Repetitive and Iterative Learning Control for Power Converter and Precision Motion Control
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.