Observer-Based Adaptive Neural Network Robust Control of Nonlinear Time-Delay Systems with Unmodeled Dynamics

An observer-based adaptive neural-network robust control for a class of nonlinear time-delay systems with unmodeled dynamics. It is presented for a class of non-affine nonlinear time-delay systems with external disturbance and unavailable states. By the implicit function theorem, Taylor's formu...

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Bibliographic Details
Main Authors: Wang Ruliang, Jiang Huiying
Format: Conference Proceeding
Language:English
Subjects:
adaptive
Adaptive systems
Approximation error
Artificial neural networks
neural network
non-affine nonlinear
Nonlinear dynamical systems
observer
Observers
Robustness
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Summary:An observer-based adaptive neural-network robust control for a class of nonlinear time-delay systems with unmodeled dynamics. It is presented for a class of non-affine nonlinear time-delay systems with external disturbance and unavailable states. By the implicit function theorem, Taylor's formula and mean theorem, the form of the non-affine nonlinear systems is transformed into the form of affine nonlinear systems. The controller designed to attenuate the effect of external disturbance and approximation errors of the neural networks on tracking. The unknown time-delay is compensated by using appropriate Young inequality, the weight update laws based on Lyapunov stability theory can guarantee the system stability and asymptotic convergence of the tracking error to zero. Theoretical analysis and simulation results demonstrate the effectiveness of the approach.
DOI:10.1109/CIS.2010.116