TY  - CHAP
T2  - Proceedings of the IEEE 51st Annual Conference on Decision and Control (CDC), 2012 
EP  - 667
N1  - 51st IEEE Conference on Decision and Control
December 10-13, 2012. Maui, Hawaii, USA
ID  - eprints1470
N2  - This paper proposes a dual fast gradient-projection method for solving quadratic programming problems that arise in linear model predictive control with general polyhedral constraints on inputs and states. The proposed algorithm is quite suitable for embedded control applications in that: (1) it is extremely simple and easy to code; (2) the number of iterations to reach a given accuracy in terms of optimality and feasibility of the primal solution can be estimated quite tightly; (3) the computational cost per iteration increases only linearly with the prediction horizon; and (4) the algorithm is also applicable to linear time-varying (LTV) model predictive control problems, with an extra on-line computational effort that is still linear with the prediction horizon.
SP  - 662 
A1  - Patrinos, Panagiotis
A1  - Bemporad, Alberto
SN  - 978-1-4673-2064-1
PB  - IEEE
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6426458&isnumber=6425800
TI  - An accelerated dual gradient-projection algorithm for linear model predictive control
AV  - none
Y1  - 2012/12//
KW  - Acceleration; Convergence; Optimization; Prediction  Algorithms; Predictive control; Tin; Vectors
ER  -