Improved Models for DC-DC Converters
Abstract: To obtain high performance control of a dc-dc converter, a good model of the converter is needed. It is suitable to consider the load to be included in the converter model since it usually affects the dynamics. The load is often the most variable part of this system. If the load current and the output voltage are measured there are good possibilities to obtain a good model of the load on-line. Adaptive control can then be applied to improve the control. In peak current-mode control, the output voltage and the inductor current are measured and utilized by the controller. This thesis analyses some properties that can be obtained if the load current is also measured and utilized for control. Accurate expressions for the control-to-output transfer function, the output impedance, and the audio susceptibility are derived for the buck, boost, and buck-boost converters operated in continuous conduction mode in the case where the load is a linear resistor. If the measured load current is utilized properly by the controller, the output impedance becomes low and the control-to-output transfer function becomes almost invariant for different loads. The use of load current acts as a feedforward term if the load is a current source. However, if the load is a resistor the load current is influenced by changes in the output voltage and the stability is affected. Therefore, the use of load current is not a feedforward action in this case. Instead it can be seen as gain scheduling, which can be considered a special case of adaptive control. In the thesis it is also shown that the two published models for currentmode control, Ridley (1991) and Tan and Middlebrook (1995), give accurate expressions for the control-to-output transfer function and the output impedance but not for the audio susceptibility. A novel model for the audio susceptibility is presented and it is used to improve the two published models. Most of the results in the thesis are validated by comparing the frequency responses predicted by the expressions and switched large-signal simulation models.
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