Date of Graduation

2016

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Lane Department of Computer Science and Electrical Engineering

Committee Chair

Yaser P Fallah

Committee Co-Chair

Muhammad A Choudhry

Committee Member

Parviz Famouri

Committee Member

Jason N Gross

Committee Member

Vinod K Kulathumani

Abstract

Cyber--Physical Systems (CPSs) are a generation of engineered systems in which computing, communication, and control components are tightly integrated. Some important application domains of CPS are transportation, energy, and medical systems. The dynamics of CPSs are complex, involving the stochastic nature of communication systems, discrete dynamics of computing systems, and continuous dynamics of control systems. The existence of communication between and among controllers of physical processes is one of the basic characteristics of CPSs. Under this situation, some fundamental questions are: 1) How does the network behavior (communication delay, packet loss, etc.) affect the stability of the system? 2) Under what conditions is a complex system stabilizable?;In cases where communication is a component of a control system, scalability of the system becomes a concern. Therefore, one of the first issues to consider is how information about a physical process should be communicated. For example, the timing for sampling and communication is one issue. The traditional approach is to sample the physical process periodically or at predetermined times. An alternative is to sample it when specific events occur. Event-based sampling requires continuous monitoring of the system to decide a sample needs to be communicated. The main contributions of this dissertation in energy cyber-physical system domain are designing and modeling of event-based (on-demand) communication mechanisms. We show that in the problem of tracking a dynamical system over a network, if message generation and communication have correlation with estimation error, the same performance as the periodic sampling and communication method can be reached using a significantly lower rate of data.;For more complex CPSs such as vehicle safety systems, additional considerations for the communication component are needed. Communication strategies that enable robust situational awareness are critical for the design of CPSs, in particular for transportation systems. In this dissertation, we utilize the recently introduced concept of model-based communication and propose a new communication strategy to address this need. Our approach to model behavior of remote vehicles mathematically is to describe the small-scale structure of the remote vehicle movement (e.g. braking, accelerating) by a set of dynamic models and represent the large-scale structure (e.g. free following, turning) by coupling these dynamic models together into a Markov chain. Assuming model-based communication approach, a novel stochastic model predictive method is proposed to achieve cruise control goals and investigate the effect of new methodology.;To evaluate the accuracy and robustness of a situational awareness methodology, it is essential to study the mutual effect of the components of a situational awareness subsystem, and their impact on the accuracy of situational awareness. The main components are estimation and networking processes. One possible approach in this task is to produce models that provide a clear view into the dynamics of these two components. These models should integrate continuous physical dynamics, expressed with ordinary differential equations, with the discrete behaviors of communication, expressed with finite automata or Markov chain. In this dissertation, a hybrid automata model is proposed to combine and model both networking and estimation components in a single framework and investigate their interactions.;In summary, contributions of this dissertation lie in designing and evaluating methods that utilize knowledge of the physical element of CPSs to optimize the behavior of communication subsystems. Employment of such methods yields significant overall system performance improvement without incurring additional communication deployment costs.

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