The developed fuzzy logic uses the minmax compositional rule of inference. A new hybrid algorithm based on optimal fuzzy controller in. By using fuzzy logic controller, there are three benefits contributed, those are. The ga is used to determine all the parameters of the membership functions of the fuzzy controller.
This paper develops a genetic algorithm ga based adaptive fuzzy logic controller flc with fourparameter for the speed control of switched reluctance motor srm drive. In this paper, a fuzzy pid controller, is investigated on the basis of genetic algorithms gas to eliminate the undershoot of a nonminimumphase nmp. This intelligent control strategy combines adaptive rule with fuzzy and sliding mode control algorithms. Gas are search algorithms based on the mechanics of.
Based on the above reasons, we have used the generalized inverse theories to design the ts fuzzy controller, which is based on the lyapunov equations 68. In this paper, an adaptive fuzzy pid controller based on genetic algorithm is designed. Output of flc is presented by u c k 3, where c is coefficient of damper, k 3 is scaling factor of output. A flc consists of a set of control rules, each rule is taken for a given process condition as. Fuzzy logic controller flc provides an alternative to pid controller, especially when the available system models are inexact or unavailable. Evolving fuzzy rule based controllers using genetic algorithms.
Gabased pid controller improved seat value, vdv ratio, and crest factor as compared to the passive system and classical pid controller. However, a systematic method for designing and tuning the fuzzy logic controller is not developed yet. Experimental and simulation results are presented that validate the proposed approach. Gabased neural fuzzy control of flexiblelink manipulators. Index termsfuzzy logic controller, rules generation, genetic algorithm, crossover operator. Genetic algorithm has its roots originated from the genetic science which is a biological phenomenon. Block diagram of the real coded ga based fuzzy controller consisting two inputs e 1,e 2 and one output u is shown in figure 2. Use of the genetic algorithmbased fuzzy logic controller for loadfrequency control in a two area interconnected power system. Membership functions within fuzzy hobic are tuned using a genetic algorithm ga.
As a result, a fixed parameter controller based on the classical control theory such as pi or leadlag controller 58 is not certainly suitable for the upfc damping control methods. Abstractthe limitations of conventional modelbased control mechanisms for flexible manipulator systems have stimulated the development of intelligent control mechanisms incorporating fuzzy logic and neural networks. The hobic is then extended using the fuzzy logic theory. In this paper, we describe a method of stability analysis for a ga based reference.
A genetic algorithm optimised fuzzy logic controller for. A twostep approach is proposed to tune a fuzzy logic controller using genetic algorithm. Design of a ga based fuzzy pid controller for nonminimum phase systems. Design of an adaptive fuzzy based control system 184 to use the ga solver, we need to provide at least two input arguments, a fitness function minimum ise and the number of variables in the problem. To maintain optimal performance, the controlling system has to adapt continuously to these changes. In section gabased fuzzy controller design for tunnel ventilation systems. Aug 27, 2014 this paper presents a genetic algorithm ga based design and optimization of fuzzy logic controller flc for automatic generation control agc for a single area. Gabased fuzzy reinforcement learning for control of a.
This ga based rafsmc would improve the immediate response, the stability, and the robustness of the control system. Pdf use of the genetic algorithmbased fuzzy logic controller for. Neural networks, fuzzy logic and genetic algorithms. In this paper, an adaptive neuro fuzzy control system combined with a genetic algorithm tool for adapting the controller for the continually changing conditions influencing on the climate inside a greenhouse is presented. Consequently, ga based flc is designed to optimally satisfy both control objectives simultaneously. An optimized fuzzy continuous sliding mode controller. Block diagram of the real coded ga based fuzzy controller consisting two inputs e1, e2 and one output u is shown in figure 2. In this paper, genetic algorithm ga applied to distance based fuzzy sliding mode controller dfsmc has been developed for an active vehicle suspension system. Ga is used to extract and optimise the rule base of the fuzzy logic controller. Soft computing paradigms for hybrid fuzzy controllers. First, we approximate and describe an uncertain and nonlinear plant for the tracking of a reference trajectory via a fuzzy model incorporating fuzzy logic. In this paper we used two approaches to tune fuzzy logic controller using genetic algorithm. In this paper, we investigate genetic algorithm applications to tuning a fuzzy controller for a second order process by simultaneously manipulating the numerical weights and the symbolic rules structure. A technology demonstration of adaptive road lighting with giant magnetoresistive sensor network for energy efficiency and reducing light pollution.
This study was supported by selcuk university scientific research projects bap support fund under contract number 2003051. It is based on historical process operation data containing manual operation actions from experienced operators. The proposed control algorithms are derived based on lyapunov stability criterion. Automatic numerical rule generation for fuzzy controller. Also rapid advances in digital technologies have given designers the option of implementing controllers using field programmable gate array fpga which depends on parallel programming. Then the designed adaptive control laws of the reference adaptive fuzzy sliding mode controller rafsmc are updated. Fuzzy logic controller based on genetic algorithms pdf.
Optimization of fuzzy logic controller for luo converter. Tuning algorithms for pid controller using soft computing. Pdf fuzzy logic control optimal realization using ga for multiarea. A ga based algorithm thus has the potential to evolve the number of fuzzy rules, and may be developed as a ga based pruning method for fuzzy neural networks. Traditionally, control systems modeling have been based upon the use of mathematical techniques to model the inputoutput relationship of. The first binary code is the flag whether the rule is used. Ga based pid controller improved seat value, vdv ratio, and crest factor as compared to the passive system and classical pid controller. In the process of designing the proposed controller, a new variable called the signed distance is introduced which is the distance between the actual state and the. Karr 31 used ga s to alter the shape of the fuzzy sets used in a given rule base.
Flcs are characterized by a set of parameters, which are optimized using ga to improve their performance. Ga based hybrid fuzzy rule optimization approach for elevator group control system p. A gabased neural fuzzy system for temperature control. Design and analysis of ga based neuralfuzzy optimum adaptive. The evalu11 experimental ation of performance for an lq based semiactive and controller concept was investigated by unger et al. Pedrycz, 1993 or fuzzy model based control see later. Gabased fuzzy controller design for tunnel ventilation s. Ga based fuzzy state feedback controller applied to a nonlinear power system. Rampriya2, 1 department of electronics and instrumentation engg, 2 department of electrical and electronics engg kamaraj college of engg and technology, virudhunagar, india summary pid controllers are widely used in industrial plants because it. Design and analysis of ga based neuralfuzzy optimum.
A fuzzy control system is a control system based on fuzzy logica mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values. Scott lancaster fuzzy flight 2 basic concept of fuzzy logic zadeh attempt to mimic human control logic do away with crisp sets, boolean, truefalse, etc. The fitness function of ga process is formed by taking weighted sum of multiple objectives to trade off between system overshoot and rise time. Results are obtained in time as well as frequency domain. The result shows the better optimization of fuzzy ga controllers. Fuzzy logic controller based ga for semiactive suspension zhang et al. In this paper, an autotuning method for fuzzy logic controller based on the genetic algorithm ga is presented. In the third part of this thesis, a genetic algorithm ga was used to optimize the parameters of membership functions of an image based fuzzy parking controller. Several robust and auto tuning techniques have been proposed in order to further improve the control and robust performance of the pidnn controller 1,2,3,4. Design and analysis of dc motor speed control by ga based. The generic fuzzy pid controller is a fourdimensional three input one output fuzzy system with a huge rulebase, which increases exponentially with the number of inputs and number of fuzzy sets.
So a novel approach is used in this project in order to extract rules from datasheet and the rule base is tuned to meet the required accuracy level using genetic. Process changes, such as flow disturbances and sensor noise, are common in the chemical and metallurgical industries. Certificate this is to certify that the thesis entitled study of the design and tuning methods of pid controller based on fuzzy logic and genetic algorithm submitted by sangram keshari mallick 107ei027 and mehetab alam khan 107ei028 in partial fulfillment of the requirements for the award of bachelor of technology degree in electronics and. In this paper, we describe a method of stability analysis for a gabased reference adaptive fuzzy sliding model controller capable of handling these types of problems for a nonlinear system. Simulation results illustrates the effectiveness of the proposed optimal fuzzy state feedback controller.
The design of a genetic algorithm based fuzzy pulse pump controller for a frequencylocked servo system liangrui chen, guanchyun hsieh, and hahn ming lee abstract in this paper, a genetic. The fuzzy controller is used for sensorless speed control of a brushless dc bldc motor in dsp based application system. Index terms fuzzy logic controller, rules generation, genetic algorithm, crossover operator. The proposed method is called ts modeling or ts fuzzy identification method based on moving rate. Gabased fuzzy controller design for tunnel ventilation. The advantages of adopting a real coded ga for the design and optimization of fuzzy. A multiobjective genetic algorithm for assessing the optimality of control algorithms for semiactive vehicle was developed by crews et al. Fuzzy logic controller based genetic algorithm for semi. In order to check the robustness of the fuzzy control system based on ga, we change the length of.
Pdf design of a gabased fuzzy pid controller for non. In this paper, we describe a method of stability analysis for a ga based reference adaptive fuzzy sliding model controller capable of handling these types of problems for a nonlinear system. Gabased fuzzy neural controller design for municipal. Allow for fractions, partial data, imprecise data fuzzify the data you have how red is this. This paper aims at the genetic algorithm ga s based tuning of fuzzy logic controller flc. The proposed control method reduced the errors between the current situation and the setpoint desired values. This paper describes an approach to genetic algorithm ga based reinforcement learning in fuzzy control based on the pittsburgh model of learning classifier. Fuzzy logic controller, gas turbine, genetic algorithm, pid controller.
Genetic algorithm is a powerful optimizing tool that is. This method is useful for the process of random search from a huge pool of data. Consequently, gabased flc is designed to optimally satisfy both control objectives simultaneously. In the process of designing the proposed controller, a. Pdf an on line fuzzy logic controller flc realization with genetic. Optimization of fuzzy controller based on genetic algorithm. Recently, pidnn controller is one of the popular methods used for control complexes systems. This study applies a fuzzy automatic ga operators controller technique faoct to automatically adjust the ga operators during the optimization process based on the information from the previous generations such as average fitness of the population. A illustrative experiment are successfully made on the computer simulation. Tuning pid controller for speed control of dc motor using. An adaptivenetworkbased fuzzy logic controller is used for position and vibration control of the flexible link.
In terms of software algorithm design of the control system, much of them are based on fuzzy control algorithm 6,7, neural network algorithm and genetic algorithm, and the pid algorithm design891011. This gabased rafsmc would improve the immediate response, the stability, and the robustness of the control system. Abstract in this study, we strive to combine the advantages of fuzzy. The design of the fuzzy controller is, base on the genetic algorithm. A gabased adaptive neurofuzzy controller for greenhouse. In section ga based fuzzy controller design for tunnel ventilation systems. The experimental results reveal that the proposed approach is efficient and effective to design a fuzzy system. Fuzzy controller in this study, mamdani type fuzzy controller, which has five blocks namely normalization, fuzzifier, inference mechanism, defuzzifier and denormalization, has been used 8.
Implementation of realcoded gabased fuzzy controller for. An efficient fuzzyga flow control of turbine compressor. Tuning algorithms for pid controller using soft computing techniques b. Automatic numerical rule generation for fuzzy controller from. Fuzzy auto tuning, gas based pid controller, gaseased fuzzy pid controller and fuzzy pid controller using neural. Kropp and baitinger 33 proposed the use of a ga to optimize. To demonstrate the effectiveness, the results of the proposed realcoded gabased fuzzy controller is compared and analyzed with conventional proportional integral controller and fuzzy controller. Development of automation system for room lighting based. These hybrid controllers consist of a hierarchical nn fuzzy controller applied to a direct drive motor, a ga fuzzy hierarchical controller applied to a flexible robot link, and a gp fuzzy behavior based controller applied to a mobile robot navigation task. A novel stability condition and its application to ga based fuzzy control for nonlinear systems with uncertainty pochen chen, chengwu chen, weiling chiang and ken yeh key words. The results of applying the genetic based fuzzy controller to the pmsm speed control have been compared to. Generally, the greatest difficulty encountered when designing a fuzzy sliding mode controller fsmc or an adaptive fuzzy sliding mode controller afsmc capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector.
The design of input and output membership functions mfs of an flc is carried out by automatically tuning offline the. Gabased multiobjective optimization of active nonlinear. For the system with two inputs and seven membership functions in each range, it leads to a 7x7 decision table and 49 fuzzy rules. The combination of genetic algorithm and fuzzy logic controllers is normally shortened as ga flc and this intelligent hybrid controller has found application in many scenarios like motor speed. But we often encountered in the process of production of temperature control system with large lag. Adaptive network based fuzzy inference system anfis as a.
A fuzzy controller converts a linguistic control strategy into an automatic control strategy and fuzzy rules are constructed by expert experience or knowledge database. In 68, the existent conditions for the ts fuzzy controller are dependent on the positive definite matrices p and q. Fuzzy pid controllers using fpga technique for real time dc. Lee and takagi 32 used a ga to optimize the rule base including the number of rules and fuzzy sets per domain. Gabased adaptive fuzzy logic controller for switched. Ga is introduced to obtain the optimal control parameters of the fuzzy controller in apidfsmc. Human operator based fuzzy intuitive controllers tuned with. Design of a real coded ga based fuzzy controller for speed. In this manner, a fuzzy pid controller can be developed with less parameters and optimized by using the genetic algorithm ga. Pdf gabased intelligent digital redesign of fuzzymodel. Fuzzy logic is expressed by means of the human language.
A study of an modeling method of ts fuzzy system based on. Fuzzy controller in this study, mamdani type fuzzy controller has been used. Design of an imagebased fuzzy controller for parking. Pdf gabased fuzzy sliding mode controller for nonlinear.
Processing of such a rulebase is time consuming and demands large memory space. First its developed stepwise method to tune a fuzzy logic controller with ga with reduced search space. In future ga based pid controller will be implemented in dc motor position control system using labview nihat ozturk et. Ga based intelligent digital redesign of fuzzy model based controllers. The rule definition is subjective and based on the experts knowledge and experience. May 15, 2014 in this article, a new methodology based on fuzzy proportional. Pdf ga based fuzzy logic controller for the crane problem. Request pdf gabased fuzzy neural controller design for municipal incinerators the successful operation of mass burn municipal incinerators for solid waste management involves many uncertain. Moreover, scaling factors of the fuzzy controller are tuned with ga to improve its performance. Fuzzy logic controller based ga for semiactive suspension a is maximum accelerator of vehicle, v is maximum velocity of vehicle, k i i 1,2 is scaling factors of inputs. Brushless dc motor uses double closed loop control system. The fuzzy control has been focus in the field of the control of the bldcm. Iv shows the coding fonnulation when using ga to optimize the fuzzy controller.
The proposed control algorithm has been simulated in matlab and implemented using tms320f2812 dsp controller and tested with 1 hp, 86 sr motor. Gabased fuzzy sliding mode controller for nonlinear systems. Fuzzy logic controller based genetic algorithm flc ga fuzzy control converts expert knowledge into an automatic control strategy without a detailed knowledge of plant. Zhong, heng design of fuzzy logic controller based on differential evolution algorithm. Ga based multi stage fuzzy controller for the upfc 61 issn 1453 1119 parameters drift due to power systems highly nonlinear and stochastic operating nature. By the implementation of the knowledge of fuzzy logic and genetic algorithm in pid controller. Ga based hybrid fuzzy rule optimization approach for elevator. Pdf design and analysis of ga based neuralfuzzy optimum. Fuzzy self tuning of pid controller for active suspension system. A new pid neural network controller design for nonlinear. It is well known that the process of manually tuning a fuzzy logic controller is a very complex task. Design and analysis of ga based neuralfuzzy optimum adaptive control. Design of a gabased fuzzy pid controller for nonminimum. In order to check the robustness of the fuzzy control system based on ga, we change the length of the two poles by 11 12 0.
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