15th Triennial World Congress of the International Federation of Automatic Control
  Barcelona, 21–26 July 2002 
ROBUST LINEAR PROGRAMMING AND OPTIMAL CONTROL
Lieven Vandenberghe* Stephen Boyd** Mehrdad Nouralishahi*
* Electrical Engineering Department, UCLA
** Electrical Engineering Department, Stanford University

The paper describes an efficient method for solving an optimal control problem that arises in robust model-predictive control. The problem is to design the input sequence that minimizes the peak tracking error between the ouput of a linear dynamical system and a desired target output, subject to inequality constraints on the inputs. The system is uncertain, with an impulse response that can take arbitrary values in a given polyhedral set. This problem can be formulated as a robust linear programming problem with structured uncertainty. The presented method is based on Mehrotra’s interior-point method for linear programming, and takes advantage of the problem structure to achieve a complexity that grows linearly with the control horizon, and increases as a cubic polynomial as a function of the system order, the number of inputs, and the number of uncertainty parameters.
Keywords: Linear programming. Convex optimization. Model-predictive control
Session slot T-Mo-M17: Problem-Specific Algorithms for Optimization Problems in/Area code 2d : Optimal Control