ON ROBUSTNESS OF CONCURRENT LEARNING ADAPTIVE CONTROL TO TIME-VARYING DISTURBANCES AND SYSTEM UNCERTAINTIES

Date

2017-11-09

Author

Sarsilmaz, S. Burak
Kutay, Ali Türker
Yucelen, Tansel

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

41
views

0
downloads

In this paper, we study the robustness characteristics of a recently developed concurrent learning model reference adaptive control approach to time-varying disturbances and system uncertainties. Specifically, the commonly-used constant (or slowly time-varying) assumption on disturbances and system uncertainties for this particular adaptive control approach is replaced with its bounded counterpart with piecewise continuous and bounded derivatives. Based on the Lyapunov's direct method, we then show that the solutions of the closed-loop system are uniformly ultimately bounded, without requiring a modification term in the adaptive law. Estimates for the ultimate bound and the exponential convergence rate to that ultimate bound are further provided. According to these estimates and illustrative numerical examples, similarities and differences between concurrent learning and one of the well-known robustness modifications in adaptive control, namely sigma modification, are explored.

Subject Keywords

Convergence, Adaptation, Excitatiion, Parameter

URI

https://hdl.handle.net/11511/53134

Conference Name

ASME International Mechanical Engineering Congress and Exposition

Collections

Department of Aerospace Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Guaranteed Exponential Convergence without Persistent Excitation in Adaptive Control Yayla, Metehan; Kutay, Ali Türker (2016-09-02) In this paper, a new adaptive control framework for linear systems in which the matched uncertainty can be linearly parameterized is introduced to guarantee the global exponential stability of reference tracking error and parameter convergence error without requiring restrictive persistent excitation condition. The framework uses time histories of control input and system signals to construct least-squares problem based on recorded data. Then, unique solution to least-squares problem is computed, and assign...
Parameter estimation in generalized partial linear models with Tikhanov regularization Kayhan, Belgin; Karasözen, Bülent; Department of Scientific Computing (2010) Regression analysis refers to techniques for modeling and analyzing several variables in statistical learning. There are various types of regression models. In our study, we analyzed Generalized Partial Linear Models (GPLMs), which decomposes input variables into two sets, and additively combines classical linear models with nonlinear model part. By separating linear models from nonlinear ones, an inverse problem method Tikhonov regularization was applied for the nonlinear submodels separately, within the e...
Electromagnetic modeling of split-ring resonators Gurel, Levent; Unal, Alper; Ergül, Özgür Salih (2006-09-15) In this paper, we report our efforts to model split-ring resonators (SRRs) and their large arrays accurately and efficiently in a sophisticated simulation environment based on recent advances in the computational electromagnetics. The resulting linear system obtained from the simultaneous discretization of the geometry and Maxwell's equations is solved iteratively with the multilevel fast multipole algorithm. As an example, we present an array of 125 SRRs showing a negative effective permeability about 92 GHz.
Adaptive Control Algorithm for Linear Systems with Matched and Unmatched Uncertainties Yayla, Metehan; Kutay, Ali Türker (2016-12-14) In this paper, a new uncertainty identification method is introduced for both matched and unmatched uncertainties in an uncertain dynamical system. Online identifications of matched and unmatched uncertainties that can be linearly parameterized are ensured without requiring persistent excitation (PE) condition. Furthermore, constant weight matrices that parameterizes the unstructured uncertainties are guaranteed to stay bounded without PE. Findings are implemented on a hybrid adaptive control design, and gl...
On optimal resource allocation in phased array radar systems Ircı, Ayhan; Saranlı, Afşar; Department of Electrical and Electronics Engineering (2006) In this thesis, the problem of optimal resource allocation in real-time systems is studied. A recently proposed resource allocation approach called Q-RAM (Quality of Service based Resource Allocation Model) is investigated in detail. The goal of the Q-RAM based approaches is to minimize the execution speed in real-time systems while meeting resource constraints and maximizing total utility. Phased array radar system is an example of a system in which multiple tasks contend for multiple resources in order to...

Citation Formats

S. B. Sarsilmaz, A. T. Kutay, and T. Yucelen, “ON ROBUSTNESS OF CONCURRENT LEARNING ADAPTIVE CONTROL TO TIME-VARYING DISTURBANCES AND SYSTEM UNCERTAINTIES,” presented at the ASME International Mechanical Engineering Congress and Exposition, Tampa, FL, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53134.