Design and Implementation of an Optimal Fuzzy Logic Controller Using Genetic Algorithm

Abstract

All control systems suffer from problems related to undesirable overshoot, longer settling times and vibrations while going form one state to another state. Most of relevant techniques had been in the form of suggesting modification and improvement in the instrumentation or interfacing part of the control system and the results reported, remain suffering from shortcomings related to hardware parameter dependence and maintenance and operational complexities. Present study was based on a software approach which was focusing on an algorithmic approach for programming a PIC16F877A microcontroller, for eliminating altogether the parametric dependence issues while adding the benefits of easier modification to suit a given control system to changing operational conditions. Said approach was first simulated using MATLAB/SIMULINK using the techniques of Proportional Derivative Fuzzy Logic Controller (PD-FLC) whose membership function, fuzzy logic rules and scaling gains were optimized by the genetic algorithm technique. Simulated results were verified by programming the PIC16F877A microcontroller with the algorithm and using it on a temperature control system where a fan was regulated in response to variations in the ambient system temperature. Resulting tabulated performance indices showed a considerable improvement in rising and settling time besides reducing overshoot and steady state error

Optimal Control Algorithms for Second Order Systems

“…Such algorithm works on a suitable genetic-neuro-fuzzy controller. The target of these different approaches is also to improve the simulation results shown in (Khan et al, 2008).…”

“…After 20 generations, the optimal fuzzy sets of (Khan et al, 2008;Kumar and Garg, 2004;Chegeni et al, 2007;Pelusi, 2011b) have proposed GA techniques to achieve optimal fuzzy rules. Therefore, the above optimization algorithm is also used to find the fuzzy rules with the higher weight.…”

“…In order to verify the robustness of the model, we consider three plants with different transfer functions. The first one is typically used to approximate the working of DC motors (Khan et al, 2008) …”

“…In these situations, a huge computational time is necessary. In order to overcome such difficulty, Genetic Algorithms (GA) are applied to fuzzy controllers with good results (Khan et al, 2008;Kumar and Garg, 2004;Chegeni et al, 2007;Pelusi, 2011c). Such genetic methods are useful approaches for problems that require efficient searching.…”

“…Such genetic methods are useful approaches for problems that require efficient searching. Khan et al (2008) the membership functions and the fuzzy logic rules were optimized through Genetic Algorithms methods for a temperature control system. Thanks to GA, Leng et al (2006) eliminated the limitation on symmetric membership functions and symmetric fuzzy rules.…”