According to the nonlinear and time-varying uncertainty characteristics of direct drive electrohydraulic servo control system, a self-tuning fuzzy PID control method with speed change integral and differential ahead optimizing operator is put forward by combining fuzzy inference and traditional PID control in this paper. The rule of fuzzy logic is designed, the membership function of the fuzzy ...

In this paper, temperature monitoring of sterilizing equipment system was established with the help of fuzzy and self tuning fuzzy logic controller designed in LabVIEW software. It combines the advantages of both fuzzy logic and self tuning fuzzy logic controller. The implementation attempts to rectify the errors between the obtained value and the given point which helps to achieve efficient te...

In recent years, there has been an increasing interest on applying advanced and intelligent control designs in power engineering area. As a heuristic optimization technique, Particle Swarm Optimization (PSO) is used for tuning the parameters of the STATCOM. Regulating the DC capacitor voltage in STATCOM is a common task and can improve the system dynamic. The nonlinear control is based on exact...

In this paper, a self-tuning fuzzy sliding-mode controller design approach with decoupled method is proposed. The decoupled method provides a simple way to achieve asymptotic stability for a class of fourth-order nonlinear system. Moreover, heuristic sliding factors are implemented as fuzzy inference. Therefore, the proposed method eliminates the trial-and-error process for finding appropriate ...

A class of modi®ed state space self-tuning controllers of the minimum state error variance type was considered. A suitable chosen structure of the proposed controller allows the tracking of a time-varying reference input and makes a possibility of applying this solution to nonlinear and non-stationar y plants. The advantage in using the proposed algorithm for nonlinear systems’ control is demon...

In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual out...