Quasi-Decentralized Networked Control of Process Systems

Modern industrial and commercial systems, such as chemical plants and manufacturing processes are large-scale dynamical systems that involve complex, distributed arrangements of interconnected subsystems which are tightly integrated through mass, energy and information flows. The traditional solution for exchanging information and control signals is point-to-point communication which involves a wire connecting the central control computer with each sensor or actuator point. As the size and complexity of industrial systems continue to grow, however, the complexity and cost of installing and maintaining hard-wired control systems become significant. These considerations, coupled with the significant growth in computing and networking abilities in recent times, have led to an increased reliance on distributed computing and process operations across computer net- works, which, compared with point-to-point cables, have many advantages. Yet, control over networks also poses a number of fundamental challenges that need to be addressed before plant operation can take full advantage of their potential. Issues such as band-width limitations, network-induced delays, data losses, signal quantization and real-time scheduling constraints challenge many of the assumptions in traditional process control theory and can degrade the overall control quality if not properly accounted for in the control system design. While these issues have been the subject of significant research work on networked control systems, the majority of research studies have focused mainly on single-unit processes using a centralized control architecture, which is not always the best choice for the structure of the controller in a plant-wide setting. By comparison, results on networked control of multi-unit plants with tightly interconnected units have been limited.;The work in this dissertation presents a methodology for the development of a resource-aware quasi-decentralized model-based control framework for plants with distributed, interconnected units that exchange information over a shared communication network. The framework brings together tools from model-based feedback control, state estimation and sensor scheduling, nonlinear and robust control, as well as hybrid system theory. The central objective is to reduce the exchange of information between the local control systems as much as possible without sacrificing the desired stability and performance properties of the overall plant. To this end, dynamic models of the interconnected units are embedded in the local control system of each unit to provide it with an estimate of the evolution of its neighbors when measurements are not transmitted through the network. The use of a model to recreate the interactions of a given unit with one of its neighbors allows the sensor suite of the neighboring unit to send its data in a discrete fashion since the model can provide an approximation of the unit's dynamics. The state of each model is then updated using the information of the corresponding unit provided by its sensors at discrete time instances to compensate for model uncertainty. Using hybrid system formulations, explicit characterization of the maximum allowable update period (i.e., minimum cross communication rate) between each control system and the sensors of its neighboring units is obtained. Techniques for handling practical implementation issues such as uncertain and nonlinear plant dynamics, incomplete and discrete state measurements, and time-varying external disturbances within the quasi-decentralized control design framework are also developed. Finally, case studies involving applications to simulated models of representative chemical plants are presented to illustrate the developed methods.