Takagi-Sugeno Integrated Fuzzy System in Subsurface Identification

This study investigates the possibility of using the rule-based fuzzy (FZ) inference method to analyse petrophysical data (DT). Some well logs (WL) DT provided by Shell Producing Development Company (SPDC), Nigeria, were utilised for this study. The exploration WL DT were clustered using an unsupervised neural network. The rule-based lithology (LTG) procedures were established from the training DT sets, and the procedure strength is weighted. The Takagi-Sugeno inference arrangement and the centroid of extent defuzzification technique were employed for the FZ inference. It was observed that FZ inference systems provide fast and comprehensive details of the LTG and fluid content of the subsurface structure of the petrophysical DT that was interpreted

Robust predictive tracking control for a class of nonlinear systems

A robust predictive tracking control (RPTC) approach is developed in this paper to deal with a class of nonlinear SISO systems. To improve the control performance, the RPTC architecture mainly consists of a robust fuzzy PID (RFPID)-based control module and a robust PI grey model (RPIGM)-based prediction module. The RFPID functions as the main control unit to drive the system to desired goals. The control gains are online optimized by neural network-based fuzzy tuners. Meanwhile using grey and neural network theories, the RPIGM is designed with two tasks: to forecast the future system output which is fed to the RFPID to optimize the controller parameters ahead of time; and to estimate the impacts of noises and disturbances on the system performance in order to create properly a compensating control signal. Furthermore, a fuzzy grey cognitive map (FGCM)-based decision tool is built to regulate the RPIGM prediction step size to maximize the control efforts. Convergences of both the predictor and controller are theoretically guaranteed by Lyapunov stability conditions. The effectiveness of the proposed RPTC approach has been proved through real-time experiments on a nonlinear SISO system