TY - GEN
A1 - Deng, Jiamei
A1 - Becerra, Victor M.
A1 - Stobart, Richard
A2 - Korbicz, Józef - ed.
PB - Zielona Góra: Uniwersytet Zielonogórski
N2 - 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.
N2 - 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.
L1 - http://www.zbc.uz.zgora.pl/Content/78763/AMCS_2009_19_2_3.pdf
L2 - http://www.zbc.uz.zgora.pl/Content/78763
KW - predictive control
KW - feedback linearization
KW - neural networks
KW - nonlinear systems
KW - constraints
T1 - Input constraints handling in an MPC/feedback linearization scheme
UR - http://www.zbc.uz.zgora.pl/dlibra/docmetadata?id=78763
ER -