The aim of this course is to present advanced control systems methods for optimal and predictive control and fault detection and isolation & fault tolerance. Tools and methods for real-time implementation of control algorithms on embedded systems are also presented
1) Predictive control : Illustrative example ; Prediction equations for linear time invariant systems ; Definition of the cost function ; Link with the unconstrained optimal regulator ; Constraints definition ; Constrained predictive control ; Control parametrization ; Application examples ; Nonlinear Predictive control
2) Model-based Diagnosis : Introduction, basic concepts, motivation and preliminaries: fault detection and isolation and its use for fault-tolerance and complex systems monitoring and safety. Process models and fault modelling. Presentation of the different approaches and focus on the model-based approach. ; Data validation and reconciliation: measurement errors, balance equations, state estimation for constrained and unconstrained systems, linear and bilinear systems ; Fault detection with parity equations - Static and dynamic cases: Analytical redundancy, parity equations and generation of residuals. Enhanced and structured residuals. Properties and analysis of residual signals ; Fault detection and isolation with state observers and state estimation. Unknown inputs observers. Observers banks.
3) Embedded system code design & implementation : Real-time and Embedded Systems design : Real-Time scheduling algorithms on uni and multiprocessor systems, programming techniquesPrerequisites
Basic course in control systems, scientific programming and real-time computer systems
Session 1 : 60%CT + 40% CC
Session 2 : R Remplace CT
CC 40% + CT 60%
The exam is given in english only
The course exists in the following branches:
Course ID : 5EUS5AUA
You can find this course among all other courses.
1) R. Isermann, Fault-Diagnosis Systems - An Introduction from Fault Detection to Fault Tolerance. Springer, 2006.
2) E. F. Camacho and C.Bordons Alba, Model Predictive Control, Springer 2004.
3)M. Alamir, A Pragmatic Story of Model Predictive Control: Self-Contained algorithms and case-studies, CreateSpace, 2013.
Date of update February 8, 2017