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The $H_{\infty}$ norm is a measure of the worst-case gain of a transfer function. In robust control, it is used to minimize the worst-case effect of disturbances on system performance, ensuring the controller's ability to handle the highest levels of uncertainty.

Uncertainty in robust control refers to the unpredictable variations in system parameters or external disturbances. It is important for the design of robust controllers to account for such uncertainties, ensuring consistent system performance under a variety of conditions.

Bode's Ideal Transfer Function outlines desired characteristics for a robust control system, such as proper phase and gain margins. Robust controller design uses this reference to shape the frequency response ensuring robustness.

Integral Quadratic Constraints (IQC) provide a framework to analyze stability and performance with uncertainty. IQC is vital in robust control for constructing bounds on uncertain elements influencing the system's behavior.

The Nyquist Stability Criterion is a graphical tool to determine the stability of a control system. Robust controller design utilizes this criterion to ensure the system remains stable for all permissible uncertainties.

Stability margin quantifies how much uncertainty a system can tolerate before becoming unstable. For robust control, it is crucial to design controllers with sufficient stability margins to preserve system stability despite uncertainties.

Gain scheduling involves switching between different controller settings based on the current operating conditions. It enhances robustness by adapting the controller to perform optimally across a range of conditions.

Lyapunov Stability Theory offers methods to assess system stability using energy-like functions called Lyapunov functions. In robust control, it is employed to show that a system remains stable regardless of uncertainties.

Structural stability refers to a system's capacity to maintain stability under small structural perturbations. It's important for robust control to ensure that minor model alterations don't compromise stability.

Parameter Space Methods involve mapping parameter variations to analyze control system robustness. These methods are important for identifying the robust stability and performance regions within the parameter space.

Passivity-Based Control focuses on ensuring the system exhibits passive behavior to enhance robustness. It's crucial in robust control for designing systems that dissipate energy and are thus less sensitive to model uncertainties.

Worst-Case Design in robust control is the practice of designing controllers to cope with the most adverse conditions anticipated. This approach is fundamental for ensuring system performance doesn't degrade below acceptable levels amid uncertainties.

Kharitonov's Theorem provides criteria to determine the stability of a family of polynomials representing uncertain systems. It is fundamental for verifying robust stability in the design of controllers.

Pole placement is a control strategy that assigns the closed-loop poles in specific locations. In robust control, pole placement helps achieve desired dynamic characteristics while taking into account system uncertainties.