Nonsingular Predefined Time Adaptive Dynamic Surface Control for Quantized Nonlinear Systems

This article focuses on the singularity-free predefined time control design problem of the quantized nonstrict feedback (NSF) nonlinear systems. Radial basis function neural networks (NNs) are introduced to model the unknown nonlinear dynamics. With the property of the NN basis function, the algebraic loop problem posed by the NSF control structures is addressed. The input quantization is addressed by using the nonlinear decomposition technique. A nonlinear filter with predefined time stability is constructed to decrease the computational complexity. Further, by introducing the designed predefined time filter into the backstepping recursive framework, a predefined time dynamic surface control algorithm is developed, in which the improved adding power integration technique is introduced to prevent control singularities. The Lyapunov theory demonstrates the predefined time stability of the closed-loop quantized nonlinear systems. By the developed control algorithm, the controlled system can effectively track the specified command signal. Both the tracking and filtering errors can reach a small neighborhood around zero within the preset time. The validity and superiority of the proposed control algorithm are illustrated by an actual system simulation.