Genetic Algorithms and designing membership function in Fuzzy Logic Controllers

This study investigates the use of Genetic Algorithms (GA) to the design and implementation of Fuzzy Logic Controllers (FLC). A fuzzy logic is fully defined by its membership function. What is the best to determine the membership function is the first question that has been tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function generated by human operators. The membership function selection process is done with trial and error and it runs step by step which is too long in completing the problem. This research develops a system that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in completing the problems. The data collection is based on the simulation results and the results refer to the transient response specification is maximum overshoot. From the results presented, the system which we developed is very helpful to determine membership function and it is clear that GA is very promising in improving the performance of the FLC to get more accurate in order to find the optimum result.

In the problem of designing a Fuzzy Logic Controller (FLC) for robots in general and a Differential Drive Robot (DDR) in particular, the determination of the parameters of Membership Functions (MFs) and Fuzzy Rules (FRs) is a difficult, complicated, and time-consuming problem because this is mainly based on heuristics and the knowledge of experts. Therefore, this paper provides a new method to efficiently design an FLC for the DDR by using the Genetic Algorithm (GA). Here, the GA is used to generate and optimize both the parameters of MFs and the FRs according to the minimum kinetic energy loss criterion. For this purpose, a program is created in Google Colab® by using the Python language with the help of the “Pymoo” library to not only automatically generate all the suboptimal parameters of MFs and the suboptimal FRs but also the simulate and evaluate different used FLCs. This program is published as an open-source code so that all readers can browse, view, run, and modify the code themselves to design their FLC. The simulation results have shown that the designed FLC is much better than other used FLCs in terms of the minimum kinetic energy loss while other control performances are still good.

This paper presents the design of a fuzzy logic controller for a process air heater system which produced an overdamped temperature response. The process air heater system is modelled mathematically and simulated using MATLAB/Simulink. Five types of membership functions (Gaussian, triangular, trapezoidal, combined trapezoid and triangular and generalized Bell) with four different sizes of rule base (3×3, 5×5, 7×7 and 9×9) were designed and tested. The performances of the fuzzy logic controllers were compared based on maximum overshoot, settling time and integral of absolute error. The results showed that fuzzy logic controller with combined trapezoid and triangular membership function with 3×3 rule base can achieve the desired response.

Design of fuzzy logic controller for smart base isolation system using genetic algorithm

Braking system plays a major safety feature in any vehicle; it is considered as one of the key points in safe motoring. The main objective of a good braking system is to provide safe stopping, in addition to the fact that it provides repeatable stopping. Anti-lock Braking System (ABS) is one of the recently developed safety feature used in a vehicle in order to prevent wheels from locking. This article proposes an intelligent adaptive fuzzy logic (FL) genetic algorithm (GA) controller for the Anti-Lock Braking System (ABS) to control the braking of a vehicle by controlling the slip ratio based on quarter car model. The main objective of the proposed controller is to prevent wheels from locking, which is a major safety issue in any braking system for different road conditions. The proposed controller consists of a Fuzzy Logic (FL) Genetic Algorithm (GA) based controller. The Genetic Algorithm is utilized in tuning the membership functions of the Fuzzy logic controller, which is developed to calculate the amount of reduction of the applied torque due to applying brake based on the error of the measured slip ratio signal compared with the desired one. Then a Genetic Algorithm (GA) is utilized in tuning the Fuzzy Logic parameters, which are the center of the Gaussian membership function, and the spread (б). The simulation results of the proposed fuzzy logic genetic algorithm controller are compared with both the results of the Fuzzy logic controller and the PD controller developed in previous work of the authors. The simulation results present many advantages of using Fuzzy Logic Genetic Algorithm based controller over both the Fuzzy Logic and the PD controllers, and the simulation is carried out at different road conditions, namely dry, wet and icy road surface.

Genetic algorithm is heuristic searching algorithm which based on nature selection of mechanism and nature genetic. The basic concept that inspires the genetic algorithm is that evolution theory. In the process of searching the best solution on classic genetic algorithm often occurs optimum local. Optimum local is a common problem which is often occurs in genetic algorithm, and one of the reasons is that because of the population diversity. If population diversity is too low is led to optimum local, and if it is too high caused more times to look for the best solution. Mutation operator plays an important role in the process of genetic algorithm to manage that population diversity and an important element of mutation operator is mutation rate. On classic genetic algorithm that mutation rate is set in the beginning while the process of genetic algorithm depends on how many generations are. Therefore is needed to control mutation rate in generation. Controlling mutation operator, especially mutation rate based on Fuzzy Logic Controller (FLC) to manage population diversity, not too high or too low, in order to get optimal result. Evaluation is done 10 times execution by comparing the performance of standard genetic algorithm (GA), IAG and genetic algorithm based on Fuzzy Logic Controller (FLC) and experimental results show that there is an improvement on genetic algorithm performance based on FLC. Keywords: Genetic Algorithm, Fuzzy Logic Controller (FLC), Mutation Rate

This paper focuses on the Genetic Algorithm learning paradigm applied to train the ANNs for balancing the cart-pole balancing system. The studied system is a classic control problem namely “cart-pole” problem. We will apply the unconventional techniques Artificial Neural Network, Genetic Algorithm and Fuzzy Logic to a classic control problem "cart-pole”. In this paper we have tried to train the Artificial Neural Network (ANN) with using Genetic Algorithms (program is written in MATLAB) which is compared with the output obtained using the Artificial Neural Network Toolbox provided in MATLAB. In proposed approach we have used both ANNs and Genetic Algorithm to get more optimal solution. Here we applied the approach for the Fuzzy logic technique to design a Fuzzy Logic Controllers (FLC) using ANNs and Genetic Algorithm (GA). The Fuzzy rules which are needed to control the problem will be framed with the combination of Artificial Neural Networks and Genetic Algorithm. It has been found that such a searching technique converges intelligently and much faster than conventional learning means. Performance of the presented neural network training using the genetic algorihtms is much better and providing more accurate results.

In the present paper, a Multi-Segment P/f Droop Control Strategy is developed to achieve localized control, coordination and power management of different sources in an islanded microgrid which consists of two PV/Battery storage Hybrid units and a droop unit. Separate control loops are designed, each for PV power production control, Battery charging and discharging level control, and Droop unit operation control based on multi-segment P/f Droop characteristics. The conventional PI controllers can be used in the control loop design as they are very simple for implementation and give a better dynamic response. But their performance deteriorates when the complexity in the system increases due to disturbances like load variations and intermittent sources. Fuzzy Logic Control (FLC) technique is model-independent and a flexible tool. It can deal with complex systems such as microgrids with different types of imprecise inputs and variables particularly if the power is supplied by intermittent sources and is also consumed by varying and unpredictable loads during sudden disturbances. The Membership functions (MF) of a conventional FLC are determined by trial and error method. This method is time-consuming and does not guarantee an optimal controller. In the present paper, a Self-Adaptive Fuzzy Logic Droop Controller (SAFLDC) is proposed, whose input–output MFs are made adaptive by obtaining the cluster centers using Self-Organizing Maps (SOM) Algorithm. The paper aims to follow a systematic approach to propose an effective SAFLDC with optimal MFs whose parameters are tuned using Hybrid GA-PSO. The developed strategy has been validated using detailed switching models in MATLAB/SIMULINK tool and the behavior of key variables in the strategy is illustrated.

In this study, interval type2 fuzzy logic (IT2FL) and PID controller is designed for swing-up position control of double inverted pendulum (DIP) system. The double inverted pendulum system consists of two rigid bars connected by a revolute joint. Mass of the revolute joint is included in the dynamic model.Rigid bars in the system are assumed to experience planar motion. The pendulum system is connected to the base by means of a revolute joint. Torque provided through a motor mounted to the base is used for position control of the system. PID (Proportional-Derivative-Integral) and interval type2 fuzzy logic controllers are developed by using the same performance criteria for position control of double inverted pendulum system. IT2FL controller is similar with type1 fuzzy logic controller. IT2FL system provides soft decision boundaries, whereas a type-1 fuzzy logic system provides a hard decision boundary. Membership function in interval type2 fuzzy logic set as an area called Footprint of Uncertainty (FOU) which limited by two type1 membership function those are upper membership function (UMF) and lower membership function (LMF).System behaviour is obtained by computer simulation using developed controllers respectively. Computer simulation results are compared in order to evaluate applicability of developed controllers. MATLAB/Simulink software is used in computer simulations.

In this paper, design and simulation of a current-mode CMOS Fuzzy Logic Controller (FLC) is presented. The proposed controller is composed of Membership Function Circuit (MFC), fuzzy inference engine with min-product method and current-mode defuzzification circuit. MFC has a new architecture that generates basic four membership functions (trapezoidal, triangle, Z-shape and S-shape). These membership functions are simply tuneable by adjusted two voltages via setting some analogue switches in a shape/label select circuit block which is called programming unit. Also, a novelty designed current-mode defuzzification circuit is based on current-mode four-quadrant multipliers that have been constructed simple current squarer and substractor circuits. The proposed current-mode CMOS FLC has been simulated and verified for two inputs, one output and nine tuneable fuzzy rules by using PSPICE program with MIETEC 1.2 µm design parameters. The proposed architecture has an operation speed of 6.25 MFLIPS (6.25 × 106 Fuzzy Logic Inference per Second).

Power system stabilizers (PSS) has been widely used to enhance damping due to the electromechanical low frequency oscillations occurrence in power systems. In this paper, a new method is used for the online tuning of parameters of conventional power system stabilizers (CPSS) using fuzzy logic. Fuzzy logic enables mathematical modeling and computation of some nonlinear parameters of the system, which are usually derived empirically by utilization of expert knowledge rules. Various literatures has shown that fuzzy logic controller is one of the most useful methods for expert knowledge utilization. This type of controller is adaptive in nature and can be used successfully as a power system stabilizer. The design of fuzzy logic controllers is mainly based on fuzzy rules and input/output membership functions. Simple and efficient clustering algorithms allow data classification in distinct groups using distance and/or similarity functions. In the present paper, the optimum generation of fuzzy rules base using Fuzzy C-means (FCM) clustering technique is used. In fact, data are classified and the number of fuzzy rules which depends on convergence radius is determined. Finally, the performance of proposed FCM controller is compared with that of conventional controller. The active power, reactive power and bus voltages used as inputs to the fuzzy logic network based power system stabilizer and the parameters of the optimum stabilizer , i.e. gain factor as well as time constants of the lead/lag compensator, are the outputs of the proposed system. The design method has been successfully implemented on a single machine power system connected to an infinite bus over various operating conditions.

Proposes a CMAC-based FLC (fuzzy logic controller) with a fast learning capability and an accurate approximation ability. The proposed CMAC-based FLC has the fast learning capability because it pursues the local generalization and only a small number of activated units in the network participate in the forward and backward computation. It also produces an accurate input-output approximation ability because it adjusts the membership function's model parameters of the input and output variables simultaneously and it considers both centers and widths of output membership functions to compute a crisp defuzzified value. Application to the truck backer-upper control problem of the proposed CMAC-based FLC is presented. Simulation results validate the fast learning and the accurate approximation of the proposed CMAC-based FLC.

REINFORCEMENT LEARNING OF FUZZY LOGIC CONTROLLERS FOR QUADRUPED

Soft computing techniques including fuzzy logic have been successfully applied to Wireless Body Area Networks (WBANs). However, most of the existing research works rely on manual design of the Fuzzy Logic Controller (FLC). To address this issue, in this paper, we propose to use Evolutionary Computation (EC) techniques to automate the design of FLCs for cross layer medium access control in WBANs. With the goal of improving network reliability while keeping the communication delay at a low level, we have particularly experimented on three different coding schemes. The influence of fitness functions has also been examined carefully in order to achieve a good balance between reliability and performance. We have also evaluated the effectiveness of two widely used evolutionary algorithms. Particularly, Particle Swarm Optimisation (PSO) is shown to be more effective than Differential Evolution (DE) for our design problem. Moreover, the FLC designed by our approach is also shown to outperform some related algorithms as well as the IEEE 802.15.4 standard.

Fuzzy logic controllers suffer from rule explosion problem as the number of rules increases exponentially with the number of input variables. Although several methods have been proposed for eliminating the combinatorial rule explosion problem, none of them is fully satisfactory. In this paper, we describe a new adaptive method for the design of cascaded layer based hierarchical fuzzy system with high input dimensions. This new adaptive hierarchical architecture could be applied to dimensionality reduction in fuzzy modeling. An evolutionary algorithm based off-line leaning algorithm is employed to generate the fuzzy rules and their corresponding membership functions. The evolutionary learning paradigm is a powerful tool to tune the fuzzy logic controllers since it requires no prior knowledge about the system's behavior in order to formulate a set of functional control rules through adaptive learning. The resulting hierarchical fuzzy system has not only an equivalent approximation capability, but less number of fuzzy rules compared with the conventional fuzzy logic system. Simulation studies exhibit competing results with high accuracy that illustrating the effectiveness of the approach.