UE Project management and seminars

Project management (10.5 h)

The objectives of this class are to supply the bases of the project management as well as to present the good practices in industries. The quality management according to the standards ISO and the piloting by process are presented through industrial projects. 

This class contains a method to establish a CV as well as simulations of real recruitment interview.Class schedule 

Industrial seminars (27 h for IPA)

• Climate change & sustainable engineering. P. Martinerie, CNRS.
• Semi-active damper design and regulation. B. TALON, SOBEN.
• Mobile robots in everyday life. V. TALON, TwinswHeel.
• Case study on warm compression station for cryogenics. B. BRADU, CERN . 
• Keys and social issues to enter the industrial life. M. PRUNIER, Schneider.

Research seminars (15 h for CST)

Each year, MiSCIT and GIPSA-lab invite keynote speakers to give a short class on their research topic. The lectures (typically 15h) are given in the Control Systems Department of GIPSA-lab. They focus on the latest results in a specific topic of control and systems theory, and may include some labs to illustrate specific aspects. The attendance is composed as Master and Ph.D. students, as well as engineers, researchers and professors. A basic knowledge in dynamical systems, linear algebra and control theory is expected.

Linear Matrix Inequalities and Sum-of-Squares Optimization in Systems and Controls Theory: A Practical and Theoretical Overview (2013) 

By Mattew M. Peet, Professor of Aerospace Engineering, Arizona State University (USA)

Abstract: The topic of this course will be the use of LMI methods for optimal control of linear, nonlinear and infinite-dimensional systems. We will start by posing all major finite-dimensional optimal control problems as LMIs. This includes both output and full-state feedback control for both the H_\infty and H_2 (LQG) system norms. We will also give a brief introduction to the popular SDP solver SeDuMi. Next, we will give a background on the use of LMIs for optimization of polynomial variables such as in the Sum-of-Squares framework - including the use of the Matlab toolbox SOSTOOLS. We will discuss several theoretical tools for the optimization of polynomials such as various versions of the Positivstellensatz. Finally, we will discuss how LMIs and polynomial optimization have been use to resolve long-standing problems in analysis of nonlinear systems and systems with delay, and how these results have been extended to synthesize optimal controllers for systems with delay and certain classes of partial-differential equations.

Syllabus:

• Day 1: Convex optimization; Semidefinite programming; Linear state-space systems theory; Optimal H_2 and H_\infty dynamic output-feedback controller synthesis. 
• Day 2: Polynomial optimization, Sum-of-Squares; Polya's lemma; Ideals, Varieties; The Positivstellensatz; Robust controller synthesis 
• Day 3: Nonlinear stability analysis; Analysis and control of linear delayed systems; Analysis and control of linear partial-differential equations; Stability Analysis of nonlinear delayed and partial-differential equations. 

Model Reduction (Approximation) of Large-Scale Systems (2012) 

By Charles Poussot-Vassal, Researcher at Onera - The French Aerospace Lab.

Abstract: In the engineering area (e.g. aerospace, automotive, biology, circuits), dynamical systems are the basic framework used for modelling, controlling and analysing a large variety of systems and phenomena. Due to the increasing use of dedicated computer-based modelling design software, numerical simulation turns to be more and more used to simulate a complex system or phenomenon and shorten both development time and cost. However, the need of an enhanced model accuracy inevitably leads to an increasing number of variables and resources to manage at the price of a high numerical cost. This counterpart is the justification for model reduction (see Figure 1). 
The objective of the lecture is to introduce the model reduction (or approximation) problem, within the linear framework only, and, in an increase complexity, some of the well established and modern techniques to solve this class of problem. The lecture is also coupled with two Matlab-based labs, in order to emphasize the numerical difficulties and to provide the participant an insight of the existing tools.

Syllabus:

• Day 1: Introduction, motivating examples and model reduction problem; Overview of the approximation methods and linear algebra tools; Gramian and SVD based techniques; Moment matching and Krylov subspace based techniques.
• Day 2: Lab 1, Application of the SVD techniques and the Arnoldi procedure; H2 first order optimality conditions, generalized Krylov subspace and Tangential techniques; Advanced techniques (Mixed and Sylvester approaches / Multi-LTI and LPV problems / Tools).
• Day 3: Lab 2, Krylov based techniques and MORE Toolbox (developed within Onera by C. Poussot-Vassal).

Design Project 2

This project is the direct continuation of Design Project 1.