TY  - JOUR
KW  - Receding horizon control; model predictive control;
multiparametric programming; convex quadratic programming; error bounds; piecewise linear control
UR  - http://dx.doi.org/10.1023/A:1023696221899
PB  - Springer-Verlag
SN  - 0022-3239
JF  - Journal of Optimization Theory and Applications
ID  - eprints471
Y1  - 2003/04//
EP  - 38
AV  - none
SP  - 9
IS  - 1
N2  - Algorithms for solving multiparametric quadratic programming (MPQP) were recently proposed in Refs. 1?2 for computing explicit receding horizon control (RHC) laws for linear systems subject to linear constraints on input and state variables. The reason for this interest is that the solution to MPQP is a piecewise affine function of the state vector and thus it is easily implementable online. The main drawback of solving MPQP exactly is that, whenever the number of linear constraints involved in the optimization problem increases, the number of polyhedral cells in the piecewise affine partition of the parameter space may increase exponentially. In this paper, we address the problem of finding approximate solutions to MPQP, where the degree of approximation is arbitrary and allows to tradeoff between optimality and a smaller number of cells in the piecewise affine solution. We provide analytic formulas for bounding the errors on the optimal value and the optimizer, and for guaranteeing that the resulting suboptimal RHC law provides closed-loop stability and constraint fulfillment.
VL  - 117
TI  - Suboptimal Explicit Receding Horizon Control via Approximate Multiparametric Quadratic Programming 
A1  - Bemporad, Alberto
A1  - Filippi, Carlo
ER  -