Adaptive quantized fuzzy control of stochastic nonlinear systems with actuator dead-zone

This paper studies a tracking issue of stochastic nonlinear quantized systems with actuator dead zone. By combing a sector-bounded property of a hysteretic quantizer and a simplified dead zone model, a novel connection between control signal and system input is established. Based on this connection, the stochastic nonlinear quantized control is transformed into the conventional stochastic nonlinear control with unknown control gain and bounded perturbation. Therefore, the control difficulty is overcome, which results from the coexistence of the unknown actuator dead zone and the quantization effect of the control signal. Then, fuzzy logic systems are utilized to cope with the unknown composite nonlinear functions including the bounded perturbation, and an adaptive learning mechanism is set up to compensate the unknown control gain. Hence, a refreshing adaptive fuzzy tracking control scheme is formed to achieve a desired tracking performance.