The design optimization of DC-DC converters requires the optimum selection of several parameters to achieve improved efficiency and performance. Some of these parameters are load dependent, line dependent, components dependent, and/or temperature dependent. Designing such parameters for a specific load, input and output, components, and temperature may improve single design point efficiency but will not result in maximum efficiency at different conditions, and will not guarantee improvement at that design point because of the components, temperature, and operating point variations. The ability of digital controllers to perform sophisticated algorithms makes it easy to apply adaptive control, where system parameters can be adaptively adjusted in response to system behavior in order to achieve better performance and stability. The use of adaptive control for power electronics is first applied with the Adaptive Frequency Optimization (AFO) method, which presents an auto-tuning adaptive digital controller with maximum efficiency point tracking to optimize DC-DC converter switching frequency. The AFO controller adjusts the DC-DC converter switching frequency while tracking the converter minimum input power point, under variable operating conditions, to find the optimum switching frequency that will result in minimum total loss and thus the maximum efficiency. Implementing variable switching frequencies in digital controllers introduces two main issues, namely, limit cycle oscillation and system instability. Dynamic Limit Cycle Algorithms (DLCA) is a dynamic technique tailored to improve system stability and to reduce limit cycle oscillation under variable switching frequency operation. The convergence speed and stability of AFO algorithm is further improved by presenting the analysis and design of a digital controller with adaptive auto-tuning algorithm that has a variable step size to track and detect the optimum switching frequency for a DC-DC converter. The Variable-Step-Size (VSS) algorithm is theoretically analyzed and developed based on buck DC-DC converter loss model and directed towered improving the convergence speed and accuracy of AFO adaptive loop by adjusting the converter switching frequency with variable step size. Finally, the efficiency of DC-DC converters is a function of several variables. Optimizing single variable alone may not result in maximum or global efficiency point. The issue of adjusting more than one variable at the same time is addressed by the Multivariable Adaptive digital Controller (MVAC). The MVAC is an adaptive method that continuously adjusts the DC-DC converter switching frequency and dead-time at the same time, while tracking the converter minimum input power, to find the maximum global efficiency point under variable conditions. In this research work, all adaptive methods were discussed, theoretically analyzed and its digital control algorithm along with experimental implementations were presented.
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