Quasi-decentralized state estimation and control of process systems over communication networks

This paper develops a quasi-decentralized state estimation and control architecture for plants with limited state measurements and distributed, interconnected units that exchange information over a shared communication network. The objective is to stabilize the plant while minimizing network resource utilization and communication costs. The networked control architecture is composed of a family of local control systems that transmit their data in a discrete (on/off) fashion over the network. Each control system includes a state observer that generates estimates of the local state variables from the measured outputs. The estimates are used to implement the local feedback control law and are also shared over the network with the control systems of the interconnected units to account for the interactions between the units. To reduce the exchange of information over the network as much as possible without sacrificing stability, dynamic models of the interconnected units are embedded in the local control system of each unit to provide it with an estimate of the evolution of its neighbors when data are not transmitted through the network. The state of each model is then updated using the state estimate generated by the observer of the corresponding unit and transmitted over the network when communication is re-established. By formulating the networked closed-loop plant as a switched system, an explicit characterization of the maximum allowable update period (i.e., minimum cross communication frequency) between the distributed control systems is obtained in terms of plant-model mismatch, controller and observer design parameters. It is shown that the lack of full state measurements imposes limitations on the maximum allowable update period even if the models used to recreate the plant units? dynamics are accurate. The results are illustrated using a chemical process example and compared with other networked control strategies. The comparison shows that the minimum communication frequency required using quasi-decentralized control is less than what is required by a centralized control architecture indicating that the former is more robust with respect to communication suspension.

[1]  Jan Lunze,et al.  Feedback control of large-scale systems , 1992 .

[2]  J. Hespanha,et al.  Communication logics for networked control systems , 2004, Proceedings of the 2004 American Control Conference.

[3]  P. Daoutidis,et al.  Nonlinear Dynamics and Control of Process Systems with Recycle , 2000 .

[4]  E. W. Jacobsen,et al.  Performance Limitations in Decentralized Control , 2000 .

[5]  A. Cinar,et al.  Agent-based system for reconfiguration of distributed chemical reactor network operation , 2006, 2006 American Control Conference.

[6]  B. Erik Ydstie,et al.  Distributed, asynchronous and hybrid simulation of process networks using recording controllers , 2004 .

[7]  Y. Tipsuwan,et al.  Control methodologies in networked control systems , 2003 .

[8]  Panagiotis D. Christofides,et al.  Smart plant operations: Vision, progress and challenges , 2007 .

[9]  C. Georgakis,et al.  Plantwide regulatory control design procedure using a tiered framework , 1993 .

[10]  Sigurd Skogestad,et al.  Control structure design for complete chemical plants , 2004, Comput. Chem. Eng..

[11]  M. R. Katebi,et al.  Predictive control design for large-scale systems , 1997, Autom..

[12]  Alex Zheng,et al.  Hierarchical procedure for plantwide control system synthesis , 1999 .

[13]  Linda Bushnell,et al.  Stability analysis of networked control systems , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[14]  Katalin M. Hangos,et al.  Thermodynamic approach to the structural stability of process plants , 1999 .

[15]  Nael H. El-Farra,et al.  Quasi-decentralized model-based networked control of process systems , 2008, Comput. Chem. Eng..

[16]  Michael Athans,et al.  Survey of Decentralized Control Methods , 1975 .

[17]  Inanç Birol,et al.  Agent-based control of autocatalytic replicators in networks of reactors , 2005, Computers and Chemical Engineering.

[18]  Dragan Nesic,et al.  Input-to-state stability of networked control systems , 2004, Autom..

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

[20]  Panagiotis D. Christofides,et al.  Control of Nonlinear and Hybrid Process Systems: Designs for Uncertainty, Constraints and Time-Delays , 2005 .

[21]  C.T. Abdallah,et al.  Inherent issues in networked control systems: a survey , 2004, Proceedings of the 2004 American Control Conference.

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

[23]  Panos J. Antsaklis,et al.  On the model-based control of networked systems , 2003, Autom..

[24]  Panagiotis D. Christofides,et al.  Fault‐tolerant control of process systems using communication networks , 2005 .

[25]  Michael Baldea,et al.  Dynamics and control of integrated networks with purge streams , 2006 .

[26]  Wei Zhang,et al.  Stability of networked control systems , 2001 .

[27]  Srdjan S. Stankovic,et al.  Decentralized overlapping control of a platoon of vehicles , 2000, IEEE Trans. Control. Syst. Technol..

[28]  William L. Luyben,et al.  Plantwide control design procedure , 1997 .

[29]  Panagiotis D. Christofides,et al.  Lyapunov-based Model Predictive Control of Nonlinear Systems Subject to Data Losses , 2007, ACC.