Contents 1 Introduction
Quantized control has attracted growing attention due to its outstanding performance under communication resources constraints, and a number of typical control schemes have been reported, among which both linear and nonlinear systems that using robust approach and adaptive approach have been studied [1–9]. Specifically, by introducing a transformation to the quantized controller, without any limitations on nonlinear functions of the system, an adaptive state-feedback stabilization method was given in [10]. Later in [11], the adaptive state-feedback tracking case with saturated input quantization was investigated. In [12], the issue of decentralized control was investigated for interconnected nonlinear systems proceed by quantized input. Different from other quantized control strategies via output feedback, the quantized input signal was applied to design the state observer that makes the controller design easier. Recently, motivated by the excellent ability of tackling completely unknown nonlinearity, neural networks and fuzzy logic systems have been widely used in quantized feedback control systems. In [13], for pure-feedback systems with unmodeled dynamics and quantized input, an adaptive neural control strategy considering both state and output constraints was proposed. While in [14], the fuzzy adaptive fault-tolerant control problem was addressed for nonstrict-feedback systems proceed by quantized input.
