Practically Prescribed-Time Adaptive Fuzzy Quantized Control of Stochastic Constrained Nonlinear Systems

This paper examines the adaptive fuzzy prescribed-time tracking problem for stochastic nonlinear systems with output constraints, which are affected by actuator dead zones and quantization effects. By introducing an innovative barrier function, this study alleviates the limitations associated with previous barrier functions. Subsequently, a fuzzy logic system was utilised to appropriately handle the non-linear items. Moreover, to address the impact of actuator dead zones and quantization effects, the paper proposes a method that combines adaptive parameterization and partial control policies, effectively approximating and offsetting nonlinear disturbances. Despite the presence of uncertainties, this method ensures that the controlled system remains stable within the prescribed time frame. Finally, a practical example is presented to demonstrate the effectiveness of the proposed theoretical development.