Prerequisite/Recommended prerequisite for
participation in the module
The project module builds on knowledge obtained during the 1st
semester
Content, progress and pedagogy of the
module
Students who complete the module: must be able to analyze modern
control methods for multi input/multi output systems and to apply
modeling methods and control synthesis for mechanical or energy
conversion systems.
Learning objectives
Knowledge
- Stability and performance limitations in robust control
- Additive and multiplicative model uncertainty
- Robust stability
- Robust performance
- Small gain theorem
- Dynamic programming
- Riccati equation
- Elimination of steady state errors in optimal control
- Use of observer in LQG control
- Stability properties of optimal controller
- Stability properties of finite horizon control
- Solving predictive control with constraints using quadratic
programming
- Dealing with uncertain and nonlinear systems in model
predictive control.
- Mass- and energy balances
- Fundamental laws of thermodynamics
- Models with lumped and distributed parameters
- Model structures for system identification: AR, MA, ARMA,
ARMAX, Box-Jenkins
- System identification methods: Moment method , Least squares
method, Prediction error method, Maximum likelihood method,
Recursive and adaptive parameter estimation
- Lagrange and Hamiltonian mechanics
- Rotation parameters, rotation matrices, quaternion
- Model representations (differential equations, state space,
transfer function, differential-algebraic equations, descriptor
form)
Skills
- Formulation of optimal control problems with references and
disturbances
- Soft real time implementation
- Formulation of the standard robustness problem
- Theory and solution to the standard robust problem
- Formulation of control problems using models of constraints,
disturbances and references combined with a performance function
(Model Predictive Control)
- Software tools for solving constrained optimization
problems
- Should be able to apply system identification methods
Competences
- Synthesis of robust control systems, model predictive control
systems, and of LQG systems
- Should be able to formulate models of a basic energy conversion
systems and mechanical systems.
Type of instruction
As described in the introduction to ยง 17.
Exam
Exams
Name of exam | Multivariable Process Control |
Type of exam | Oral exam based on a project |
ECTS | 15 |
Assessment | 7-point grading scale |
Type of grading | External examination |
Criteria of assessment | The criteria of assessment are stated in the Examination
Policies and Procedures |