Who needs QP for linear MPC anyway?

Conventional MPC uses quadratic programming (QP) to minimise, on-line, a cost over n linearly constrained control moves. However, stability constraints often require the use of large n thereby increasing the on-line computation, rendering the approach impracticable in the case of fast sampling. Here, we explore an alternative that requires a fraction of the computational cost (which increases only linearly with n), and propose an extension which, in all but a small class of models, matches to within a fraction of a percent point the performance of the optimal solution obtained through QP. The provocative title of the paper is intended to point out that the proposed approach o ers a very attractive alternative to QP-based MPC. ? 2002 Published by Elsevier Science Ltd.

• Type: Journal
• Year: 2002
• Where: AUTOMATICA
• Authors: Basil Kouvaritakis, Mark Cannon, J. Anthony Rossiter
• Keywords: AUTOMATICA 2002, Conventional MPC, Quadratic Programming, Stability Constraints