Adaptive Control Algorithm for Linear Systems with Matched and Unmatched Uncertainties

Date

2016-12-14

Author

Yayla, Metehan
Kutay, Ali Türker

Metadata

Show full item record

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

Item Usage Stats

282
views

0
downloads

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 global exponential stability is established.

Subject Keywords

PERSISTENCY, CONVERGENCE, EXCITATION, PARAMETER

URI

https://hdl.handle.net/11511/53209

Conference Name

55th IEEE Conference on Decision and Control (CDC)

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...
ON ROBUSTNESS OF CONCURRENT LEARNING ADAPTIVE CONTROL TO TIME-VARYING DISTURBANCES AND SYSTEM UNCERTAINTIES Sarsilmaz, S. Burak; Kutay, Ali Türker; Yucelen, Tansel (2017-11-09) 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...
Maximum likelihood estimation of transition probabilities of jump Markov linear systems Orguner, Umut (Institute of Electrical and Electronics Engineers (IEEE), 2008-10-01) This paper describes an online maximum likelihood estimator for the transition probabilities associated with a jump Markov linear system (JMLS). The maximum likelihood estimator is derived using the reference probability method, which exploits an hypothetical probability measure to find recursions for complex expectations. Expectation maximization (EM) procedure is utilized for maximizing the likelihood function. In order to avoid the exponential increase in the number of statistics of the optimal EM algori...
Multi-objective decision making using fuzzy discrete event systems: A mobile robot example Boutalis, Yiannis; Schmidt, Klaus Verner (2010-09-29) In this paper, we propose an approach for the multi-objective control of sampled data systems that can be modeled as fuzzy discrete event systems (FDES). In our work, the choice of a fuzzy system representation is justified by the assumption of a controller realization that depends on various potentially imprecise sensor measurements. Our approach consists of three basic steps that are performed in each sampling instant. First, the current fuzzy state of the system is determined by a sensor evaluation. Seco...
Quantitative measure of observability for linear stochastic systems Subasi, Yuksel; Demirekler, Mübeccel (Elsevier BV, 2014-06-01) In this study we define a new observability measure for stochastic systems: the mutual information between the state sequence and the corresponding measurement sequence for a given time horizon. Although the definition is given for a general system representation, the paper focuses on the linear time invariant Gaussian case. Some basic analytical results are derived for this special case. The measure is extended to the observability of a subspace of the state space, specifically an individual state and/or t...

Citation Formats

M. Yayla and A. T. Kutay, “Adaptive Control Algorithm for Linear Systems with Matched and Unmatched Uncertainties,” presented at the 55th IEEE Conference on Decision and Control (CDC), Las Vegas, NV, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53209.