Contents I. Introduction
Over the past few decades, quantized control has attracted an increasing amount of attention and extensive research has been reported [1]–[9]. Early research on quantized control is mainly based on robust control or adaptive control via state feedback. More specifically, in [10], an adaptive quantized controller was designed with a simple clever transformation, in which some rigorous assumptions made on the nonlinear systems of early research were eliminated. For pure-feedback nonlinear systems considering input quantization, an adaptive dynamic surface control scheme with output and state constraints was given in [11]. Owing to the excellent ability in dealing with unknown function, neural network and fuzzy logic systems were utilized to tackle the quantization problem of more complex systems. By defining a preassigned performance function and utilizing the neural network to approximate the completely unknown functions, an adaptive finite-time controller was constructed for strict-feedback nonlinear systems with input quantization in [12].
