ME 320 Dynamics/ME 321 Dynamics Simulation Lab
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Dynamics analysis is central to the design of mechanical systems. This course will walk the students through the dynamics of particles and rigid bodies. The study is based on a Newtonian formulation of the governing equations. An emphasis will be placed on understanding the fundamental principles and applying them to analyze various problems. The instruction will involve a large amount of in-class group discussion and problem solving in order to help students acquire knowledge in an active learning environment and facilitate the learning process. The topics will comprise two major parts, the kinetics of a particle and a rigid body. Each part will include analysis of key concepts such as force, acceleration, work and energy.
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ME 788 Optimal Estimation
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This course presents the fundamentals of optimal state estimation, which is a primary branch of control theory and has wide applications in many engineering areas ranging from robotics and navigation to smart buildings and grid. The central theme is the Kalman filter, a popular mathematical tool to estimate the unknown state variables of a dynamic system using measurements over time. We will offer a systematic and bottom-up introduction, beginning with the key concepts in probability and linear systems theories, and then moving upward to show the derivations, analysis and generalizations of the Kalman filter. Through the course, we intend to help students grasp the essence of the Kalman filtering techniques and apply them to their research projects.
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ME 789 Energy Storage Systems & Control
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This course presents an introduction to electric energy storage systems including batteries, fuel cells and ultra-capacitors from the perspective of dynamic systems and control. It will offer a systematic coverage of the work mechanisms, dynamic modeling, parameter identification, state estimation and control of these systems. In navigating students through these topics, this course is anticipated to help students build a profound understanding of energy storage with its key role in the clean-energy era and elevate their capability of applying control theory to address critical issues toward enabling high-performing energy storage systems.
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ME 890 Model Predictive Control
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Model predictive control (MPC) seeks to optimize the future dynamic behavior of a system under operational constraints. It has proven as a powerful method and tool to address numerous control and automation problems, with ever-increasing application in autonomous vehicles, robotics, energy management and chemical processes. This course will introduce the fundamentals of MPC by covering a series of foundational topics, which include basics of unconstrained and constrained optimization, linear MPC for deterministic systems, stability of MPC, robust and stochastic MPC for uncertain systems, and MPC for nonlinear systems. The course is open to master’s and doctoral students in engineering areas. It will be designed to help students grasp the key subjects and explore the application of this approach to their thesis research.
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ME 890 Topics on Advanced Control and Estimation (I)
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This course is the first installment of Topics on Advanced Control and Estimation. It is a seminar-style course aimed to help students learn the frontier work, independently and collectively, on advanced control and estimation. It has a 2.5-hour session per week, in which the participating students present and discuss research articles at great depths. A student can select the articles based on the research interests or thesis projects and with guidance from the instructor. The seminar topics include but are not limited to optimal control, model predictive control, system identification, state estimation, network systems, robotics, and distributed control. After taking this course, the students will be able to build an understanding of the current and emerging trends about control and estimation theories and develop critical acuity for conducting research in this area.
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ME 640 Capstone Design Project
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This capstone project serves as an opportunity for a team of undergraduate students to bring years of coursework and experiences together to address a real-life engineering challenge. The past and ongoing projects include development of smart battery packs for electric transportation applications, control of HVAC systems for smart buildings and microgrid management. Multifaceted tasks and activities are performed, spanning hardware/software development, algorithm design, and design accounting for engineering needs, economic cost and environmental impact. Another objective of this project is to promote creativity, innovation and teamwork among participants to lay a foundation for their career growth.
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