Cooperative distributed model predictive control of multiple coupled linear systems

This study presents a cooperative distributed model predictive control (CDMPC) algorithm for a team of linear subsystems with the coupled cost and coupled constraints. At each sampling time, all the subsystems are permitted to synchronously optimise. An improved compatibility constraint, which plays an important to ensure the stability, is constructed to ensure that the actual state trajectory of each subsystem does not deviate too much from its assumed one. Moreover, a positive invariant terminal set and an associated terminal cost (a local control-Lyapunov function) are designed in the distributed manner. By applying the proposed algorithm, the recursive feasibility with respect to both local and coupled constraints and the closed-loop stability of the whole system are ensured. In final, the numerical results of the comparisons between the CDMPC algorithm and the centralised model predictive control are given to show the effectiveness of the proposed algorithm.

[1]  William B. Dunbar,et al.  Distributed receding horizon control for multi-vehicle formation stabilization , 2006, Autom..

[2]  Paul A. Trodden,et al.  Cooperative distributed MPC of linear systems with coupled constraints , 2013, Autom..

[3]  Jonathan P. How,et al.  Distributed Robust Receding Horizon Control for Multivehicle Guidance , 2007, IEEE Transactions on Control Systems Technology.

[4]  Chen Wang,et al.  Distributed model predictive control of dynamically decoupled systems with coupled cost , 2010, Autom..

[5]  David Q. Mayne,et al.  Model predictive control: Recent developments and future promise , 2014, Autom..

[6]  Soon-Jo Chung,et al.  Decentralized Model Predictive Control of Swarms of Spacecraft Using Sequential Convex Programming , 2013 .

[7]  Baocang Ding,et al.  Distributed RHC for Tracking and Formation of Nonholonomic Multi-Vehicle Systems , 2014, IEEE Transactions on Automatic Control.

[8]  Eduardo Camponogara,et al.  Distributed model predictive control , 2002 .

[9]  William B. Dunbar,et al.  Distributed Receding Horizon Control of Dynamically Coupled Nonlinear Systems , 2007, IEEE Transactions on Automatic Control.

[10]  Akiko Takeda,et al.  On the role of norm constraints in portfolio selection , 2011, Comput. Manag. Sci..

[11]  Jonathan P. How,et al.  Robust distributed model predictive control , 2007, Int. J. Control.

[12]  Baocang Ding,et al.  A synthesis approach of distributed model predictive control for homogeneous multi-agent system with collision avoidance , 2014, Int. J. Control.

[13]  Jonas Balderud,et al.  Data-driven adaptive model-based predictive control with application in wastewater systems , 2011 .

[14]  Raman Uppal,et al.  A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms , 2009, Manag. Sci..

[15]  Stephen J. Wright,et al.  Distributed MPC Strategies With Application to Power System Automatic Generation Control , 2008, IEEE Transactions on Control Systems Technology.

[16]  L. Giovanini Game approach to distributed model predictive control , 2011 .

[17]  Dimos V. Dimarogonas,et al.  Distributed aperiodic model predictive control for multi-agent systems , 2015 .

[18]  Huiping Li,et al.  Distributed receding horizon control of large-scale nonlinear systems: Handling communication delays and disturbances , 2014, Autom..

[19]  Lihua Xie,et al.  Distributed model predictive control for constrained linear systems , 2009 .