We consider adaptive output feedback control methodology of highly uncertain nonlinear systems with both parametric uncertainties and unmodelled dynamics. The approach is also applicable to systems of unknown, but bounded dimension. However, the relative degree of the regulated output is assumed to be known. This new control strategy is proposed to address the tracking problem of an induction motor (IM) based on a modified field-oriented control (FOC) method. The obtained controller is then augmented by an online neural network that serves as an approximator for the neglected dynamics and modeling errors. The network weight adaptation rule is derived from Lyapunov stability analysis, that guarantees boundedness of all the error signals of the closed loop system. Computer simulations of an output feedback controlled Induction Machine, augmented via single-hidden-layer (SHL) Neural Networks (NNs), demonstrate the practical potential of the proposed control algorithm.
An optical nondestructive strain measurement technique was performed to analyze the mechanical deformation induced by an electrical field within the insulating materials. Poly(ethylene naphthalene 2,6-dicarboxylate) (PEN) films were then subjected to constant electrical fields right up to their electrical breakdown. The experimental technique made it possible to follow the various stages of the mechanical behavior of PEN in real time. The final breakdown occurred in the observation zone and the related mechanical deformation was captured. A ''margarita'' structure was observed with a hole at the center.The experimental results indicated that the level of the induced-mechanical deformations depended on the local environment. We defined two different zones representing the inside and the outside of the damaged area. The induced-deformations were larger in the damaged zone. It was also observed that deformations increased when the sample had a lower degree of crystallinity.
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