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Using a constructive function approximation network, an adaptive learning control (ALC) approach is proposed for finite interval tracking problems. The constructive function approximation network consists of a set of bases, and the number of bases can evolve when learning repeats. The nature of the basis allows the continuous adaptive learning of parameters when the network undergoes any structural...
Air-fuel ratio control is a challenging control problem for port fuel injected and throttle body fuel injected spark ignition (SI) engines, since the dynamics of air manifold and fuel injection of the SI engines are highly nonlinear and often with unmodeled uncertainties and disturbance. This paper presents nonlinear control approaches for multi-input multi-output engine models, by developing adaptive...
Function Approximation has been proven to be an effective approach when dealing with nonlinear dynamics. Among numerous function approximation methods, wavelet network shows unique advantage in terms of its orthonormality and multi-layer resolution properties, which enable the on-line tuning or closed-loop tuning for the wavelet network structure. Using such a constructive wavelet network, an adaptive...
In this paper, a learning control approach is applied to the synchronization of two uncertain chaotic systems which contain nonlinear uncertainties with unknown time delays. This learning approach also deals with unknown time-varying parameters having distinct periods in the master and slave systems. Using the Lyapunov-Krasovskii functional and incorporating periodic parametric learning mechanism,...
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