Multivariable Control

The course covers several aspects of model-based control and estimation methods. The focus is on industrial applications, implementation, real life problems, and hands-on experience. The course gives an overview of state-of-the-art techniques, and provides students with tools to analyse and solve further industrial and research problems. Strong emphasis is given to the use of numerical simulation and scientific programming with Matlab/Simulink or similar.

Basic course in control theory, linear systems and system dynamics. Basic understanding of matrix algebra, statistics, and programming using MATLAB or similar.

No formal requirements over and above the admission requirements.

Upon successful completion of the course, the student:

Knowledge

Upon successful completion of the course, the student should have:

• advanced knowledge in dynamic modeling for industrial applications
• knowledge on how to evaluate suitability of different model structures and choose appropriate model for chosen industrial application
• knowledge in state estimation techniques for linear systems
• advanced knowledge in control strategy development
• knowledge on how to evaluate different model-based control algorithms for multivariate systems
• knowledge on how to select and argue for suitable combination of model-based control algorithms for a chosen industrial application.

Skills

Upon successful completion of the course, the student can:

• apply systematic approach to develop nonlinear and linear dynamic models for chosen industrial application.
• apply data-driven modelling methods to obtain dynamic models for a chosen industrial application.
• implement, test, compare and validate nonlinear and linear dynamic models in simulation environment.
• implement, test and validate linear techniques for state estimation in simulation environment
• implement, test, compare and validate model-based control algorithms for multivariate systems in simulation environment.

General competence

Upon successful completion of the course, the student can:

• develop, implement, test and validate control strategies for multivariate system using different model-based control and estimation methods.

• Systematic approach to dynamic modeling
• Linearization of nonlinear models
• Data driven dynamic modelling (system identification)
• State estimation
• Model-based PID-tuning methods
• Multivariable control methods
• Predictive control algorithms
• Implementation, testing and validation with numerical simulation.

Weekly lectures and exercises, assignments in groups of 2-3 students and one individual semester project.

Two guest lectures on selected topics given by experts from industry and academia.

The following coursework must be approved before the student can take the exam:

Four assignments in groups of 1-3 students (1000 - 2000 words per assignment)

The assessment will be based on two part-exams:

1) Individual project report (4000-6000 words). The project report counts 80% of the final grade.

2) Individual project presentation (10 minutes). The oral examination counts 20% of the final grade

Both exams must be passed in order to pass the course.

The oral examination cannot be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

All aids (a computer with MATLAB and Simulink included) are permitted, provided the rules for plagiarism and source referencing are complied with.

Grade scale A-F.

Two internal examiners. External examiner is used periodically.

Professor Tiina Komulainen