Many dynamic systems to be controlled have both parametric and dynamic uncertainties. For instance, robot manipulators may carry large objects with unknown inertial parameters. Power systems may be subjected to large variations in loading conditions. Fire-fighting aircraft may experience considerable mass changes as they load and unload large quantities of water. Adaptive control theory is motivated by similar examples and offers solutions for controlling systems in the presence of uncertainties. This course presents a rigorous mathematical foundation for the synthesis and analysis of robust adaptive control systems. It covers fundamentals of Lyapunov stability theory and robust control, presents methods of direct and indirect model reference adaptive control, and places the focus on L1 adaptive control. Various examples will be discussed throughout the course to illustrate the results.
Classical Control is the set of methods and procedures for designing control systems that were developed around the time of the World Wars, ending in 1960. These types of controllers are still some of the most used today because of their simplicity and the maturity of the design methods. Development of these control systems took place in research labs such as Bell Labs, the MIT Radiation Lab, and Military Labs. Many applications still use Classical Control as the basis for controlling systems for practical and historical reasons. For many simple applications, classical design methods are quick and easy to use with well-known metrics for performance, robustness, and stability. Historically fields such as aerospace engineering have their roots in the design of aircraft using classical control techniques and continue to use many methods, if not for design then as a method of describing the systems simply and concisely. In order to work with control systems, a solid understanding of classical control is necessary. Far from being out-dated, it is still used in many applications and is the source of much of the language and metrics used to describe even the most complicated systems.
