Course: EW306H Honors Advanced Control Engineering

3 Credits – 2 Recitation Hours – 2 Laboratory Hours


Course Description:

This course builds upon the foundation established in EW305H and covers the analysis and design of state-space control systems. Specifically, state feedback design control and state estimation methods are presented and supported by a series of laboratory projects on the design and implementation of state-space control systems for physical systems. This honors course focuses on deeper analysis of the linear and advanced control toolsets and include an open-ended control design project.


Pre-requisites:

EW305H and EW301

Course Coordinator:

Prof. Kiriakidis

Textbook:

Control Systems Engineering, by Norman S. Nise

Course Objectives:

  1. Understand the differences between “classical” and “modern control.”

  2. Use state feedback to manipulate the internal dynamics of a plant (“modern control”).

  3. Apply the control design framework (e.g., transient and steady state).

  4. Understand performance measures in terms of matrix characteristic roots or eigenvalues.

  5. Apply linear algebra for efficient manipulation of state equations.

  6. Apply series expansion to linearize nonlinear state equations.

  7. Solve the state equation for zero-input (free) and zero-state (forced solution).

  8. Analyze the solution of the state equation in terms of modes and eigenvectors.

  9. Apply linear algebra to solve the eigenvalue or pole assignment problem.

  10. Apply linear algebra to address questions of controllability.

  11. Evaluate the effect of pole assignment alternatives on the transient response (e.g., ITAE).

  12. Evaluate the performance of state feedback against implementation and operational costs.

  13. Understand the need for a state estimator with feedback when unknown inputs and ICs.

  14. Apply linear algebra to address the question of observability.

  15. Synthesize a state estimator based on the plant model and the availability of sensors.

  16. Implement the sampled-data state estimator on a microprocessor.

Topics:

  1. The Control Problem in State Space

  2. The Estimation Problem in State Space

  3. Implementation of State Feedback and Observers on Microprocessors