Yongduan Song

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.

In this article, a prescribed-time output feedback controller is proposed for a class of uncertain nonlinear systems with unknown control coefficients and mismatched nonvanishing disturbances. Both unknown control coefficients and mismatched disturbances are tricky to address by the existing prescribed-time output feedback control frameworks. Differently, a novel prescribed-time control criterion in conjunction with Nussbaum functions is proposed, and prescribed-time stability is achieved. Furthermore, design methods for a state observer and a prescribed-time output feedback controller are developed. With the proposed control design, both the system output and observer errors are rigorously proved to converge to zero within a prescribed time. Moreover, the unified prescribed performance (UPP) of the system output and the satisfaction of output constraints are simultaneously achieved. Numerical simulations and experiments are provided to illustrate the effectiveness of the proposed control design.