Adaptive Control Systems

Adaptive control is what you reach for when your plant's parameters are unknown or drifting in time and a fixed-gain controller just won't track — the course is really about designing controllers that learn the system as they run, and proving via Lyapunov arguments that the closed loop stays stable while they do. You'll work through six problem sets, a midterm and final, and a term project, deriving identification and MRAC laws for state-feedback and output-feedback cases, then bolting on robustness for disturbances, time-varying parameters, and unmodeled dynamics. It builds directly on classical and nonlinear control (Slotine/Li territory), and matters because almost every real plant — aircraft, robots, processes — eventually violates the assumptions your ME 460-level design rested on.