Robust H ∞ Control for Delayed Nonlinear Systems Based on Standard Neural Network Models
A neural-network-based robust output feedback H ∞ control design is suggested for control of a class of nonlinear systems with time delays. The design approach employs a neural network, of which the activation functions satisfy the sector conditions, to approximate the delayed nonlinear system. A full-order dynamic output feedback controller is designed for the approximating neural network. The closed-loop neural control system is transformed into a novel neural network model termed standard neural network model (SNNM). Based on the robust H ∞ performance analysis of the SNNM, the parameters of output feedback controllers can be obtained by solving some lilinear matrix inequalities (LMIs). The well-designed controller ensures the asymptotic stability of the closed-loop system and guarantees an optimal H ∞ norm bound constraint on disturbance attenuation for all admissible uncertainties.
KeywordsOutput Feedback Disturbance Attenuation Global Exponential Stability Output Feedback Controller Dynamic Output Feedback
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