Content, progress and pedagogy of the module

Students who complete the module will obtain skills within Nonlinear Control including analysis of controllability, observability, and stabilizability and stability, along with control synthesis for non-linear systems, hybrid systems covering dynamical system with both discrete and continuous components, the optimal linear estimator - the Kalman filter - as well as non-linear estimation and sensor fusion.

Learning objectives

Knowledge

• Lyapunov stability
• Backstepping
• Linear Kalman Filters and their limitations
• The extended Kalman filterThe unscented Kalman filter
• Particle filtering
• Kalman filters as parameter estimators
• The influence of (coloured) sensor and model noise on the filter estimate.
• Must be able to understand…
• The invariance principle
• Feedback linearization

Skills

• Controllability
• Observability
• Online estimation techniques to a given system
• Understand and analyze systems with multiple sensors for the purpose of fusing sensor information to control-relevant information
• Stabilizability

Competences

• Synthesis of non-linear control systems
• Synthesis of hybrid control systems
• Synthesis of estimators for non-linear systems

Type of instruction

As described in the introduction to Chapter 3.

Exam

Exams