Position Control of a Pneumatic Drive Using a Fuzzy Controller with an Analytic Activation Function.

Željko Šitum,Danko Ćorić

doi:10.3390/s22031004

Abstract

The fuzzy logic controller, which uses an analytic activation function for the defuzzification procedure, was applied to the position control of a servo pneumatic drive controlled by a proportional valve. The Gaussian shape of input fuzzy sets, with the possibility of their modification, was used to fuzzify the input signal. The control signal was determined by introducing an analytic function instead of defining the fuzzy rule base. In this way, a conventional 2-D fuzzy rule table base is modified into 1-D fuzzy defuzzification based on an analytic function to calculate the controller output. In this control algorithm, the problem of conventional fuzzy logic control, in terms of the exponential growth in rules as the number of input variables increases, is eliminated. The synthesis controller procedure is adjusted to the flow rate characteristic of the proportional valve. The developed control algorithms are verified by computer simulation and by testing on a real pneumatic rodless cylindrical drive.

Highlights

Fuzzy logic control (FLC) can be used to improve an existing classical controller solution by adding an extra layer of “intelligence” to the control strategy
The fuzzy logic controller, which uses an analytic activation function for the defuzzification procedure, was applied to the position control of a servo pneumatic drive controlled by a proportional valve
We proposed the input domain distribution to 5 fuzzy sets with the center positions of membership functions is zero

Summary

Introduction

Fuzzy logic control (FLC) can be used to improve an existing classical controller solution by adding an extra layer of “intelligence” to the control strategy. It is usually a difficult task to optimize fuzzy membership functions and rule base. To avoid such difficulties, some design techniques based on a self-organizing fuzzy controller [4,5] or synthesis of fuzzy and neural networks [6,7,8] have been proposed. With a large enough fuzzy rule base and input variables, any unknown function can be approximated, i.e., any shape of the input-tooutput mapping surface and adopted controller action to process characteristics [12]. Increasing the input variables will exponentially increase the fuzzy rule base It is, necessary to find ways to cope with this inconvenient problem in the realization and implementation of the fuzzy logic controller. A large fuzzy rule base can cause problems in computing and the practical realization of the control algorithm

Methods

Results

Discussion

Conclusion