(148f) Distributed Data-Based Fault Identification and Accommodation in Networked Process Systems

Control and supervision of large-scale process networks are important problems that encompass a host of fundamental and practical challenges. These problems have received significant attention in process control as evident by the large and growing body of research work over the past two decades (see, for example, [1]-[5] for some recent results and references). In addition to these advances, research efforts within process control have begun to address the new challenges arising from the integration of networked control systems in process operations. Efforts to address problems such as network resource constraints and real-time scheduling constraints were initiated in a number of studies and led to the development of resource-aware networked plant-wide control [6], state estimation [7] and communication scheduling methods [8] that enforce the desired stability and performance properties with minimal communication requirements. The resulting design methods, however, do not explicitly consider fault diagnosis or compensation in the control system design. These are important problems that require special attention in the context of large-scale process networks. Specifically, due to the interconnections between plant units through mass and energy flows and recycle, the adverse effects of local faults in a given subsystem may propagate to the rest of the plant and potentially cause failure at the plant level, if not explicitly accounted for. Timely identification and handling of faults are therefore essential capabilities that the networked plant-wide control system needs to have.

Motivated by these considerations, we present in this work a data-based framework for distributed fault identification and accommodation in networked process systems controlled over a resource-constrained communication medium. Initially, a quasi-decentralized networked control structure is designed to stabilize the plant in the absence of faults. The structure consists of a set of local model-based control systems that communicate with each other at discrete times. An explicit characterization of the networked closed-loop stability region is obtained in terms of the choice of the update period, the accuracy of the models, and the choice of controller design parameters. To address the fault identification problem, a set of local fault diagnosis agents are designed for the various subsystems. Each agent uses a moving-horizon parameter estimation scheme to estimate on-line the size of the local fault using the locally-sampled states and manipulated inputs. The resulting estimates are then transmitted to a higher-level supervisor that reconciles any potential discrepancies or ambiguities in the local fault diagnosis results which may be caused by the strong coupling between the dynamics of the individual subsystems as well as the presence of plant-model mismatch. Once the location and magnitude of the fault are identified, a number of possible fault accommodation strategies are devised to maintain closed-loop stability. These include updating the post-fault models and switching to a new set of stabilizing controller parameters. The selection of the appropriate fault accommodation strategy is made on the basis of the estimated fault magnitude and the characterization of the networked closed-loop stability region. Finally, the developed methodology is illustrated using a reactor-separator process example.

References:

[1] P. D. Christofides, J. Liu and D. M. de la Pena. Networked and Distributed Predictive Control: Methods and Nonlinear Process Network Applications. Springer-Verlag, London, 2011.

[2] B. T. Stewart, S. Wright and J. B. Rawlings, "Cooperative distributed model predictive control for nonlinear systems," J. Process Control, 21, 698-704, 2011.

[3] K. R. Jillson and B. E. Ydstie, "Process networks with decentralized inventory and flow control," J. Process Control, 17, 399-413, 2007.

[4] M. D. Tetiker, A. Artel, F. Teymour and A. Cinar, A., "Control of grade transitions in distributed chemical reactor networks: An agent-based approach," Comp. Chem. Eng., 32, 1984-1994, 2008.

[5] S. Jogwar, M. Baldea and P. Daoutidis, "Dynamics and control of process networks with large energy recycle, " Ind. Eng. Chem. Res., 48, 6087-6097, 2009.

[6] Y. Sun and N. H. El-Farra, "Quasi-decentralized model-based networked control of process systems,'' Comp. Chem. Eng., 32, 2016-2029, 2008.

[7] Y. Sun and N. H. El-Farra, "A quasi-decentralized approach for networked state estimation and control of process systems," Ind. Eng. Chem. Res., 49, 7957-7971, 2010.

[8] Y. Sun and N. H. El-Farra, "Resource-aware quasi-decentralized control of networked process systems over wireless sensor networks," Chem. Eng. Sci., 69, 93-106, 2012.