Abstract
So far, we have shown that, using difference signals of a plant to be controlled, a single CAN2 (competitive associative net) is capable of leaning piecewise Jacobian matrices of nonlinear dynamics of the plant. Here, the CAN2 is an artificial neural net for learning efficient piecewise linear approximation of nonlinear function. Furthermore, a multiobjective robust controller is obtained by means of combining the GPC (generalized predictive controller) and a switching scheme of multiple CAN2s to cope with plant parameter change and control objective change. This paper focuses on an improvement of control performance by means of replacing single CAN2 by bagging CAN2. We analyze to show the effectiveness of the present method via numerical experiments of a crane system.
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Huang, W., Kurogi, S., Nishida, T. (2013). Performance Improvement via Bagging Competitive Associative Nets for Multiobjective Robust Controller Using Difference Signals. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42054-2_40
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DOI: https://doi.org/10.1007/978-3-642-42054-2_40
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