Abstract
This paper develops an adaptive feedback linearization approach to control nonlinear systems under model mismatch conditions. The approach uses the participatory learning modeling algorithm to estimate the nonlinearities from data streams online, and the certainty equivalence principle to compute the control signal. Simulation experiments with the classic surge tank level control benchmark show that evolving robust granular feedback linearization outperforms exact feedback linearization.
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Acknowledgments
The authors acknowledge the Brazilian National Council for Scientific and Technological Development (CNPq) for grant 305906/2014-3, and the Federal Center for Technological Education of Minas Gerais (CEFET-MG) for their support.
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Oliveira, L., Bento, A., Leite, V., Gomide, F. (2019). Robust Evolving Granular Feedback Linearization. In: Kearfott, R., Batyrshin, I., Reformat, M., Ceberio, M., Kreinovich, V. (eds) Fuzzy Techniques: Theory and Applications. IFSA/NAFIPS 2019 2019. Advances in Intelligent Systems and Computing, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-21920-8_40
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DOI: https://doi.org/10.1007/978-3-030-21920-8_40
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