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University of South Florida

USF Tampa Graduate Theses and Dissertations

2018

Model reference adaptive control

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Full-Text Articles in Engineering

Control Of Uncertain Dynamical Systems With Spatial And Temporal Constraints, Ehsan Arabi Nov 2018

Control Of Uncertain Dynamical Systems With Spatial And Temporal Constraints, Ehsan Arabi

USF Tampa Graduate Theses and Dissertations

The overarching objective of this dissertation is the development of feedback control frameworks for uncertain dynamical systems that are subject to spatial and/or temporal constraints. These spatiotemporal constraints usually arise from the physical and/or performance characteristics associated with a considered dynamical system in safety-critical applications, where synthesis and analysis of feedback control laws are not trivial. Specifically, the proposed control architectures in this dissertation mainly contribute to the model reference adaptive control and finite-time control literature. In particular, unlike existing model reference adaptive control approaches that are not capable of enforcing user-defined performance guarantees without an ad-hoc tuning process, the …


Toward Verifiable Adaptive Control Systems: High-Performance And Robust Architectures, Benjamin Charles Gruenwald Jun 2018

Toward Verifiable Adaptive Control Systems: High-Performance And Robust Architectures, Benjamin Charles Gruenwald

USF Tampa Graduate Theses and Dissertations

In this dissertation, new model reference adaptive control architectures are presented with stability, performance, and robustness considerations, to address challenges related to the verification of adaptive control systems.

The challenges associated with the transient performance of adaptive control systems is first addressed using two new approaches that improve the transient performance. Specifically, the first approach is predicated on a novel controller architecture, which involves added terms in the update law entitled artificial basis functions. These terms are constructed through a gradient optimization procedure to minimize the system error between an uncertain dynamical system and a given reference model during the …