@misc{Deng_Jiamei_Input,
 author={Deng, Jiamei and Becerra, Victor M. and Stobart, Richard},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 abstract={The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization.},
 abstract={This paper introduces a technique for handling input constraints within a real time MPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.},
 type={artykuł},
 title={Input constraints handling in an MPC/feedback linearization scheme},
 keywords={predictive control, feedback linearization, neural networks, nonlinear systems, constraints},
}