# 2022/2023

## 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 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