Abstract
For a discrete-time nonlinear MIMO system, a multiple models neural network decoupling controller is designed in this paper. At each equilibrium point, the system is expanded into a linear and nonlinear term. These two terms are identified using two neural networkss, which compose one system model. Then, all models, which are got at all equilibrium points, compose the multiple models set. At each instant, the best model is chosen as the system model according to the switching index. To design the controller accordingly, the nonlinear term and the interactions of the best model is viewed as measurable disturbance and eliminated by the use of the feedforward strategy. The simulation example shows that the better system response can be got even when the system is changed around these equilibrium points.
This work is supported by 863 High Technology Project (2002AA412130, 2003AA412310).
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Wang, X., Li, S., Wang, Z., Yue, H. (2004). Multiple Models Neural Network Decoupling Controller for a Nonlinear System. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_27
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DOI: https://doi.org/10.1007/978-3-540-28648-6_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
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