Linear Multivariable Control

These are (work-in-progress) course notes for University of Washington AA/EE/ME 548.

Overview

This course bridges foundational theory and practical application in multivariable control and estimation, with a focus on linear systems in both continuous and discrete time. It emphasizes the development of skills necessary for modeling complex systems and designing robust controllers, serving as a gateway to advanced topics in control theory, robotics, and optimization. A strong mathematical foundation in linear algebra and multivariable calculus is essential, as is familiarity with graduate-level systems theory (e.g., 547 or equivalent), including linear algebra, ordinary differential equations, feedback control, and scientific computing. The course places a strong emphasis on leveraging modern computational tools to implement and analyze control strategies, blending theoretical understanding with hands-on experience.

• Analyze the stability and performance of control systems using mathematical tools.

• Apply state-space methods to model and control multivariable systems.

• Utilize computational software to simulate and validate control designs.

• Develop an understanding of the trade-offs in control system design.

• Formulate a control synthesis problem and mathematically describe control requirements and objectives.

• Gain familiarity in modern software tools that enable the possibility of real-time controllers.

Acknowledgement

This jupyter book template is based on the deep learning for molecules & materials notes by Andrew D. White.