(40e) Event-Triggered Model-Based Control and Identification of Networked Process Systems

The problem of designing distributed and supervisory control systems for process networks has been the subject of significant research work in process control. In addition to handling the dynamic coupling and interactions between the constituent subsystems, the management and optimization of information flow and communication between the plant subsystems are becoming increasingly important considerations in the control problem formulation, especially in view of the growing reliance on networked sensor and control systems in plant operations and the increased emphasis placed on smart plant operations [1].

In a previous work [2], the integration of control and communication was addressed in the context of plant-wide control, where a quasi-decentralized model-based networked control structure that enforces closed-loop stability with minimal cross communication was developed. Within each local control system, a set of predictive models were used, in conjunction with the local state measurements, to generate the local control action at times when communication between the plant subsystems was suspended, and the states of the models were updated when communication was permitted at discrete times. In doing so, a minimum communication rate that guarantees closed-loop stability could be determined and it was shown to depend strongly on the size of the plant-model mismatch, with a larger mismatch requiring a higher rate of information transfer between the plant subsystems.

Due to the fixed periodic nature of the communication strategy that this approach utilizes, the local controllers may be irresponsive to time-varying external disturbances, or they may establish communication at unnecessary times. Moreover, this approach may not be the optimal strategy when the plant requires all sensors to access the communication medium simultaneously, which may lead to practical issues such as data losses and transmission delays. One way to overcome these limitations is to use event-triggered communication strategies (e.g., see [3], [4],[5]) in which the local control systems transmit their state measurements to their neighbors only when an event triggers the need for such communication. These events are typically defined based on the desired closed-loop stability and performance characteristics. This approach minimizes network utilization and enables the controllers to be more responsive to time-varying external disturbances. This approach can also reduce the energy expenditures associated with wireless networks.

While a model-based control strategy generally helps reduce the control system’s reliance on the communication medium, and therefore reduces its vulnerability to sensor failures and communication outages, the use of a single model with fixed parameters may limit the achievable savings in network resource utilization, especially when plant operating conditions experience variations and drift over time. Such variations (e.g., due to catalyst deactivation in reactors and fouling in heat exchangers) are unavoidable and invariably lead to process parameters variations which exacerbate the mismatch between the existing model and the actual plant, ultimately leading to increased communication levels to maintain closed-loop stability. One way to address this problem is by constantly updating the model parameters using model identification techniques (e.g., [6],[7]) so as to try to keep plant-model mismatch to a minimum.

In this work, we present a methodological framework for the integration of model-based control and model identification in the context of networked process systems subject to external perturbations, process parameter variations and communication constraints. The framework aims to maintain closed-loop stability in the presence of plant-model mismatch while simultaneously minimizing network utilization. Initially, a networked model-based controller with an event-triggered communication logic is designed and implemented. During operation, model state updates are triggered when a certain state-dependent stability threshold on the model estimation error is breached. The threshold is obtained using Lyapunov techniques and is explicitly characterized in terms of the model uncertainty and the controller design parameters. Meanwhile, a moving-horizon error detection scheme is used to continuously track the overall network utilization and stability. When a significant plant-model mismatch is detected, based on a predefined threshold, model re-identification is carried out based on the available state and input data, and the model parameters are updated with the consideration of system stability together with network utilization. A new closed-loop stability threshold is then obtained based on the newly identified model to trigger future communications between the plant subsystems. A chemical plant example is used to illustrate the implementation of the developed methodology.

References:

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[2] Sun Y, El-Farra NH. Quasi-decentralized model-based networked control of process systems. Computers & Chemical Engineering. 2008;32(9):2016–2029.

[3] Sun Y, El-Farra NH. Quasi-decentralized networked process control using an adaptive communication policy. In Proceedings of American Control Conference, 2010;2841-2846.

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[5] Xue D, El-Farra NH. Networked Event-Triggered Control of Spatially Distributed Processes using a Dual-Mode Communication Strategy. In: Proceedings of 54th IEEE Conference on Decision and Control, 2015; 1899-1904.

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[7] Ljung L. System Identification. Prentice Hall. 1999.