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Hessian Estimation Based Adaptive and Cooperative Extremum Localization

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Demircioglu_Huseyin.pdf (5.57 MB)

Date

2021-01-13

Authors

Demircioglu, Huseyin

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Publisher

University of Waterloo

Abstract

The thesis is on Hessian estimation based adaptive and cooperative extremum localization via a single mobile sensory agent as well as a network of multiple such agents. First, we develop a continuous time adaptive extremum localization of an arbitrary quadratic function F(·) based on Hessian estimation, using the measured signal intensity via a single mobile sensory agent. A gradient based adaptive Hessian parameter estimation and extremum localization scheme is developed considering a linear parametric model of field variations. Next, we extend the proposed single agent based Hessian estimation and extremum localization scheme to consensus based cooperative distributed scheme to be implemented by a network of such sensory agents.For the networked multi-agent case, a consensus term is added to the base adaptive laws to obtain enhanced estimation cooperatively. Stability and convergence analysis of the proposed scheme is studied, establishing asymptotic convergence of the Hessian parameters and location estimates to their true values robustly, provided that the motion of agent(s) satisfies certain persistence of excitation(PE) conditions. Furthermore, we show that for a network of connected agents, the PE requirements can be distributed to the agents so that the requirement on each agent is more relaxed and feasible. Later, we design an adaptive motion control scheme for steering a mobile sensory agent in 2D toward the source of a signal field F(·) using the signal intensity the agent continuously measures at its current location. The proposed adaptive control design is based on the Hessian estimation based adaptive extremum localization. Results are displayed to verify that the proposed scheme is stable, provides asymptotic convergence of the Hessian parameter and extremum location estimates to their true values and the agent location to the source location, robustly to signal measurement noises.

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Keywords

adaptive control, extremum localization

LC Keywords

Adaptive control systems

Citation

URI

http://hdl.handle.net/10012/16646

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Theses
Mechanical and Mechatronics Engineering