PROJECT TITLE :

DOB Fuzzy Controller Design for Non-Gaussian Stochastic Distribution Systems Using Two-Step Fuzzy Identification

ABSTRACT:

This paper presents a completely unique non-Gaussian stochastic control framework for the problem of disturbance estimation and rejection by combining fuzzy identification technology with disturbance observer style. Initial, Fuzzy Logic models are used to approximate the output chance density functions (PDFs) of non-Gaussian processes such that the task of PDF form control will be reduced to a fuzzy weight dynamics modeling and management downside. Next, Takagi-Sugeno fuzzy models with multiple disturbances are employed to explain the nonlinear relations between fuzzy weight dynamics and therefore the control input, in that a completely unique disturbance-observer-based PI-sort fuzzy feedback controller is meant to ensure the system stability and convergence of the tracking error to zero. Meanwhile, the disturbance estimation and attenuation performance furthermore because the state constrained requirement can additionally be guaranteed. Moreover, the novel composite observer is constructed by augmenting the disturbance estimation into the full-state estimation. The satisfactory tracking performance and full-state observation effect will be achieved by the designed optimization algorithm. Finally, simulations for paper-making process are given to show the efficiency of the proposed approach.