ECE 5764 Applied Linear Control | ECE | Virginia Tech


Course Information


Analysis and design of sampled-data systems, extraction of discrete-time dynamic models from experimental data, and implementation of dynamic compensators on digital processors. In-depth design experience with LQR optimal control and an introduction to Kalman filtering. Realistic design problems with numerical simulations of practical implementations.

Why take this course?

The proposed course provides a hands-on understanding of fundamental implementation issues in the design of control systems implemented on digital processors. The course emphasizes the use of software tools for the analysis and design of practical controller solutions.


5554 or 5544 or AOE 5744 or AOE 5754 or ECE 5744 or ECE 5754

Prior to taking this course, students must have a solid background in classical control theory (which is taught at the undergraduate level), as well as a solid background in linear systems theory (which is only taught at the graduate level). Completion of assigned work in this course requires a prerequisite knowledge of state-feedback controller design, output-feedback controller design, and general linear systems theory. These topics are only taught at the graduate level, and are covered by any one of the prerequisite courses listed above.

Major Measurable Learning Objectives

  • convert and implement discrete-time dynamic compensators from continuous-time designs
  • design appropriate anti-alias and anti-imaging filters for a controller implementation
  • extract dynamic system models from experimental data for use in design and analysis
  • design state- and output-feedback controllers using LQR/LQG optimal control

Course Topics


Percentage of Course

1. Sampled data systems 10%
2. Digital controller architectures and anti-aliasing/anti-imaging 10%
3. Z-transform and difference equations 10%
4. Continuous-to-discrete transformations (SISO and MIMO) 5%
5. Discrete state-space, IIR, and FIR realizations 10%
6. Frequency response and transfer functions matrices 5%
7. Characteristic Equation, Eigenvalues, and stability criteria 5%
8. Discrete time full state feedback and output feedback design 10%
9. LQR Optimal Control 15%
10. Kalman Filtering and stochastics 10%
11. System Identification 10%