TY - JOUR

T1 - Distributed model predictive control—Recursive feasibility under inexact dual optimization

AU - Köhler, Johannes

AU - Müller, Matthias A.

AU - Allgöwer, Frank

N1 - Funding information: The authors thank the German Research Foundation (DFG) for support of this work within grant AL 316/11-1 and within the Research Training Group Soft Tissue Robotics ( GRK 2198/1 ). The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Giancarlo Ferrari-Trecate under the direction of Editor Ian R. Petersen.

PY - 2019/4

Y1 - 2019/4

N2 - We propose a novel model predictive control (MPC) formulation, that ensures recursive feasibility, stability and performance under inexact dual optimization. Dual optimization algorithms offer a scalable solution and can thus be applied to large distributed systems. Due to constraints on communication or limited computational power, most real-time applications of MPC have to deal with inexact minimization. We propose a modified optimization problem inspired by robust MPC which offers theoretical guarantees despite inexact dual minimization. The approach is not tied to any particular optimization algorithm, but assumes that the feasible optimization problem can be solved with a bounded suboptimality and constraint violation. In combination with a distributed dual gradient method, we obtain a priori upper bounds on the number of required online iterations. The design and practicality of this method are demonstrated with a benchmark numerical example.

AB - We propose a novel model predictive control (MPC) formulation, that ensures recursive feasibility, stability and performance under inexact dual optimization. Dual optimization algorithms offer a scalable solution and can thus be applied to large distributed systems. Due to constraints on communication or limited computational power, most real-time applications of MPC have to deal with inexact minimization. We propose a modified optimization problem inspired by robust MPC which offers theoretical guarantees despite inexact dual minimization. The approach is not tied to any particular optimization algorithm, but assumes that the feasible optimization problem can be solved with a bounded suboptimality and constraint violation. In combination with a distributed dual gradient method, we obtain a priori upper bounds on the number of required online iterations. The design and practicality of this method are demonstrated with a benchmark numerical example.

KW - Control of constrained systems

KW - Distributed dual optimization

KW - Large scale systems

KW - Predictive control

UR - http://www.scopus.com/inward/record.url?scp=85059845242&partnerID=8YFLogxK

U2 - 10.1016/j.automatica.2018.12.037

DO - 10.1016/j.automatica.2018.12.037

M3 - Article

VL - 102

SP - 1

EP - 9

JO - Automatica

JF - Automatica

SN - 0005-1098

ER -