0000000000302941

AUTHOR

Mohammad Mehdi Arefi

showing 3 related works from this author

Observer-based adaptive stabilization of a class of uncertain nonlinear systems

2014

In this paper, an adaptive output feedback stabilization method for a class of uncertain nonlinear systems is presented. Since this approach does not require any information about the bound of uncertainties, this information is not needed a priori and a mechanism for its estimation is exploited. The adaptation law is obtained using the Lyapunov direct method. Since all the states are not measurable, an observer is designed to estimate unmeasurable states for stabilization. Therefore, in the design procedure, first an observer is designed and then the control signal is constructed based on the estimated states and adaptation law with the σ-modification algorithm. The uniformly ultimately bou…

Lyapunov functionMathematical optimizationControl and OptimizationAdaptive controlObserver (quantum physics)Chaoticsymbols.namesakeNonlinear systemArtificial IntelligenceControl and Systems EngineeringControl theorysymbolsA priori and a posterioriLyapunov redesignAdaptation (computer science)MathematicsSystems Science & Control Engineering
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Adaptive neural state-feedback stabilizing controller for nonlinear systems with mismatched uncertainty

2014

In this paper, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is presented. By using a radial basis (RBF) neural network, a bound of unknown nonlinear functions is approximated so that no information about the upper bound of mismatched uncertainties is required. The state-feedback is based on Lyapunov stability theory, and it is shown that the asymptotic convergence of the closed-loop system to zero is achieved while maintaining bounded states at the same time. The presented methods are more general than the previous approaches, handling systems with no restriction on the dimension of the system and the number of inpu…

Lyapunov stabilityNonlinear systemEngineeringArtificial neural networkControl theorybusiness.industryAdaptive systemBounded functionConvergence (routing)businessUpper and lower boundsProceeding of the 11th World Congress on Intelligent Control and Automation
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Adaptive Neural Stabilizing Controller for a Class of Mismatched Uncertain Nonlinear Systems by State and Output Feedback

2015

In this paper, first, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is proposed. By using a radial basis function NN (RBFNN), a bound of unknown nonlinear functions is approximated so that no information about the upper bound of mismatched uncertainties is required. Then, an observer-based adaptive controller based on RBFNN is designed to stabilize uncertain nonlinear systems with immeasurable states. The state-feedback and observer-based controllers are based on Lyapunov and strictly positive real-Lyapunov stability theory, respectively, and it is shown that the asymptotic convergence of the closed-loop system to ze…

Lyapunov functionObserver (quantum physics)Computer Simulation; Feedback; Neural Networks (Computer); Nonlinear Dynamics; Control and Systems Engineering; Software; Information Systems; Human-Computer Interaction; Computer Science Applications1707 Computer Vision and Pattern Recognition; Electrical and Electronic EngineeringComputer Science Applications1707 Computer Vision and Pattern RecognitionNeural Networks (Computer)Nonlinear controlUpper and lower boundsFeedbackComputer Science ApplicationsHuman-Computer InteractionNonlinear systemsymbols.namesakeNonlinear DynamicsControl and Systems EngineeringControl theoryAdaptive systemStability theorysymbolsComputer SimulationNeural Networks ComputerElectrical and Electronic EngineeringSoftwareInformation SystemsMathematicsIEEE Transactions on Cybernetics
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