Aggregate control of clustered networks with inter-cluster time delays

We address a control problem for networks that have multiple dense clusters with sparse interconnection structure. By making use of the time-scale separation properties of such networks, we design state-feedback controllers at the cluster level to guarantee stability in the presence of time varying delays in the inter-cluster feedback channels. Applying results from singular perturbation theory, we show that when these individual controllers are implemented on the actual network model, the closed-loop response is close to that obtained from the approximate models, provided that the clustering is strong and the time delay is below the maximum limit. The design procedure is demonstrated by a simulation example.

[1]  Joe H. Chow,et al.  Power System Coherency and Model Reduction , 2019, Power System Modeling, Computation, and Control.

[2]  Pramod P. Khargonekar,et al.  Introduction to wide-area control of power systems , 2013, 2013 American Control Conference.

[3]  Chung-Yao Kao,et al.  On Stability of Discrete-Time LTI Systems With Varying Time Delays , 2012, IEEE Transactions on Automatic Control.

[4]  Leonidas J. Guibas,et al.  Wireless sensor networks - an information processing approach , 2004, The Morgan Kaufmann series in networking.

[5]  Peter Seiler,et al.  Integral quadratic constraints for delayed nonlinear and parameter-varying systems , 2015, Autom..

[6]  P. Kokotovic,et al.  Control strategies for decision makers using different models of the same system , 1978 .

[7]  A. Rantzer,et al.  System analysis via integral quadratic constraints , 1997, IEEE Trans. Autom. Control..

[8]  Florian Dörfler,et al.  Novel results on slow coherency in consensus and power networks , 2013, 2013 European Control Conference (ECC).

[9]  Almuatazbellah M. Boker,et al.  On aggregate control of clustered consensus networks , 2015, 2015 American Control Conference (ACC).

[10]  Peter Seiler,et al.  Robustness analysis of linear parameter varying systems using integral quadratic constraints , 2014, 2014 American Control Conference.

[11]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[12]  Chung-Yao Kao,et al.  Simple stability criteria for systems with time-varying delays , 2004, Autom..

[13]  Hassan K. Khalil,et al.  Singular perturbation methods in control : analysis and design , 1986 .

[14]  Huibert Kwakernaak,et al.  Linear Optimal Control Systems , 1972 .

[15]  Mi-Ching Tsai,et al.  Robust and Optimal Control , 2014 .

[16]  Chung-Yao Kao,et al.  Stability analysis of systems with uncertain time-varying delays , 2007, Autom..

[17]  Peter Seiler,et al.  Stability Analysis With Dissipation Inequalities and Integral Quadratic Constraints , 2015, IEEE Transactions on Automatic Control.

[18]  J. Doyle,et al.  Robust and optimal control , 1995, Proceedings of 35th IEEE Conference on Decision and Control.

[19]  Fen Wu,et al.  Robust Gain-Scheduling Output Feedback Control of State-Delayed LFT Systems Using Dynamic IQCs , 2015 .

[20]  Chengzhi Yuan,et al.  Dynamic IQC-Based Control of Uncertain LFT Systems With Time-Varying State Delay , 2016, IEEE Transactions on Cybernetics.

[21]  Joe H. Chow,et al.  Time scale modeling of sparse dynamic networks , 1985 .

[22]  A. Rantzer,et al.  System analysis via integral quadratic constraints , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[23]  Chengzhi Yuan,et al.  Exact-memory and memoryless control of linear systems with time-varying input delay using dynamic IQCs , 2017, Autom..