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

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