(497d) Event-Based Networked Control of Distributed Process Systems with Sensor-Controller Communication Failures

Xue, D. - Presenter, University of California, Davis
El-Farra, N. H., University of California, Davis
The design and analysis of event-based control systems have emerged in recent years as the focus of significant attention in both the academic and industrial circles in networked control research. Interest in event-based control approaches stems, in part, from their promising capabilities to achieve the desired levels of control system performance with reduced utilization of the resources of the sensor- controller communication medium. Maintaining a balance between control performance, on the one hand, and extent of network utilization, on the other, is an important objective in the design of any networked control system, particularly in the context of applications involving large-scale sensor and actuator networks, where reducing network utilization can lead to savings in the energy expenditure of battery-powered wireless devices, thereby prolonging their service life and enabling their deployment in a wide range of technological applications [1].

A key idea in event-triggered model-based control is to compute the control action based on the predictions of an approximate model of the plant, and to suspend or restore sensor-controller communication in response to events that tie the model estimation error to a certain stability threshold. Sensor-controller communication is established only to update the model state when the prescribed stability threshold is breached; otherwise communication is suspended and network resources are saved. Compared with time-triggered communication strategies (e.g., [2]–[4]), event-triggered approaches are more responsive to changes in operating conditions and generally lead to lower overall network load and improved system robustness since they allow a more timely feedback of the potential changes in process operating conditions.

A close inspection of the available literature on this topic shows that while event-triggered control has been widely studied in the context of lumped parameter systems (e.g., see [5]– [7] for some results and references in this area), it has received only limited attention in the context of distributed parameter systems. This is an important gap that needs to be addressed given the fact that many important practical applications (e.g., transport-reaction processes and fluid flows) are characterized by spatial variations and are modeled by Partial Differential Equations (PDEs). Efforts aimed at the development of a framework for event-triggered control of spatially distributed systems have been initiated in a number of recent studies (e.g., [8]–[10]), where the focus has been on addressing various practical implementation issues such as performance optimization, the accommodation of control actuator faults and the handling of limited state measurements.

An important consideration in the implementation of the above approaches is the assumption that the communication medium is available whenever sensor-controller communication is needed. In other words, the communication medium is assumed to reliably transmit the broadcasted data from the sensor to the controller whenever needed. This assumption needs to be re-examined, especially in the context of wireless sensor and actuator networks where transmission failures, communication outages and environmental impacts are common and can interfere with the implementation of the event-triggered control strategy by precluding the execution of timely model updates. This consideration calls for an assessment of the event-triggered control system’s robustness to failures in the communication medium, and the development of appropriate measures to help mitigate the impact of these failures on the stability and performance of the networked closed-loop system.

Motivated by these considerations, the objective of this work is to develop a model-based framework for the analysis and handling of sensor-controller communication failures in event-triggered networked control of a class of spatially-distributed processes. We focus on distributed processes whose dominant dynamics are finite-dimensional, but uncertain, with a finite number of spatially- distributed networked control actuators and measurement sensors. Initially, a model-based networked output-feedback controller is designed based on an approximate low-order model that captures the slow dynamics of the infinite-dimensional system. The controller is implemented using an event-triggered sensor-controller communication policy, which determines the times when the sensor data need to be transmitted to the controller to update the model state based on a suitably-designed stability threshold. An assessment of the networked control systems robustness to failures in the communication medium is carried out using an on-line forecasting approach that tracks the growth of the estimation errors during times of communication outages. An explicit characterization of the allowable down time that communication losses can be tolerated without compromising closed-loop stability is obtained in terms of the various control system design parameters, and used to devise various accommodation measures that enhance the networked closed-loop systems robustness against communication losses. A simulated diffusion-reaction process example is used to illustrate the developed approach.


[1] Christofides, P.D., Davis, J., El-Farra, N.H., Clark, D., Harris, K., and Gipson, J. “Smart plant operations: Vision, progress and challenges,” AIChE J., 53(11), 2734–2741, 2007.

[2] Y. Sun and N. H. El-Farra, “Quasi-decentralized model-based net- worked control of process systems,” Comp. & Chem. Eng., vol. 32, pp. 2016–2029, 2008.

[3] E. Garcia, P. J. Antsaklis, and L. A. Montestruque, Model-Based Control of Networked Systems, Systems & Control: Foundations & Applications. Switzerland: Springer International Publishing, 2014.

[4] C. Zheng, M. Tippett, J. Bao, and J. Liu, “Dissipativity-based distributed model predictive control with low rate communication,” AIChE J., vol. 61, pp. 3288–3303, 2015.

[5] X. Wang and M. Lemmon, “Event-triggering in distributed networked control systems,” IEEE Trans. Automat. Contrr., vol. 56, no. 3, pp. 586–601, 2011.

[6] W. Heemels, M. Donkers, and A. Teel, “Periodic event-triggered control for linear systems,” IEEE Trans. Automat. Contr., vol. 58, no. 4, pp. 847–861, 2013.

[7] E. Garcia, Y. Cao, H. Yu, P. Antsaklis, and D. Casbeer, “Decentralized event-triggered cooperative control with limited communication,” International Journal of Control, vol. 86, no. 9, pp. 1479–1488, 2013.

[8] Z. Yao and N. H. El-Farra, “Resource-aware model predictive control of spatially distributed processes using event-triggered communication,” in Proceedings of 52th IEEE Conference on Decision and Control, Florence, Italy, 2013, pp. 3726–3731.

[9] D. Xue and N. H. El-Farra, “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, pp. 1899–1904.

[10] D. Xue and N. H. El-Farra, “Actuator fault-tolerant control of networked distributed processes with event-triggered sensor-controller communication,” in Proceedings of American Control Conference, Boston, MA, 2016, pp. 1661–1666.