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: short introduction to 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

Recommended previous knowledge

Courses TTK4105 Control Systems and TTK4115 Linear System Theory.

Course materials

To be announced.