Adaptive Quasi-Decentralized MPC of Networked Process Systems
This work presents a framework for quasi-decentralized model predictive control (MPC) design with an adaptive communication strategy. In this framework, each unit of the networked process system is controlled by a local control system for which the measur
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Adaptive Quasi-Decentralized MPC of Networked Process Systems Y. Hu and N. H. El-Farra
Abstract This work presents a framework for quasi-decentralized model predictive control (MPC) design with an adaptive communication strategy. In this framework, each unit of the networked process system is controlled by a local control system for which the measurements of the local process state are available at each sampling instant. And we aim to minimize the cross communication between each local control system and the sensors of the other units via the communication network while preserving stability and certain level of control system performance. The quasidecentralized MPC scheme is designed on the basis of distributed Lyapunov-based bounded control with sampled measurements and then the stability properties of each closed-loop subsystem are characterized. By using this obtained characterization, an adaptive communication strategy is proposed that forecasts the future evolution of the local process state within each local control system. Whenever the forecast shows signs of instability of the local process state, the measurements of the entire process state are transmitted to update the model within this particular control system to ensure stability; otherwise, the local control system will continue to rely on the model within the local MPC controller. The implementation of this theoretical framework is demonstrated using a simulated networked chemical process.
13.1 Introduction Driven by the need to maximize economic efficiency as well as the demand of the market, operations, the size and complexity of modern industrial and commercial processes have been continuing to increase. As an example, chemical plants are Y. Hu (B) · N. H. El-Farra University of California, Davis, California e-mail: [email protected] N. H. El-Farra e-mail: [email protected] J. M. Maestre and R. R. Negenborn (eds.), Distributed Model Predictive Control 209 Made Easy, Intelligent Systems, Control and Automation: Science and Engineering 69, DOI: 10.1007/978-94-007-7006-5_13, © Springer Science+Business Media Dordrecht 2014
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Y. Hu and N. H. El-Farra
typically large-scale processes involving many units that have complex dynamical behavior; tight interconnections usually exist among the various units and thus the dynamics of each unit are strongly coupled with the dynamics of the rest of the plant through the exchange of material and energy. The traditional solution for control of plants with interconnected and distributed units usually falls within either the centralized or the decentralized framework (e.g., see [1, 5, 11, 13] and the references therein). The centralized framework offers satisfactory performance for small- to medium-scale plants as the single control agent that controls the entire plant is able to account for the interconnections of all the units; however, for large-scale plants, the single agent has to maintain control for a large number of subsystems which may have different objectives, and this poses significant problems
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