Alan Lynch

Professor, Faculty of Engineering - Electrical & Computer Engineering Dept


Professor, Faculty of Engineering - Electrical & Computer Engineering Dept
(780) 492-5147
11-360 Donadeo Innovation Centre For Engineering
9211 116 St
Edmonton AB
T6G 2H5


Area of Study / Keywords

Robotics Control Systems Energy Systems


Alan F. Lynch obtained his BASc degree with honours at the University of Toronto in engineering science (electrical option) in 1991, his MASc degree in electrical engineering from University of British Columbia in 1994 under the supervision of Professor S.E. Salcudean, and his PhD degree in electrical and computer engineering from the University of Toronto in 1999 under the supervision of Dr. S.A. Bortoff.

From 1999 to 2001 he was a Postdoctoral Researcher in Germany at the Institut fuer Regelungs und Steuerungstheorie (RST) at the Technische Universitaet Dresden with Prof. Dr.-Ing. habil. J. Rudolph. Since 2001 he has been a faculty member at the Department of Electrical and Computer Engineering at the University of Alberta, and currently holds the rank of Professor. From 2009 to 2010 he was a Visiting Professor and Alexander von Humboldt Research Fellow at the Instituts fuer Systemtheorie und Regelungstechnik (IST) at the Universitaet Stuttgart (directed by Prof. Dr.-Ing. Frank Allgoewer).

Dr. Lynch is a member of IEEE and is a Professional Engineer in the province of Alberta and an Associate Editor of Journal of Intelligent and Robotic Systems (Springer).


Since it was founded by Prof. Alan F. lynch in 2001, the Applied Nonlinear Control Lab (ANCL) has researched the theory and application of nonlinear control. This field studies the analysis, prediction, and influence of systems described by nonlinear dynamics. These models capture complex behaviour for a broad range of applications. ANCL balances the derivation of nonlinear control theory with its application on test stands for experimental validation.

The application emphasis of ANCL is Robotics with a particular emphasis on Unmanned Aerial Vehicles (UAVs). Research on UAVs began in 2004 with a outdoor heavy-lift gas powered Bergen Industrial Twin Traditional helicopter which was modified for autonomous flight. Since 2010, we have have focused on multirotor UAVs which can be conveniently flown in our indoor flight arena at the University of Alberta.


ECE 341 - Analytical Methods in Electrical Engineering

Introduction to analytical solutions of partial differential equations, eigenfunctions and eigenvalue problems, special functions in cylindrical and spherical coordinates, Green's functions, and transform methods. These concepts provide the necessary mathematical foundation for understanding and analyzing important physical phenomena encountered at the micro and nanoscales. Examples drawn from electromagnetics, quantum mechanics, solidstate physics, photonics, thermal transport, and microelectromechanical systems. Prerequisites: ECE 240 or E E 238, and MATH 309 or 311. Credit may be obtained in only one of ECE 341 or E E 323.

ECE 360 - Control Systems I

Linear system models. Time response and stability. Block diagrams and signal flow graphs. Feedback control system characteristics. Dynamic compensation. Root locus analysis and design. Frequency response analysis and design. Prerequisites: ECE 203 or E E 250, and ECE 240 or E E 238. Credit may be obtained in only one of ECE 360, ECE 362, E E 357, E E 462 or E E 469.

ECE 664 - Nonlinear Control Design with Applications

Nonlinear geometric control and observer design methods for multi-input nonlinear systems. Differential geometric tools including manifolds, Lie derivatives, Lie brackets, distributions, and the Frobenius Theorem. Conditions for local and global exact and partial state feedback linearization. Output tracking design using input-output state feedback linearization. Local and global nonlinear observer design using exact error linearization. Output feedback control including output feedback linearization and output feedback stabilization based on normal forms. Design methods learnt in this course are implemented on a real physical system.

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