Adaptive learning-based finite-time performance of nonlinear switched systems with quantization behaviors and unmodeled dynamics

This paper proposes a novel finite-time adaptive learning strategy for a class of nonlinear switched systems with quantization behaviors and unmodeled dynamics under unpredictable switchings. The neural network learning framework is introduced to manifest the characteristics of nonlinear systems to obtain better performances. The system nonlinearities include more complicated non-strict feedback structure and full-state dependent unmodeled dynamics. In virtue of the nonlinear decomposition technique and the finite-time criterion, an adaptive learning strategy is developed step by step. Under the proposed strategy, a common Lyapunov function for all the subsystems is constructed to guarantee that in finite time all the signals of the switched system converge to a small domain near the origin under arbitrary switchings. An illustrative example is finally given to verify the effectiveness of the proposed main results.