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This thesis is composed of two parts. The first part of the thesis focuses on studying inversion-based output tracking control and learning control for nonminimum phase systems. The second part of the thesis focuses on RF CMOS LNA and mixer design. The nonminimum phase property has long been recognized as a major obstacle in many control problems. In part one, we introduce a new design procedure for output tracking control of nonminimum phase systems. We provide a causal inversion solution for general nonlinear systems. By using the scaling property, we present a causal in­ version solution such that the causal state and input trajectories track those obtained by stable inversion approach for linear systems. This new controller achieves stable e— tracking. In contrast to stable inversion, the causal inversion approach does not require precalculation. In contrast to nonlinear regulation, the causal inversion approach avoids the numerical intractability of solving nonlinear PDEs. As an example of the appli­ cation, a causal inversion-based controller is designed for tip trajectory tracking of a one-link flexible manipulator. Inversion-based adaptive and robust learning algorithms are developed for unstable nonminimum phase systems. Fast growth of personal communication market puts a high demand on the production of low cost and low power transceivers for wireless applications. In part two, we present a design of a CMOS low noise amplifier and does its sensitivity analysis which is beneficial for making appropriate design trade offs. We also propose a novel low voltage downconversion mixer design. As an example, all the circuits have been designed at 5.8 GHz and integrated in a TSMC 0.18 jim. CMOS process. These front-end circuit designs can be used for low voltage and low power wireless applications.

Inversion-based output tracking control and learning control for nonminimum phase systems and RF CMOS LNA and mixer design