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)
- Must know how to formulate own competences related to
PBL
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
- Can reflect over own use of PBL methods and how these methods
can be used in future projects and work situations
Competences
- Synthesis of robust control systems, model predictive control
systems, and of LQG systems
- Should be able to formulate models of basic energy conversion
systems and mechanical systems.
Type of instruction
As described in the introduction to ยง 17.
Exam
Prerequisite for enrollment for the exam
- An approved PBL competency profile is a prerequisite for
participation in the project 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 |