Adaptive Global Asymptotic Output Stabilization of Uncertain Nonlinear Systems Under Dynamic State/Input Quantization

This article proposes a novel adaptive backstepping control strategy for nonlinear systems subject to mismatched uncertainties under both dynamic input and state quantization, achieving global asymptotic convergence of the output. Compared to existing research in this field, the innovations of this work lie in: 1) achieving global instead of semi-global stability, while providing explicit control parameter tuning guidelines; 2) ensuring system output asymptotic convergence to the origin, rather than merely guaranteeing ultimate uniform boundedness; 3) implementing a dual-channel dynamic quantization mechanism enabling online adaptive adjustment of quantization parameters; and 4) designing higher-order filters to generate smooth, sufficiently differentiable state estimates, resolving the nondifferentiability issue in virtual control laws induced by state quantization. Numerical simulations validate the effectiveness and advantages of the proposed quantized control approach.