Abstract
An approach is investigated for the adaptive guaranteed cost control design for a class of nonlinear state-delayed systems. The nonlinear term is approximated by a linearly parameterized neural networks(LPNN). A linear state feedback H ∞ control law is presented. An adaptive weight adjustment mechanism for the neural networks is developed to ensure H ∞ regulation performance. It is shown that the control gain matrices and be transformed into a standard linear matrix inequality problem and solved via a developed recurrent neural network.
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Shen, Y., Yu, H., Jian, J. (2006). Reliable Robust Controller Design for Nonlinear State-Delayed Systems Based on Neural Networks. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_74
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DOI: https://doi.org/10.1007/11893295_74
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