**Course description**— This course offers a practical introduction to the analysis and design of model-based control for linear systems. Topics include modeling and linearization of multi-input multi-output dynamic systems using the state-variable description, fundamentals of linear algebra, analytical and numerical solutions of systems of linear time-invariant differential and difference equations, structural properties of linear dynamic physical systems (controllability, observability and stability), canonical realizations, and design of state feedforward/feedback, optimal, and stochastic controllers and observers, including pole placement, LQR, MPC, Kalman filter, adaptive control approaches. Students will learn how to design linear controllers and implement them to solve real-world problems in control and robotics.

**Course objectives**— The objectives of this course are to develop students’ knowledge of multi-input / multi-output linear systems and their control, to enhance students’ knowledge of engineering applications of linear algebra, and to prepare students for further graduate study in control systems, robotics, machine learning, and signal processing.

**Course outcomes**— Upon completion of this course, the student should be able to:

- Compare state space and transfer function approaches to the study of linear control systems and their relative advantages
- Assess the stability of systems using various stability definitions
- Determine the controllability and observability of a linear system
- Design feedback controllers and observers for linear systems
- Design optimal controller and stochastic observer for linear systems
- Design model-based adaptive controlled for linear systems
- Design controllers to solve real-world problems and implement them in Python and Webot

**Textbook** — No textbook. Lecture slides will be available on Canvas.

**Lecture schedule for Fall 2021**