Optimization for Information Systems
An introduction to the fundamentals of optimization with a focus on algorithms and applications in signal processing, control systems, machine learning, and robotics.
Digital Control
Techniques for analysis and synthesis of digital control systems. Sample-data systems, state-space systems, and linear feedback design.
Optimal Control and Optimization
Optimal control of dynamic systems, numerical optimization techniques and
their applications in solving optimal-trajectory problems.
their applications in solving optimal-trajectory problems.
Optimal Estimation
Techniques for signal and state estimation in the presence of measurement
and process noise with the emphasis on Wiener and Kalman filtering.
and process noise with the emphasis on Wiener and Kalman filtering.
Advanced Linear Systems
Study of multivariable linear system theory and robust control design
methodologies.
methodologies.
Stochastic Systems
Advanced techniques in stochastic analysis with emphasis on stochastic
dynamics, nonlinear filtering and detection, stochastic control and
stochastic optimization and simulation methods.
dynamics, nonlinear filtering and detection, stochastic control and
stochastic optimization and simulation methods.
Autonomous Control of Robotic Systems
Fundamental issues associated with autonomous robot control. Emphasizes biological perspective that forms the basis of many current developments in robotics.
Nonlinear Systems and Control
Classical analysis techniques and stability theory for nonlinear systems.
Control design for nonlinear systems, including robotic systems. Design
projects.
Control design for nonlinear systems, including robotic systems. Design
projects.
Adaptive Control
Methods of parameter estimation and adaptive control for systems with
constant or slowly-varying unknown parameters. MATLAB design projects
emphasizing applications to physical systems.
constant or slowly-varying unknown parameters. MATLAB design projects
emphasizing applications to physical systems.
Linear Systems and Controls
Introduction to linear system theory and feedback control. Topics include
state space representation, controllability and observability, linear
feedback control.
state space representation, controllability and observability, linear
feedback control.
Advanced Computer Vision & Image Processing using PDEs and Active Contours
Algorithms for computer vision and image processing, emphasizing partial-differential equation and active contour methods. Topics include image smoothing and enhancement, edge detection, morphology, and image reconstruction.
Feedback Control Systems
Analysis and design of control systems. Laplace transforms, transfer functions, and stability. Feedback systems: tracking and disturbance rejection. Graphical design techniques.
Control System Design
Design of control algorithms using state-space methods, microcontroller implementation of control algorithms, and laboratory projects emphasizing motion control applications.
Introduction to Automation and Robotics
Fundamental disciplines of modern robotics: mechanics, control, and computing. Analysis, design, and control of mobile robots and manipulators. Course may contain team projects and hands-on labs.
System Theory for Communication and Control
Study of the basic concepts in linear system theory and numerical linear
algebra with applications to communication, compution, control and signal
processing. A unified treatment.
algebra with applications to communication, compution, control and signal
processing. A unified treatment.
Numerical Methods for Optimization and Optimal Control
Algorithms for numerical optimization and optimal control, Gradient-descent techniques, linear programming, numerical linear system solvers, second-order methods for optimizing performance of dynamical systems.
Computatonal Computer Vision
Computational and theoretical aspects of computer vision. Application areas include robotics, autonomous vehicles, tracking, and image-guided surgery. Includes major project.
Engineering Software Design
Object-oriented software methods for engineering applications. Numerical analysis methods; simulations and graphical presentation of simulation results; analysis of numerical precision. Programming projects.
Signals and Systems
Continuous-time linear systems and signals, their mathematical representations, and computational tools; Fourier and Laplace transforms, convolutions, input-output responses, stability.
Networked Control and Multiagent Systems
Covers tools and techniques for networked control systems as well as application domains and promising research directions.
Game Theory and Multiagent Systems
An introduction to game theory and its application to multiagent systems, including distributed routing, multivehicle control, and networked systems.
Embedded and Hybrid Control Systems
Modeling, analysis, and design of embedded and hybrid control systems.
Neural Networks and Fuzzy Logic in Control
Principles of neural networks and fuzzy systems; the MATLAB Neural Network
and Fuzzy Logic Toolboxes; examples from system identification,
classification and control; laboratory experience.
and Fuzzy Logic Toolboxes; examples from system identification,
classification and control; laboratory experience.
Industrial Controls and Manufacturing
Students are introduced to industrial controls and the fundamentals of
manufacturing with hands-on experience based on lab projects using
industry software and hardware for communications and control.
Crosslisted with TFE 4761.
manufacturing with hands-on experience based on lab projects using
industry software and hardware for communications and control.
Crosslisted with TFE 4761.
Software Fundamentals for Engineering Systems
Using computer algorithms for solving electrical engineering problems arising in
various application domains. Development of effective algorithms and their
implementation by object-oriented code.
various application domains. Development of effective algorithms and their
implementation by object-oriented code.
Computing for Control Systems
Introduction to real-time computing, distributed computing, and software engineering in control systems. The particular requirements of control systems will be presented.
Intelligent Control
Principles of intelligent systems and their utility in modeling,
identification and control of complex systems; neuro-fuzzy tools applied
to supervisory control; hands-on laboratory experience.
identification and control of complex systems; neuro-fuzzy tools applied
to supervisory control; hands-on laboratory experience.
Manufacturing Systems Design
Analytic and simulation tools for design, control and optimization of
manufacturing systems. Discrete event dynamic systems and optimization.
manufacturing systems. Discrete event dynamic systems and optimization.