Cluster 11: Feedback Control with Applications to Robotics

Prerequisite for the cluster: none

Feedback Control

Instructor: Abhishek Halder, PhD (Department of Applied Mathematics)

Feedback control is the science of making best decisions in uncertain situations. It is the common science behind all smart technologies in modern society: computers, automobiles, cell phones, robots, washing machines, stock markets, GPS, airplanes, spacecrafts, power grid. This course will introduce the students to the basic principles of feedback control. Students will learn about the concepts of state, input, output, disturbance, sensing, actuation, stabilization, robustness, and tracking. Particular emphasis will be in explaining how uncertainties enter in all complex systems, and how feedback control can help to handle these uncertainties. The lectures will focus on concepts instead of mathematical representation of those concepts. We will use block diagrams and real-life examples--from balancing a broom to Mars entry-descent-landing--to illustrate the concepts in a lively manner. At the completion of this course, students will learn how to think about complex engineering systems in a systematic way.

Robotic Control Applications

Instructor: Ricardo G. Sanfelice, PhD (Department of Electrical and Computer Engineering)

Feedback control is the science that enables most engineering systems of today.  Robotic systems use algorithms that rely on real-time information to make decisions that affect the motion of the components defining robots.  This course will introduce models of robots, feedback control algorithms to accomplish a given goal, and methods to implement and validate a robotic system in a simulation environment. The lectures will focus on mathematical modeling, design of algorithms, and computer simulation in Matlab.  We will employ a quadrotor system as the driving robotic system on which the concepts and ideas are illustrated in.  At the completion of this course, the students will be capable of translating specifications into properties of a robotic system, formulate basic mathematical models, employ and tune algorithms in the literature that are suitable to meet the given specifications, and simulate in Matlab the entire robotic system.


Transferable Skills: Tools for Success

The transferable skills will be the basic engineering principles and processes widely used in the semiconductor and related industries, especially those involving materials engineering in the Silicon Valley. Additionally, current engineering challenges will be discussed to develop an appreciation of addressing unknown questions and answers -- There are many times in engineering practice where there is no "right" answer or it is unknown and decisions still need to be made, and therefore leads to compromises between various scientific, economic, and technical issues. The tools that students will develop are the basic scientific and engineering principles, as well as how to apply these principles in practice.