Systems Control and Command (09_O-CCM)

• Coefficient : 5
• Hourly Volume: 150.0h (including 72.0h supervised)
  CTD : 19.5h supervised
  Labo : 52.5h supervised (and 12h unsupervised)
  Out-of-schedule personal work : 66h
• Including project : 18h supervised and 36h unsupervised project

AATs Lists

Description

1. Linear systems control (state representation, stability, controllability, observability, state feedback control, observer, state estimator)
2. Lyapunov stability (equilibrium stability concepts, Lyapunov linearization method, Lyapunov direct method)
3. Introduction to controlled systems identification
4. Introduction to parameter and noisy signal estimation using algebraic methods
5. Introduction to robust control based on singular perturbations
6. Introduction to nonlinear control (linearization, sliding modes, Lyapunov, flatness)

Learning Outcomes (AAv)

• AAv1 [heures: 12, B2, B3]: By the end of this course, students will be able to describe and model a controlled system in state representation, for applications in mechatronics, instrumentation, or energy production. The dynamic system may be of varied nature: linear or non-linear, possibly non-stationary, continuous or discrete time, single-input/single-output (SISO) or multi-input/multi-output (MIMO).

• AAv2 [heures: 8, B2, B3]: By the end of this course, students will be able to simulate a controlled system in state representation using scientific computation software and perform transformations to access various system representations (differential equations, state representation, transfer function, transfer matrix).

• AAv3 [heures: 12, B2, B3, B4, D2, D3, D4]: By the end of this course, students will be able to construct a state observer and synthesize an observed state feedback control on a linear SISO system meeting specifications (stability, precision, speed, robustness).

• AAv4 [heures: 12, B2, B3, B4, D2, D3, D4]: By the end of this course, students will be able to model modeling uncertainties of a discrete-time dynamic system and state observation uncertainties, for adaptive state estimation they will implement using Kalman filtering for linear systems.

• AAv5 [heures: 20, B2, B3, B4, D2, D3, D4, E1, F1]: By the end of this course, students will be able to linearize a dynamic process or observation law to perform adaptive state estimation using extended Kalman filtering (EKF filter) and make a comparison with an Unscented Kalman filter (UKF).

• AAv6 [heures: 16, B2, B3, B4, D2, D3, D4, E1, F1]: By the end of this course, students will be able to implement linear system control by state feedback according to a quadratic optimization criterion: LQR control or LQG control when the state is only partially observed

• AAv7 [heures: 12, B2, B3]: By the end of this course, students will be able to identify equilibrium points and analyze local stability of a nonlinear dynamic system, in the phase plane (for order 2 systems) and using Lyapunov methods

• AAv8 [heures: 42, B2, B3, B4, D2, D3, D4, E1, F1]: By the end of this course, students will be able to implement, deploy and tune some nonlinear system control solutions: linearizing control, flatness-based control, Lyapunov function-based control...

Assessment Methods

• Average of several continuous assessment evaluations

Keywords

• state variables, modeling, stability, phase plane, Lyapunov, linear and nonlinear control, robustness, estimation, simulation.

Prerequisites

• Analog and digital control systems; standard linear algebra; analysis; differential equations; programming concepts (Scilab).

Resources

• B. Friedland. Control System Design. An introduction to State-Space Methods. Dover Publication. 1986.
• J. Lévine Analysis of Nonlinear Systems. A Flatness-based Approach. Springer. 2009.
• N. S. Nise, "Control Systems Engineering", 4th Ed., Wiley, 2004.
• H. Sira-Ramirez and S. K. Agrawal. Differentially Flat Systems. Marcel Dekker. 2004.
• J. J. E Slotine and W. Li. Applied nonlinear control. Prentice-Hall, 1990.