This course will present the recent advances made in control and nonlinear dynamics. It will expose students to the concept of approximation theory for real systems, including control systems. Topics to be covered include logistics map and its applications in biology, economics, business, music, and engineering, bifurcations, Poincare maps, strange attractors, and characteristic behavior of nonlinear systems, nonlinear differential equations for control systems and explore the dynamics of a controlled, electrostatically actuated systems, Hopf bifurcations, and chaos.

This course will cover fundamentals of learning and adaptation for control system design and signal processing. Both continuous and discrete time systems will be considered. Adaptive control topics include: Lyapunov stability theory, uniform boundedness, system identification techniques, direct and indirect adaptive control strategies, and adaptive inverse control. Adaptive signal processing topics include: learning algorithms for digital filters, self-optimization, quadratic performance functions, speed of convergence, and applications.

This course will present the issue of control systems in an uncertain, random environment. It will present key techniques for leveraging the control paradigm in the face of stochasticity. Stochastic search algorithms are an important class of search techniques. They are also very useful in application domains where one needs to take multiple objectives (e.g., performance, weight, cost) into account when optimizing.

This course will present the recent advances made in automation and robust control for engineering applications. Topics will include: H-infinity and H-2 control design; structured-singular value analysis and synthesis; model reduction; convex optimization; semi-definite programming; and interior-point methods.

This course will highlight multiagent system technology for control and optimization of complex systems.

This course provides a deep understanding of certain aspects of linear systems, along with a set of tools which are very useful in system analysis and control design. Topics to be treated include: elementary operator theory, basic traditional control theory topics (controllability, observability, realization theory), and advanced control topics, such as the small gain theorem, robust control problems, quadratic control theory, H-infinity control design, Nehari theorem and its applications.

The course covers various aspects of optimization theory with applications to large-scale energy networks such as the electrical power grid, gas and oil field networks.

This course will explore the recent advances made in computer controlled engineering systems and will present state-space formulations for engineering problems through computer-aided engineering analysis and design software such as MATLAB/Simulink and its application to dynamic physical systems. Topics covered will include: modern approach to the analysis and engineering applications of linear systems, modelling and linearization of multi-input, multi-output dynamic physical systems, state-space models and transfer functions.

The course willinvestigate the latest advances in network security. It will explore the aforementioned research area by studying, critically analyzing and discussing, summarizing, and presenting selected first-rate research articles. Techniques such as thoroughly surveying literature, critical discussion and analysis of scientific articles, and the presentation of the obtained results will be demonstrated by students.

This course will present recent advances in the field of optical networking and protocols and their corresponding impact on communication systems in terms of bandwidth usage and efficiency.