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A neuro-fuzzy adaptive control approach for nonlinear systems with model uncertainties is proposed. The implemented control scheme consists of a proportional plus derivative controller that is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by an on-line learning...
A neuro-fuzzy adaptive control approach for nonlinear dynamical systems, which are coupled with unknown dynamics, modeling errors, and various sorts of disturbances, is proposed and used to design a wheel slip regulating controller. The implemented control structure consists of a conventional controller and a neuro-fuzzy network-based feedback controller. The former is provided both to guarantee global...
In this paper, a grey neuro-adaptive control algorithm is suggested for Antilock Braking Systems (ABS). The concept of grey system theory, which has a certain prediction capability, offers an alternative approach to conventional control methods. A multilayer neural network and a grey predictor, GM(1,1) model, are combined in the approach proposed in the paper. The grey neural network controller is...
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