Adaptive Control of Nonlinear Systems

Adaptive control of nonlinear systems is the study of how to design controllers that can adjust their own behavior in real time when a system's dynamics are uncertain, changing, or subject to unpredictable disturbances. Unlike classical control methods that rely on accurate mathematical models, adaptive approaches — including sliding mode control and neural-network-based schemes — allow robots, aircraft, and other physical systems to maintain stable, precise performance even when key parameters are unknown or shift during operation. Quadrotors and robotic manipulators serve as common testbeds because they combine strong nonlinearities with demanding real-world requirements for speed and reliability. Active research questions include how to guarantee stability within a finite, provably bounded time horizon rather than asymptotically, and how to build disturbance observers that are both computationally lightweight and robust enough to handle structured and unstructured uncertainties simultaneously.

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Keywords

Adaptive ControlSliding Mode ControlDisturbance ObserverNonlinear SystemsFinite-Time StabilityQuadrotor