Course Overview

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 techniques


http://ense3.grenoble-inp.fr/en/academics/advanced-control-methods-and-practical-implementation-tools-5eus5aua

Learning Achievement

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

Competence

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

Course prerequisites

Basic course in control systems, scientific programming and real-time computer systems

Grading Philosophy

Session 1 : 60%CT + 40% CC
Session 2 : R Remplace CT

Course schedule

Course type

Lecture

Online Course Requirement

Instructor

Christophe Berenguer

Other information

Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
- April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
- September 1st, 2020 for Spring Semester intake (February – June).

Site for Inquiry