Course overview
This hands-on course covers a variety of advanced topics in automatic control system design. This involves time domain descriptions of dynamic systems using state-space system models. Control laws using state-space methods are introduced, including optimal control (LQR and LQG) and advanced PID. State observers are presented, including observer design using both pole placement and optimal (Kalman) observers. The implementation of state space controllers and Kalman filters in digital systems is also covered.
- State Space Models and Applications
- Control and Observe
- Advanced Topics
Course learning outcomes
- Design and construct state-space representation of dynamic systems
- Investigate basic control concepts: controllability, observability, stability for control systems
- Design and build full-state feedback control systems
- Design and build optimal control systems
- Design and build state estimators/observers
- Simulate state space representation of control systems in MATLAB/Simulink.