Non Linear and Model Predictive Controls - 5EU9NLM0

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

Responsible(s)

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

Test

First session:
Final assessment (ET1) : Supervised written exam of 2 hours and a half, with one part on MPC and one part on Nonlinear systems;
Continous assessment (CC1) : Homeworks, lab reports, oral examination.

If distant learning mandatory:
Final assessment (ET1) : 2 hours of remote written exam
Continous assessment (CC1) : Homeworks, lab reports, remote oral examination
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Second session
Continous assessment (CC2): Retaking this assessment is not possible / Final assessment (ET1): same as first session.

The exam is given in english only

Calendar

The course exists in the following branches:

• Curriculum - Master's Degree in Engineering ASI - Semester 9 (this course is given in english only )

Additional Information

Course ID : 5EU9NLM0
Course language(s):

You can find this course among all other courses.

Bibliography

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.