Removing the Need for QP in Constrained Predictive Control

Abstract

Predictive control strategies allow for systematic constraint handling and performance optimization while at the same time giving guarantees for stability. However, the quadratic programming (QP) problems associated with constraint handling can be computationally expensive. A recent paper (Kouvaritakis et al. , 1999) introduced a novel alternative for uncertain systems which, for a low degree of suboptimality, converts the on-line optimization problem to one of determining the (unique) positive real root of a polynomial. The current paper examines the issue of computational efficiency versus degree of suboptimality for systems without uncertainty and develops an extension of this algorithm to reduce the degree of suboptimality for a small increase in the computational burden. It is claimed here that the proposed algorithm offers an efficient alternative to QP which should be preferred for applications with fast sampling.