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TTK4215 - Adaptive Control

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TTK4215 - Adaptive Control 

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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:

• 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 possess:

1. Detailed knowledge of on-line parameter estimation and the development and properties of the various methods.
2. Detailed knowledge of adaptive and learning control systems and their development and properties.
3. 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

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