Abstract
This paper proposes Fractional–Order Generalized Predictive Control (FGPC), a model-predictive control methodology that makes use of a cost function of arbitrary real order. FGPC uses two scalar parameters that represent fractional-order differentiation. These parameters can be tuned to achieve closed-loop specifications in a way much easier and faster than using the classical GPC weighting sequences.
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Hortelano, M.R., de Madrid y Pablo, Á.P., Hierro, C.M., Berlinches, R.H. (2010). Generalized Predictive Control of Arbitrary Real Order. In: Baleanu, D., Guvenc, Z., Machado, J. (eds) New Trends in Nanotechnology and Fractional Calculus Applications. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3293-5_35
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DOI: https://doi.org/10.1007/978-90-481-3293-5_35
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