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Stable multi-input multi-output adaptive fuzzy neural control
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DOI：10.1109/91.771089.png)
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摘要
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In this letter, stable direct and indirect adaptive controllers are presented that use Takagi-Sugeno (T-S) fuzzy systems, conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal vector for a class of continuous time multi-input multi-output (MIMO) square nonlinear plants with poorly understood dynamics. The direct adaptive scheme allows for the inclusion of a priori knowledge about the control input in terms of exact mathematical equations or linguistics, while the indirect adaptive controller permits the explicit use of equations to represent portions of the plant dynamics. We prove that with or without such knowledge the adaptive schemes can learn how to control the plant, provide for bounded internal signals, and achieve asymptotically stable tracking of the reference inputs. We do not impose any initialization conditions on the controllers and guarantee convergence of the tracking error to zero.
Keyword:
direct adaptive control
fuzzy control
indirect adaptive control
MIMO nonlinear systems
neural control
期刊
IF:
11.9
论文数:
5.0K
被引数:
2.9W
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分享收藏EXPERT SUPERVISION OF FUZZY LEARNING-SYSTEMS FOR FAULT-TOLERANT AIRCRAFT CONTROL.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
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