|Approximation-based Adaptive Tracking of Uncertain Input-Quantized Nonlinear Systems in the Presence of Unknown Quantization Parameters and Control Directions
Yun Ho Choi and Sung Jin Yoo*
International Journal of Control, Automation, and Systems, vol. 15, no. 3, pp.1414-1424, 2017
Abstract : "This paper investigates an adaptive tracking problem of input-quantized strict-feedback nonlinear systems
with unknown quantization parameters, unmatched nonlinearities, and control directions. The hysteresis quantizer
is considered to quantize the control input. Compared with the existing literature related to the input quantization,
the main contribution of this paper is to design an adaptive neural network control scheme without requiring
the exact knowledge of both the quantization parameters of the hysteresis quantizer and the control directions. A
low-pass filter is employed to to overcome an algebraic-loop problem of the control input caused by the quantization
error depending on the unknown quantization parameters and the control input. Furthermore, in order to deal with
the unknown control direction problem in the presence of unknown quantization parameters, a bounding lemma
for the parameter of Nussbaum gain function is presented in the dynamic surface design framework. The stability
problem of the proposed adaptive control scheme is thoroughly investigated in the Lyapunov sense."
Dynamic surface design, quantized control, unknown quantization parameters, unmatched uncertainties.