Traditionally, control of chemical plants with multiple geographically-distributed interconnected units has been studied within either the centralized or decentralized control frameworks. In centralized control, all measurements are collected and sent to a central unit for processing, and the resultant control commands are then sent back to the plant. In decentralized control, on the other hand, the plant is decomposed into a number of simpler subsystems (typically based on functional and/or time-scale differences of the unit operations) with interconnections, and a number of local controllers are connected to each subsystem with no signal transfer taking place between different local controllers. An approach that provides a compromise between the complexity of traditional centralized control schemes and the performance limitations of decentralized control approaches is quasi-decentralized control. The term quasi-decentralized control refers to a distributed control structure in which most signals used for control are collected and processed locally -- although some signals (the total number of which is kept to a minimum) still need to be transferred between local units and controllers to adequately account for the interactions between the different units and minimize the propagation of disturbances and process upsets from one unit to another.

One of the key problems that need to be addressed in the design of quasi-decentralized control systems is the coordination between the control and communication tasks and how to account for possible limitations of the communication medium in the formulation and solution of the control problem. The importance of this problem stems from the increased reliance in the process industries in recent years on distributed sensor and control systems that are accessed over communication networks rather than dedicated links, which is motivated in part by the substantial savings in installation and maintenance time and costs as well as the flexibility and enhanced fault-tolerance capabilities of networked control systems. Also, as the trend towards augmenting dedicated control networks with low-cost wireless sensor and actuator networks in the process industry continues to take hold in order to achieve high-density sensing and actuation (see, for example, [1]), the need to account for communication issues in the controller design framework becomes apparent. In this context, communication limitations arise both from the disruptions caused by interference in the field and/or environmental impact, as well as the inherent constraints on the power, computation and communication capabilities of the wireless devices.

The design of a quasi-decentralized control strategy that enforces the desired closed-loop objectives with minimal cross communication between the component subsystems is an appealing goal since it reduces reliance on the communication medium. This is an important consideration particularly when the communication medium is a potentially unreliable wireless sensor network where conserving network resources is key to prolonging the service life of the network. Beyond saving on communication costs, the study of this problem provides an assessment of the robustness of a given networked process control system, and allows designers to identify the fundamental limits on the tolerance of a given control system to communication suspensions. In an effort to address this problem, we developed in [2] a quasi-decentralized networked control architecture that enforces closed-loop stability with minimal cross communication between the constituent subsystems. The main idea was to embed in the local control system of each unit a set of dynamic models that provide the local controller with estimates of the states of the neighboring units, in order to be used when state information is not transmitted over the network. Both the control and communication laws in this case were derived under the assumption that the full state of each unit is available for measurement. In many practical applications, direct measurements of the full state are seldom available, and the lack of full state measurements has important implications that need to be accounted for both at the local control and the plant-wide communication levels.

Motivated by these considerations, we develop in this work a quasi-decentralized output feedback control architecture for multi-unit plants with limited state measurements and tightly interconnected units that exchange information over a shared communication network. We address the problem of designing an integrated state estimation, control and communication policy that requires minimal communication between the units without sacrificing closed-loop stability. To this end, we embed in the local control system of each unit a set of dynamic models that provide an approximation of the interactions between the given unit and its neighbors in the plant when communication is suspended over the network. To deal with the lack of full state measurements, an appropriate state observer is included in the local control system of each unit to generate estimates of the local state variables from the measured outputs. The estimates are used to implement the local state feedback controllers and are also broadcasted at the plant-wide level over the shared communication network to update the state of the corresponding model embedded in the interconnected subsystems when communication is re-established. Using a switched system formulation, 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, the controller and observer design parameters. The results are illustrated using a chemical plant example and compared with existing networked control strategies.

References:

[1] Song, J., A. K. Mok, D. Chen, and M. Nixon, ``Challenges of wireless control in process industry," Proceedings of Workshop on Research Directions for Security and Networking in Critical Real-Time and Embedded Systems, San Jose, CA, 2006.

[2] Sun, Y. and N. H. El-Farra, ``Quasi-decentralized model-based control of networked process systems," Comp. & Chem. Eng., in press.