Cluster 11

Feedback Control with Applications to Robotics

Instructors:
Abhishek Halder, PhD
UCSC Department of Applied Mathematics
Ricardo G. Sanfelice, PhD
UCSC Department of Electrical and Computer Engineering
Kunal Garg, PhD (advisor, Ricardo G. Sanfelice, PhD)
UCSC Department of Electrical and Computer Engineering
Adeel Ahktar, PhD (advisor, Ricardo G. Sanfelice, PhD)
UCSC Department of Electrical and Computer Engineering

Prerequisite: None

Summary: This cluster teaches the basics behind feedback control systems with a focus on robotic control applications.  The lectures introduce key concepts needed for automation and AI, ranging from stability and robustness to learning via experiments.  Students are exposed to mathematics, programming, and state-of-the-art experimental test beds using in academia and industry.

All students in this cluster will be enrolled in the following courses:

Feedback Control

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

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. 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.