Number of hours
- Lectures 32.0
- Projects -
- Tutorials -
- Internship -
- Laboratory works 20.0
ECTS
ECTS 5.0
Goal(s)
Know the limits of approximate linearization, the tools for nonlinear stability analysis, the basics of nonlinear feedback control, as well as notions, methods and implementation tools for predictive control
Gildas BESANCON
Content(s)
NONLINEAR SYSTEMS AND CONTROL
1. What is a nonlinear system
2. Stability analysis for nonlinear systems
3. Basics on nonlinear feedback control
4. Tools to go further (in analysis and control)
MODEL PREDICTIVE CONTROL
1. Basic MPC ingredients: model, constraints and cost function
2. LQR and Riccati equations
3. MPC stability conditions
4. Moving Horizon Estimation
5. NMPC implementation
Linear Systems, Transfer and state space approach, frequency and time-domain analysis
First session:
ER assessment : Supervised written exam of 2 hours and a half, with one part on MPC and one part on Nonlinear systems;
EN assessment : Homeworks, lab reports, oral examination.
If distant learning mandatory:
ER assessment : 2 hours of remote written exam
EN assessment : Homeworks, lab reports, remote oral examination
===========================
Second session
EN assessment: Retaking this assessment is not possible / ER assessment: same as first session.
The exam is given in english only
The course exists in the following branches:
- Curriculum - Master inter MARS - Semester 9 (this course is given in english only
)
- Curriculum - Master's Degree in Engineering ASI - Semester 9 (this course is given in english only
)
Course ID : 5EU9NLM0
Course language(s):
You can find this course among all other courses.
NONLINEAR
1. A. Isidori, Nonlinear control systems, 3rd Ed., Springer, 1995.
2. H. Khalil, Nonlinear systems, 3rd Ed., Prentice Hall, 2002.
MPC
1. J.B. Rawlings, D.Q. Mayne, and M. Diehl, Model predictive control: theory, computation,
and design, volume 2. Nob Hill Publishing Madison, WI, 2017.
2. D.Q. Mayne, J.B. Rawlings, C.V. Rao, and P.O. Scokaert, P. O., Constrained model predictive control: Stability and optimality. Automatica, 36(6), 789-814, 2000.
3. J.A. Andersson, J. Gillis, G. Horn, J.B. Rawlings, and M. Diehl, CasADi: a software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11, 1-36, 2019.