learning , engineering , physics , electrical engineering , quantum mechanics , optics
SUMMARY Learning and adaptation are essential abilities for feedback control systems to improve performance under uncertainties and external disturbances. In the past decades, there are more and more research interests in developing feedback controllers with learning abilities to ensure stability or optimality of closed‒loop systems. In this guest editorial for the special issue, some recent advances in this area are introduced from three perspectives. The first one is about new developments in adaptive dynamic programming and reinforcement learning methods, which use function approximators such as neural networks to approximately solve the adaptive optimal control problem of uncertain nonlinear systems. The second perspective is related to the learning issues in adaptive control systems based on neural networks. The third perspective includes some new results to deal with uncertainties in feedback control systems based on traditional nonlinear control approaches such as multi‒step nonlinear model predictive control and nonlinear H ‒ ∞ control.