Examination arrangement

Course content

The course gives and introduction to methods for adaptation and learning in control of dynamic systems with uncertain parameters. On-line parameter estimation: On-line parameter estimation: gradient methods and least squares methods in continuous and discrete time. Parameter estimation with projection. Extremum seeking methods. Direct and indirect adaptive control: pole placement control (PPC), adaptive pole placement control (APPC), model reference control (MRC), model reference adaptive control (MRAC), adaptive backstepping with tuning functions. Machine learning methods: neuroadaptive control and reinforcement learning control.

Learning outcome

Knowledge:
The student should posess:
- detailed knowledge of on-line parameter estimation and the development and properties of the various methods.
- detailed knowledge of adaptive and learning control systems and their development and properties.
- detailed knowledge of methods and tools for stability analysis of adaptive and learning systems.

Skills:
The student should independently be able to:
- apply methods for on-line parameter estimation.
- develop adaptive and learning control systems.
- analyze a problem and select an appropriate method.
- analyze existing methods with respect to stability.

General skills:
The student should be able to:
- communicate technical issues with both experts and laymen.
- communicate independent work in writing.
- contribute to creative processes.

Learning methods and activities

Lectures and compulsory homework assignments.

Compulsory assignments

• Exercises

Further on evaluation

A final written exam is the basis for the final grade in the subject (letter grade). The compulsory activities must be approved to be eligible for the exam.
If there is a re-sit examination, the examination form may change from written to oral.

Specific conditions

Recommended previous knowledge

Courses TTK4105 Control Systems and TTK4115 Linear System Theory.

Course materials

To be announced.