ME 451 - Automatic Controls
The Automatic Controls course aims to teach the fundamentals concepts of classical control theory, to enable the capability of analyzing dynamic processes, and design related control systems. In particular, by the end of the course, the student will be able to:
a. Solve mathematical equations in the modeling of dynamic systems in both the time and frequency domains.
b. Draw functional block diagrams and determine the corresponding transfer functions.
c. Use matrix methods for solving and interpreting systems of differential equations.
d. Analyze the transient response, stability, and steady state response of control systems.
e. Analyze and interpret the stability and response of control systems using Laplace Transform Methods, Root Locus Plots, and Bode Plots.
f. Become adept in communicating the outcomes of control system analysis and understand its relevance towards system design.
g. Experience system modeling and the estimation of physical parameters.
h. Know the current technology and techniques including analysis and design tools used in modern engineering practice, such as MATLAB and Simulink.
ME 492/592 – Robotic Systems (offered every spring semester)
The Robotics course is co-instructed by Dr. Kim Nguyen and Dr. Marco Ciarcià. It is divided in two sections. The first part of the course will focus on the study of the kinematics, dynamics and control strategies of robotics manipulators. Subsequently, the second part will provide an analogous set of knowledge for multirotors. The two sections will include both theoretical studies and hands-on experience on actual hardware to validate the concepts learned in class.
ME 592 - Nonlinear Programming (offered every fall semester)
The goal of the Nonlinear Programming course is to provide the attendees the fundamental tools to state and solve generic optimization problems, both analytically and numerically. The course is based on a step-by-step approach in which the focus is first dedicated to the minimization/maximization of simple unbounded linear functions. Subsequently, a gradual level of complexity is added into the optimization problems to achieve its final general form, the minimization/maximization of multivariable constrained nonlinear functions. The student will use Matlab as programming language to build the optimization algorithm. There is no prerequisite level of knowledge of Matlab, therefore the course itself will be a good opportunity to learn programming!