Naira Hovakimyan

Bio: Naira Hovakimyan is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Adaptive control & Control theory. The author has an hindex of 48, co-authored 476 publications receiving 10255 citations. Previous affiliations of Naira Hovakimyan include Virginia Tech & Wentworth Institute of Technology.

L1 adaptive control theory : guaranteed robustness with fast adaptation

Abstract: This book presents a comprehensive overview of the recently developed L1 adaptive control theory, including detailed proofs of the main results. The key feature of the L1 adaptive control theory is the decoupling of adaptation from robustness. The architectures of L1 adaptive control theory have guaranteed transient performance and robustness in the presence of fast adaptation, without enforcing persistent excitation, applying gain-scheduling, or resorting to high-gain feedback. The book covers detailed proofs of the main results and also presents the flight test results that have used this theory and contains results not yet published in technical journals and conference proceedings. The material is organized into six chapters and concludes with an appendix that summarizes the mathematical results used to support the proofs. Software is available on a supplementary Web page. Audience: L1 Adaptive Control Theory is intended for graduate students; researchers; and aerospace, mechanical, chemical, industrial, and electrical engineers interested in pursuing new directions in research and developing technology at reduced costs. Contents: Foreword; Preface; Chapter 1: Introduction; Chapter 2: State Feedback in the Presence of Matched Uncertainties; Chapter 3: State Feedback in the Presence of Unmatched Uncertainties; Chapter 4: Output Feedback; Chapter 5: L1 Adaptive Controller for Time-Varying Reference Systems; Chapter 6: Applications, Conclusions, and Open Problems; Appendix A: Systems Theory; Appendix B: Projection Operator for Adaptation Laws; Appendix C: Basic Facts on Linear Matrix Inequalities; Bibliography

Design and Analysis of a Novel $\mathcal{L}_1$ Adaptive Control Architecture with Guaranteed Transient Performance

Abstract: Novel adaptive control architecture is presented that has guaranteed transient performance for system's both signals, input and output, simultaneously.

Design and Analysis of a Novel ${\cal L}_1$ Adaptive Control Architecture With Guaranteed Transient Performance

Abstract: This paper presents a novel adaptive control architecture that adapts fast and ensures uniformly bounded transient response for system's both signals, input and output, simultaneously. This new architecture has a low-pass filter in the feedback loop and relies on the small-gain theorem for the proof of asymptotic stability. The tools from this paper can be used to develop a theoretically justified verification and validation framework for adaptive systems. Simulations illustrate the theoretical findings.

Adaptive output feedback control of uncertain nonlinear systems using single-hidden-layer neural networks

Abstract: We consider adaptive output feedback control of uncertain nonlinear systems, in which both the dynamics and the dimension of the regulated system may be unknown. However, the relative degree of the regulated output is assumed to be known. Given a smooth reference trajectory, the problem is to design a controller that forces the system measurement to track it with bounded errors. The classical approach requires a state observer. Finding a good observer for an uncertain nonlinear system is not an obvious task. We argue that it is sufficient to build an observer for the output tracking error. Ultimate boundedness of the error signals is shown through Lyapunov's direct method. The theoretical results are illustrated in the design of a controller for a fourth-order nonlinear system of relative degree two and a high-bandwidth attitude command system for a model R-50 helicopter.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Coverage control for mobile sensing networks

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Linear systems

Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge: