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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-

stable filter.

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.

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