Abstract
In modern wireless systems, the demand for reconfigurable radio frequency (RF) electricalcomponent and adaptive antenna or phased array have increased, requiring components and
antennas with high power handling capability, a wide tuning range, and reconfigurability. For
wireless power transmission (WPT), high power RF front ends are required to have the high
tolerance for power dissipation related thermal issue. Conventional tunable technique based on
traditional components often have limited power handling, mechanical stiffness, thermal
sensitivity, and narrow reliability margins. Moreover, beam steering range and narrow bandwidth
for transmitter and receiver ends are another challenges for antenna phased array design,
especially in the HF/VHF/UHF bands. In this dissertation, a general framework for soft robotic
enabled RF electrical component design, shape-changing antenna and antenna array design,
and non-Foster matched electrically small antenna (ESA) investigation are presented starting
with background and motivation introduction in Chapter 1.
Chapter 2 focuses on RF reconfigurable component design. Contributions include the eval
uation of conventional tunable high power technique, especially for binary weighted capacitor
bank based on commercial high power PIN diode switch. A pneumatic actuating soft robotic
enabled tunable high power inductor was proposed with the ability to realize large inductance
tuning ratio. In Chapter 3, shape-changing antennas and arrays design was proposed to realize
wide beam steering with acceptable gain level. A machine learning (ML) assisted parameter
synthesis tool was developed to predict phase difference between array elements with desired
main beam direction and maximum gain. A numerical method to estimate the directivity upper
bounds of arbitrary shape antennas based on the effective aperture. The rest of this chapter
investigates plasma antenna indirect feeding techniques and presents optimized solution. Chapter
4 mainly focuses on non-Foster matched ESAs with numerical analysis between the achieved
bandwidth, the order of matching elements and the number of antennas.
In general, these projects thoroughly cover the analysis of the RF electrical circuit and
antenna theory, contributing to the design of reconfigurable, high power, and mechanically
adaptive RF systems. The results presented provide new fundamental understanding as well
as practical tools to enable the implementation of soft robotics, Machine Learning (ML), and
flexible materials in next-generation antennas and RF components. This research also shows
feasibility for commercial products which requires low cost, easy production, control and
integration specifications in wireless communication market.