Observer Backstepping Design for Flight Control

This paper presents observer backstepping as a new nonlinear flight control design framework. Flight control laws for general-purpose maneuvering in the presence of nonlinear lift and side forces are designed. The controlled variables are the angle of attack, the sideslip angle, and the roll rate. The stability has been proved using Lyapunov stability criteria. Control laws were evaluated using realistic aircraft simulation models, with highly encouraging results

Prediction of critical flashover voltage of polluted insulators under sec and rain conditions using least squares support vector machines (LS-SVM)

This paper describes a methodology that was developed for the prediction of the critical flashover voltage of polluted insulators under sec and rain conditions least squares support vector machines (LS-SVM) optimization. The methodology uses as input variable characteristics of the insulator such as diameter, height, creepage distance, and the number of elements on a chain of insulators. The estimation of the flashover performance of polluted insulators is based on field experience and laboratory tests are invaluable as they significantly reduce the time and labour involved in insulator design and selection. The majority of the variables to be predicted are dependent upon several independent variables. The results from this work are useful to predict the contamination severity, critical flashover voltage as a function of contamination severity, arc length, and especially to predict the flashover voltage. The validity of the approach was examined by testing several insulators with different geometries. A comparison with the Grouping Multi-Duolateration Localization (GMDL) method proves the accuracy and goodness of LS-SVM. Moreover LS-SVMs give a good estimation of results which are validated by experimental tests

Adaptive nonlinear observer augmented by radial basis neural network for a nonlinear sensorless control of an induction machine

International audienc

Feedback Linearization Control for Highly Uncertain Nonlinear Systems Augmented by Single-Hidden-Layer Neural Networks

The main objective of this paper is to design an adaptive output feedback control for a class of uncertain nonlinear systems using only one Single-Hidden-Layer (SHL) Neural Networks (NN) in order to eliminate the unstructured uncertainties. The approach employs feedback linearization, coupled with an on-line NN to compensate for modelling errors. A fixed structure dynamic compensator is designed to stabilize the linearized system. A signal, comprised of a linear combination of the measured tracking error and the compensator states, is used to adapt the NN weights. The network weight adaptation rule is derived from Lyapunov stability analysis, and guarantees that the adapted weight errors and the tracking error are bounded. Numerical simulations of both nonlinear systems, Van der Pol example and tunnel diode circuit model, having full relative degree, are used to illustrate the practical potential of the proposed approach

Experiential Integral Backstepping Sliding Mode Controller to achieve the Maximum Power Point of a PV system

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Vehicle longitudinal force estimation using adaptive neural network nonlinear observer

International audienc

Sliding Mode Control and Modified SVM for Suppressing Circulating Currents in Parallel-Connected Inverters

International audienc

Novel Differential Current Control Strategy Based on a Modified Three-Level SVPWM for Two Parallel-Connected Inverters

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