Adaptive neural network fixed‐time stabilization control for high‐order nonlinear systems.

This paper investigates a neural network (NN) adaptive fixed‐time stabilization control problem for high‐order nonlinear systems. Radial basis function NNs (RBFNNs) are used to approximate unknown nonlinear function. A nonlinear filter is designed to solve the issue of “explosion of complexity.” Combining adaptive backstepping design and adding a power integrator, an adaptive NN fixed‐time control method is developed. It is proved that all the signals of the closed‐loop system are bounded in fixed time. Finally, simulation example is provided to confirm the effectiveness of the presented control method. [ABSTRACT FROM AUTHOR]