Optimized distributed control and topology design for hierarchically interconnected systems

A method to reduce the computational complexity for the simultaneous design of a communication topology and feedback control laws for large scale systems is proposed. In general, such combined procedures may be posed as Mixed-Integer Programs (MIPs), which suffer from high combinatorial complexity when the number of possible communication links grows large. Although some explicit solutions for MIP formulations exist, these are either based on very restrictive assumptions or yield an iterative LMI procedure, which is computationally expensive. The presented scheme tackles the problem by pre-analyzing the coupling structure of the plant and dividing it into hierarchically coupled, distinct groups (clusters). This enables one to decompose the global MIP into a set of smaller MIPs, which can then be solved independently. In contrast to existing approaches, a global LMI optimization has to be solved only once, not repeatedly.

[1]  Alex Pothen,et al.  Computing the block triangular form of a sparse matrix , 1990, TOMS.

[2]  Ping Li,et al.  Design of structured dynamic output-feedback controllers for interconnected systems , 2011, Int. J. Control.

[3]  J. Lofberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004, 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508).

[4]  Azwirman Gusrialdi,et al.  Performance-oriented communication topology design for large-scale interconnected systems , 2010, 49th IEEE Conference on Decision and Control (CDC).

[5]  Anders Rantzer,et al.  Dynamic dual decomposition for distributed control , 2009, 2009 American Control Conference.

[6]  P. Dorato,et al.  Optimal linear regulators: The discrete-time case , 1971 .

[7]  Frank Harary,et al.  Graph Theory , 2016 .

[8]  Jos F. Sturm,et al.  A Matlab toolbox for optimization over symmetric cones , 1999 .

[9]  Alexandre M. Bayen,et al.  Optimal network topology design in multi-agent systems for efficient average consensus , 2010, 49th IEEE Conference on Decision and Control (CDC).

[10]  Panos J. Antsaklis,et al.  Control and Communication Challenges in Networked Real-Time Systems , 2007, Proceedings of the IEEE.

[11]  W. Wolovich State-space and multivariable theory , 1972 .

[12]  Dragoslav D. Šiljak,et al.  Decentralized control of complex systems , 2012 .

[13]  V. Armentano,et al.  A procedure to eliminate decentralized fixed modes with reduced information exchange , 1982 .

[14]  Olaf Stursberg,et al.  Optimized distributed control and network topology design for interconnected systems , 2011, IEEE Conference on Decision and Control and European Control Conference.

[15]  Richard C. Larson,et al.  Model Building in Mathematical Programming , 1979 .

[16]  Fu Lin,et al.  Sparse feedback synthesis via the alternating direction method of multipliers , 2012, 2012 American Control Conference (ACC).

[17]  Francesco Borrelli,et al.  Distributed LQR Design for Identical Dynamically Decoupled Systems , 2008, IEEE Transactions on Automatic Control.

[18]  B. Anderson,et al.  Optimal control: linear quadratic methods , 1990 .

[19]  E. Yaz Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[20]  Dragoslav D. Šiljak,et al.  Large-Scale Dynamic Systems: Stability and Structure , 1978 .

[21]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .