Robust adaptive control of nonaffine nonlinear systems using radial basis function neural networks
IECON 2006-32nd Annual Conference on IEEE Industrial Electronics, 2006•ieeexplore.ieee.org
In this paper, the problem of noise rejection for a class of nonaffine nonlinear systems with
parameter uncertainty is considered. We develop a neuro adaptive controller with
guaranteed stability by introducing a robust adaptive bound based on Lyapunov stability
analysis. A radial-basis function type neural network is used in the paper. To show the
effectiveness of the proposed controller, the nonlinear Van der Pol oscillator has been
chosen as a case study. Simulation results are very promising
parameter uncertainty is considered. We develop a neuro adaptive controller with
guaranteed stability by introducing a robust adaptive bound based on Lyapunov stability
analysis. A radial-basis function type neural network is used in the paper. To show the
effectiveness of the proposed controller, the nonlinear Van der Pol oscillator has been
chosen as a case study. Simulation results are very promising
In this paper, the problem of noise rejection for a class of nonaffine nonlinear systems with parameter uncertainty is considered. We develop a neuro adaptive controller with guaranteed stability by introducing a robust adaptive bound based on Lyapunov stability analysis. A radial-basis function type neural network is used in the paper. To show the effectiveness of the proposed controller, the nonlinear Van der Pol oscillator has been chosen as a case study. Simulation results are very promising
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