Multivariable Process Control


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


  • 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


  • 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


  • 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.


Prerequisite for enrollment for the exam

  • An approved PBL competency profile is a prerequisite for participation in the project exam


Name of examMultivariable Process Control
Type of exam
Oral exam based on a project
Assessment7-point grading scale
Type of gradingExternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleFlervariabel proceskontrol
Module codeESNCAK2P2N
Module typeProject
Duration1 semester
Language of instructionEnglish
Empty-place SchemeYes
Location of the lectureCampus Aalborg
Responsible for the module


Education ownerMaster of Science (MSc) in Engineering (Control and Automation)
Study BoardStudy Board of Electronics and IT
DepartmentDepartment of Electronic Systems
FacultyThe Technical Faculty of IT and Design