Repetitive and Iterative Learning Control for Power Converter and Precision Motion Control
- Author(s): Teng, Kuo-Tai
- Advisor(s): Tsao, Tsu-Chin
- et al.
This thesis develops learning control algorithms for power converters and precision
motion control. The repetitive control is designed for power converters to provide
zero steady state error and harmonic compensation. A model-based iterative
learning control is designed for linear motor to track given reference profile with
sub-micron RMS error.
The objective of the control design for power converters is to compensate har-
monic distortions in the AC side to enhance power factor of the power converter.
For power inverter, implementations focus on compensating harmonic distortions
in the output AC voltage; For power rectifier, the objective is to compensate
harmonic distortions in the input AC current.
In order to compensate harmonics for power converters, the prototype repeti-
tive control [TTC89] is first being applied to power inverter in fixed frame. How-
ever, the power rectifier is not a linear system. To linearize the system at a more
meaningful equilibrium point, a D-Q transformation is applied. But the original
single-input single-output system become multi-input multi-output system in D-Q
rotating frame, the famous prototype repetitive control design mythology can not
be applied directly.
Repetitive control for multi-input multi-output system is developed for the
control of power converters in D-Q rotating frame. The coupled dynamics in
the multi-input multi-output system is first decoupled by utilizing the Smith-
McMillan decomposition. Then the prototype repetitive control design is applied
to the decoupled single-input single-output system.
In the precision motion control, model-based iterative learning control is pro-
posed to achieve sub-micron RMS tracking error. The learning fiter in iterative
learning control determines the performance in terms of convergence rate and con-
verged error. The ideal learning filter is the inverse of the system being learned.
For non-minimum phase system, direct system inversion would result in an un-
In this thesis, a data-based dynamic inversion method in frequency domain
is proposed. Different inversion filter was investigated in the thesis including
Zero-Phase-Error-Tracking-Controller (ZPETC), Zero-Magnitude-Error-Tacking-
Controller (ZMETC), Direct inversion, data-based phase compensator, and the
proposed data-based frequency domain inversion.