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6.01SC Introduction to Electrical Engineering and Computer Science I

6.01SC Introduction to Electrical Engineering and Computer Science I (Spring 2011, MIT OCW). Taught by Professor Dennis Freeman, this course provides an integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with mobile robots. Our primary goal is for you to learn to appreciate and use the fundamental design principles of modularity and abstraction in a variety of contexts from electrical engineering and computer science. Our second goal is to show you that making mathematical models of real systems can help in the design and analysis of those systems. Finally, we have the more typical goals of teaching exciting and important basic material from electrical engineering and computer science, including modern software engineering, linear systems analysis, electronic circuits, and decision-making. (from ocw.mit.edu)

Lecture 06 - Designing Control Systems

In this session, we look at how to put all the components of the Signals and Systems unit together. We'll model complex systems by breaking them down into smaller systems and combining them. Then we'll solve for the poles of those complex systems and use them to predict long-term behavior. We can also look at a plot of poles in our system to discover how changing parameters of a small part of a complex system affects the overall behavior of that system.


References
Designing Control Systems | Unit 2
Readings. Lecture handout (PDF). Lecture slides (PDF). Session Activities. Check Yourself.

Go to the Course Home or watch other lectures:

Unit 1: Software Engineering
Lecture 01 - Object-oriented Programming
Lecture 02 - Primitives, Combination, Abstraction, and Patterns
Unit 2: Signals and Systems
Lecture 03 - Signals and Systems
Lecture 04
Lecture 05 - Characterizing System Performance
Lecture 06 - Designing Control Systems
Unit 3: Circuits
Lecture 07 - Circuits
Lecture 08 - Op-Amps
Lecture 09 - Circuit Abstractions
Unit 4: Probability and Planning
Lecture 10 - Discrete Probability and State Estimation
Lecture 11
Lecture 12 - Search Algorithms
Lecture 13 - Optimizing a Search