Abstract
The successful application of fuzzy control depends to a large extent on the parameters of some subjective decisions, such as fuzzy membership function (MF). Fuzzy logic controller (FLC) implementing augmented output MFs as compare to input MFs is presented to improve the accuracy, robustness, and performance of the system. The best possible combination of input and output MFs is introduced to distribute the uniform input MFs and augmented output MFs in the treatise. The simulation of the 2-Inputs 1-Output Fuzzy Control System is performed in many nonlinear processes. Then, the experimental outcomes of the uniformly and augmented distributed output MFs are compared under similar circumstances. The experimental outcomes are in a virtuous covenant with the simulation outcomes. The experimental outcomes show that the root mean square error (RMSE) is reduced around 75.3% and bringing down the relative error to the acceptable range (≤±10%). The control accuracy is improved and the robustness is boosted by reducing the RMSE through the FLC with augmented-distributed output MFs. Moreover, the cost and energy efficiency in any fuzzy system will be improved by implementing the augmented-distributed output MFs using the best possible combination of input and output MFs.
Highlights
Fuzzy control systems (FCS) enable humans to make decisions and control real-time applications based on inaccurate linguistic information
SYSTEM MODEL OVERVIEW The fuzzy model is designed for a general control system that takes inputs from two sensors i.e. Sensor One (SN-1) and Sensor Two (SN-2) and provides a pulse width modulated (PWM) output signal as shown in Fig.1 Since most of the modern sensors provide transistor-transistor logic (TTL) compatible output voltage levels (i.e. 0 – 5 V), these two sensors (SN-1 and SN-2) can be assumed to measure any physical parameter e.g. temperature, pressure, humidity etc
WORK The main purpose of this research is to find the best possible relationship that exists between the number of input and output membership function (MF) and the stability of the controller, i.e. the speed required for the controller to reach a steady state
Summary
Fuzzy control systems (FCS) enable humans to make decisions and control real-time applications based on inaccurate linguistic information. Khokhar et al.: Simple Tuning Algorithm of Augmented Fuzzy MFs optimization (PSO) [6], ant colony algorithm (ACO) [7] and genetic algorithm (GA) [8]. These algorithms are very time consuming since complex computation is required for convergence [8]–[10]. We have presented a new and simple algorithm for the tuning of MFs. It is shown that, for a given monotonic system, by determining the effects of relative variation in the positions of output MFs peaks and incorporating the results in the de-fuzzification process, accurate results can be achieved.
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