Date of Award

2026

Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy in Mechanical Engineering-Engineering Mechanics (PhD)

Administrative Home Department

Department of Mechanical and Aerospace Engineering

Advisor 1

Wayne Weaver

Committee Member 1

Gordon Parker

Committee Member 2

Shangyan Zou

Committee Member 3

Flavio Bezerra Costa

Abstract

This dissertation presents a unified adaptive and coordinated control architecture for multi-station centrifugal pump pipeline systems operating under dynamic hydraulic conditions. Local controller adaptability is achieved using a variable-forgetting-factor Recursive Least Squares (RLS) algorithm, which continuously estimates process parameters to adjust controller gains in real time. To prevent excessive effort and controller-induced instability, a reinforcement-learning supervisory layer based on Q-learning is implemented to determine optimal adaptation-activation policies.

For pipeline systems with multiple pump stations, a game-theoretic coordination layer is introduced to balance discharge-pressure contributions, minimize pressure oscillations, and improve energy distribution across geographically distributed assets. Cooperative and non-cooperative strategies are analyzed, and Nash equilibria are shown to support graceful degradation in the presence of communication loss.

Simulation results show significant reductions in steady-state error, improved transient recovery, and improved network pressure uniformity compared with conventional PI control. The unified framework provides a scalable, resilient approach to intelligent pipeline operation and lays the foundation for further research into learning-enabled, multi-agent, autonomous industrial control systems.

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