Practical Predefined‐Time Stabilization of Uncertain Nonlinear Systems Via Neuroadaptive Time‐Varying Feedback Control

ABSTRACT This paper proposes a control approach for achieving practical predefined‐time stabilization in a class of nonlinear systems influenced by matched bounded disturbances and unknown nonlinearities. Radial basis function (RBF) neural networks (NNs) are used for function approximation to tackle these uncertainties. By applying Lyapunov stability theory, we show that the proposed time‐varying control law and adaptive mechanism ensure the convergence of the system states to a region around the equilibrium within a predefined time, while all closed‐loop signals remain bounded. The effectiveness of the approach is validated through simulations on a single‐link flexible‐joint robotic manipulator.