Abstract
In this paper an adaptive control strategy based on neural network for a class of nonlinear system is analyzed. A simplified algorithm is presented with the technique in generalized predictive control theory and the gradient descent rule to accelerate learning and improve convergence. Taking the neural network as a model of the system, control signals are directly obtained by minimizing the cumulative differences between a setpoint and output of the model. The applicability in nonlinear system is demonstrated by simulation experiments.
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Lei, J. (2011). Dynamic Structure Neural Network for Stable Adaptive Control of Nonlinear Systems. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21111-9_12
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DOI: https://doi.org/10.1007/978-3-642-21111-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21110-2
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