Robust distributed model predictive control using tubes

This paper presents a new form of robust distributed model predictive control (MPC) for multiple subsystems with coupled constraints and persistent disturbances. The new method allows greater flexibility in communications than existing methods, and relaxes restrictions on the order in which distributed computations are performed. The new controller uses the concept of tube MPC, in which an optimization designs a tube for the system to follow rather than a trajectory. The contributions of this paper are the modification of tube MPC for distributed implementation and investigation of the trade between performance and communication

[1]  D. Mayne,et al.  Min-max feedback model predictive control for constrained linear systems , 1998, IEEE Trans. Autom. Control..

[2]  A. Richards,et al.  A decentralized algorithm for robust constrained model predictive control , 2004, Proceedings of the 2004 American Control Conference.

[3]  Eric Feron,et al.  Decentralized Cooperative Trajectory Planning of Multiple Aircraft with Hard Safety Guarantees , 2004 .

[4]  D. Jia,et al.  Min-max feedback model predictive control for distributed control with communication , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[5]  Eduardo Camponogara,et al.  Distributed Model Predictive Control: Synchronous and Asynchronous Computation , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[6]  Stephen J. Wright,et al.  Stability and optimality of distributed model predictive control , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[7]  Ilya Kolmanovsky,et al.  Maximal output admissible sets for discrete-time systems with disturbance inputs , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[8]  Jonathan P. How,et al.  COORDINATION AND CONTROL OF MULTIPLE UAVs , 2002 .

[9]  Stephen J. Wright,et al.  Plant-Wide Optimal Control with Decentralized MPC , 2004 .

[10]  S. Sastry,et al.  Decentralized Reflective Model Predictive Control of Multiple Flying Robots in Dynamic Environment , 2022 .

[11]  Claire J. Tomlin,et al.  DECENTRALIZED OPTIMIZATION VIA NASH BARGAINING , 2004 .

[12]  David Q. Mayne,et al.  Robust model predictive control of constrained linear systems with bounded disturbances , 2005, Autom..

[13]  Jonathan P. How,et al.  Model predictive control of vehicle maneuvers with guaranteed completion time and robust feasibility , 2003, Proceedings of the 2003 American Control Conference, 2003..

[14]  F. Borrelli,et al.  A study on decentralized receding horizon control for decoupled systems , 2004, Proceedings of the 2004 American Control Conference.

[15]  Bruce H. Krogh,et al.  Distributed model predictive control , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[16]  Marios M. Polycarpou,et al.  COOPERATIVE PATH-PLANNING FOR AUTONOMOUS VEHICLES USING DYNAMIC PROGRAMMING , 2002 .

[17]  R.M. Murray,et al.  Receding horizon control of multi-vehicle formations: a distributed implementation , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[18]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .