TY  - CHAP
TI  - Stabilizing embedded MPC with computational complexity guarantees
ID  - eprints2176
KW  - Closed loop systems
KW  -  Optimal control
KW  -  Optimization
KW  -  Real-time systems
KW  -  Stability analysis
KW  -  Standards
KW  -  Vectors
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6669435&isnumber=6669080
N2  - This paper describes a model predictive control (MPC) approach for discrete-time linear systems with hard constraints on control and state variables. The finite-horizon optimal control problem is formulated as a quadratic program (QP), and solved using a recently proposed dual fast gradient-projection method. More precisely, in a finite number of iterations of the mentioned optimization algorithm, a solution with bounded levels of infeasibility and suboptimality is determined for an alternative problem. This solution is shown to be a feasible suboptimal solution for the original problem, leading to exponential stability of the closed-loop system. The proposed strategy is particularly useful in embedded control applications, for which real-time constraints and limited computing resources can impose tight bounds on the possible number of iterations that can be performed within the scheduled sampling time.
A1  - Rubagotti, Matteo
A1  - Patrinos, Panagiotis
A1  - Bemporad, Alberto
T2  - Proceedings of European Control Conference 
SN  - 978-3-033-03962-9
EP  - 3070
N1  - 2013 European Control Conference (ECC)
July 17-19, 2013, Zürich, Switzerland.
SP  - 3065
AV  - none
Y1  - 2013/07//
PB  - IEEE
ER  -