Adaptive Control of Nonlinear Systems

Adaptive control of nonlinear systems is concerned with designing feedback controllers that can adjust their own parameters in real time to handle systems whose dynamics are uncertain, time-varying, or subject to external disturbances. Robots, aerial vehicles like quadrotors, and robotic manipulators are natural test cases because their governing equations are inherently nonlinear and their operating environments are rarely predictable in advance. Techniques such as sliding mode control, disturbance observers, and neural network-based approximators have emerged as practical tools for achieving reliable, and sometimes provably finite-time, stability under these harsh conditions. Active research questions include how to tighten the gap between theoretical guarantees and real hardware performance, and how to reduce the computational burden of learning-based methods enough that they can run reliably on embedded systems with strict timing constraints.

Works

83,564

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1,462,435

Keywords

Adaptive ControlSliding Mode ControlDisturbance ObserverNonlinear SystemsFinite-Time StabilityQuadrotor