• Develop expertise with the state space modeling/analysis/design approach.
• Learn to see dynamical systems in a new way through new concepts, vocabulary, tools, and insights.
• See linear algebra in a new light, one where matrices are representations of linear operators and these operators have simple geometry.
• Learn how optimization can be used to design control systems "automatically."
• Understand how applications can be limited by inaccuracy in system models.

The purpose of this course is to provide an understanding of the theory of linear systems from the state space perspective, with specific application toward feedback control design. Although mathematics (particularly linear algebra) is the language by which the theory is described, this is not a mathematics course. The theorem/proof format is avoided in favor of an exposition of useful "truths" and a demonstration of the underlying reasons. The geometry and insight behind the matrix algebra, in particular, is stressed. However, expect to learn a little math in the process.

The understanding sought in this course is a foundation for further graduate work in various fields, particularly nonlinear dynamical systems, data analysis, advanced control systems, etc. It introduces standard viewpoints, methods, and terminology used in the applied and research literature. It also provides the basis for understanding how many computational analysis and design tools work.

The main learning objectives of Linear Control Design are:

Develop some expertise with the state space modeling/analysis/design approach, learning to see dynamical systems in a new way with new concepts, vocabulary, tools, and insights.
1. See linear algebra in a new light, where matrices are representations of linear operators, and these operators have simple geometry and corresponding insights.
2. Glimpse how optimization can be used to design control systems "automatically."
3. Understand how applications of this theory can be limited by inaccuracy in system models.

Required