Date of Award

Winter 12-2016

Embargo Period

1-12-2018

Degree Type

Dissertation (CMU Access Only)

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical and Computer Engineering

Advisor(s)

Abe Ishihara

Second Advisor

Jason Lohn

Third Advisor

John Dolan

Fourth Advisor

Kalmanje Krishnakumar

Abstract

There is an emerging need to develop new techniques for control system design that better address the challenges posed by modern large-scale cyber-physical systems. These systems are often massive networks of interconnected and interoperating subsystems that fuse physical processes, embedded computation, automation technologies, and communication. The resulting problems are dimensionally large, exhibit significant time-varying structural variations during operation, and feature complex dynamics, constraints and objectives across local and global-system scales. These properties are difficult to address using traditional control theoretic methods without substantial loss of performance and robustness. To overcome these limitations, this dissertation presents new concepts and methods for control of modern large-scale variably-structured systems through self-assembling and self-configuring control constructs that allow for fundamental restructuring of the control system’s topology in response to the current system structure. We present the System Component Graph (SCG) formulation as a mathematical framework that generalizes and extends directed graph methods from decentralized control. We present algorithms, methods, and metrics for real-time decentralization and control-structure optimization, utilizing the inclusion principle for addressing interconnected overlapping dynamics and optimal linear-quadratic (LQ) methods for local decentralized subsystem control. Global system control and performance is achieved through a centralized planner that provides continuous real-time optimized trajectories as guidance command inputs to each subsystem. We present the method of Random Subcomplement Trees (RST) for pseudo-optimal real-time trajectory planning of large-scale systems which formalizes and extends the method of rapidly-exploring random trees in a control optimization framework. The RST method defines transformations from the higher-dimension state space into an intermediate lower-dimensional search space, where optimal transitions between subspace states are defined. In the context of this approach, the resulting decentralized topology found within the SCG framework provides the RST subspace definition and requisite transformations, and optimal transitions in the search space are found through forward evaluation of the closed-loop decentralized subsystem dynamics. The methods developed in this thesis are applied to a set of real-world problems spanning various domains and demonstrate the application of these methods from first-principle modeling through control system analysis, design, implementation, and evaluation in experimental tests and simulation.

Available for download on Friday, January 12, 2018

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