TY  - CHAP
N2  - The control based on online optimization, popularly known as model predictive control (MPC), has long been recognized as the winning alternative for constrained systems. The main limitation of MPC is, however, its online computational complexity. For discrete-time linear time-invariant systems with constraints on inputs and states, we develop an algorithm to determine explicitly the state feedback control law associated with MPC, and show that it is piecewise linear and continuous. The controller inherits all the stability and performance properties of MPC, but the online computation is reduced to a simple linear function evaluation instead of the expensive quadratic program. The new technique is expected to enlarge the scope of applicability of MPC to small-size/fast-sampling applications which cannot be covered satisfactorily with anti-windup schemes
M1  - 2
Y1  - 2000///
SN  - 0-7803-5519-9 
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=876624&isnumber=18973
KW  - computational complexity; discrete-time systems; linear time-invariant systems; model predictive control; multiparametric quadratic programming; optimization; stability; state feedback; computational complexity; control system analysis computing; discrete time systems; linear systems; predictive control; quadratic programming; stability; state feedback
AV  - none
TI  - The explicit solution of model predictive control via multiparametric quadratic programming 
ID  - eprints569
SP  - 872
CY  - Chicago, Illinois June 2000
A1  - Bemporad, Alberto
A1  - Morari, Manfred
A1  - Dua, Vivek
A1  - Pistikopoulos, Efstratios N.
T2  - American Control Conference
EP  - 876
PB  - IEEE
ER  -