Department of Electrical and Electronics EngineeringDepartment of Electrical and Electronics Engineering

Paper presentation on:Paper presentation on:

Automatic Car Parking Mechanism using Automatic Car Parking Mechanism using Neuro Fuzzy Controller tuned by Genetic AlgorithmNeuro Fuzzy Controller tuned by Genetic Algorithm

ByBy Aran Glenn.JAran Glenn.J Rajathurai.ARajathurai.A

Second year (EEE)Second year (EEE) Email id: aranglenn@gmail.comEmail id: aranglenn@gmail.com a.rajathurai@yahoo.ina.rajathurai@yahoo.in

ST. XAVIER’S CATHOLIC COLLEGE OF ENGINEERING Chunkankadai, Kanyakumari district,T.N

•INTRODUCTION

•SYNOPSISINTELLIGENT CONTROLLERSNEURAL NETWORKFUZZY LOGICGENETIC ALGORITHM

•NEURO FUZZY CONTROLLER

•DESIGN OF NFC BY GA

•TUNING OF NFLC BY GA

•APPLICATION TO AUTOMATIC CAR PARKING

•CONCLUSION

ABSTRACTABSTRACT                        Artificial Intelligence is concerned with automation of Intelligent Artificial Intelligence is concerned with automation of Intelligent

behaviour.Due to their powerful optimization, the genetic algorithm(GA's) behaviour.Due to their powerful optimization, the genetic algorithm(GA's) are currently being investigated for the development of adaptive /self-tuning are currently being investigated for the development of adaptive /self-tuning logic control system.Our paper presents a Neuro Fuzzy Logic Controller logic control system.Our paper presents a Neuro Fuzzy Logic Controller (NFLC) based on Gaussian type - Radial Basis Function (RBF) neural (NFLC) based on Gaussian type - Radial Basis Function (RBF) neural network simultaneously using GA..Using this network we propose the network simultaneously using GA..Using this network we propose the timing statergies for automatic car parking mechanism where the controller timing statergies for automatic car parking mechanism where the controller is used to decide the steering angle.is used to decide the steering angle.

                      The Radial Basis Function (RBF) neural network forms the basis of The Radial Basis Function (RBF) neural network forms the basis of NFLC,with Gaussian membership function.The architecture of RBF  NFLC,with Gaussian membership function.The architecture of RBF  network is similar to multi-layer feedfoward network with interconnections network is similar to multi-layer feedfoward network with interconnections between input,hidden and output layers.The weights in the hidden layer are between input,hidden and output layers.The weights in the hidden layer are tuned by GA's Algorithm.The GA is implemented using dynamic crossover tuned by GA's Algorithm.The GA is implemented using dynamic crossover and mutation probability rates for better exploitation of optimal NFLC and mutation probability rates for better exploitation of optimal NFLC parameters.Comparing with conventional Fuzzy Logic Controller,NFLC parameters.Comparing with conventional Fuzzy Logic Controller,NFLC eliminates laborious design steps such as manual tuning of the eliminates laborious design steps such as manual tuning of the membership functions and selection of fuzzy rules.membership functions and selection of fuzzy rules.

        

INTRODUCTIONINTRODUCTION

“ “Artificial Intelligence “ is an area of Artificial Intelligence “ is an area of computer science concerned with designing computer science concerned with designing intelligent computer system.i.e. systems that intelligent computer system.i.e. systems that exhibit the characteristics associated with exhibit the characteristics associated with intelligence in human behaviour. Due to the intelligence in human behaviour. Due to the drawbacks of conventional PID controller ie., drawbacks of conventional PID controller ie., Complexity in calculation of Kd, Ki, Kd. Complexity in calculation of Kd, Ki, Kd. Intelligent controllers are currently being Intelligent controllers are currently being investigated for the development of adaptive or investigated for the development of adaptive or self tuning fuzzy logic control system.self tuning fuzzy logic control system.

INTELLIGENT CONTROLLERSINTELLIGENT CONTROLLERS

Three typesThree types

Neural networkNeural network

It consist of 3 layers-input ,hidden,output.They are connected by It consist of 3 layers-input ,hidden,output.They are connected by means of weights and correspondingly we will get outputmeans of weights and correspondingly we will get output

Fuzzy logicFuzzy logic

it consist of conditional statements and is working on the basis of it consist of conditional statements and is working on the basis of membership functions membership functions

Genetic AlgorithmGenetic Algorithm

This is implemented by reproduction, dynamic crossover and This is implemented by reproduction, dynamic crossover and mutation and thereby calculate the populationmutation and thereby calculate the population

NEURO-FUZZY CONTROLLERNEURO-FUZZY CONTROLLER

It works by Radial Basis FunctionIt works by Radial Basis Function

Which is given by: IF(X12) and…..(Xn2)….and(XN2)

THEN(wi1) and…(wim)…..and (wiM) where wim is the singleton

defined controlled action for the ith control rule of the mth output variable.

DESIGN OF NEURO FUZZY CONTROLLERDESIGN OF NEURO FUZZY CONTROLLER

Tuning of NFLC parameters by GATuning of NFLC parameters by GAIt involves 3 stepsIt involves 3 steps

coding strategies of NFLC parameterscoding strategies of NFLC parameters As GA deals with the coded parameters,all the NFLC As GA deals with the coded parameters,all the NFLC

parameters that need to be tuned must be encoded into final length parameters that need to be tuned must be encoded into final length of string,the linear mapping can be used for this purpose which isof string,the linear mapping can be used for this purpose which is

Gq = Gqmin + (Gqmax - Gqmin).Aq/(2N-1)Gq = Gqmin + (Gqmax - Gqmin).Aq/(2N-1) where gq is the actual value of the qth parameter and Aq is where gq is the actual value of the qth parameter and Aq is the integer represented by aN-bit string gene.Gqmaxand Gqmin are the integer represented by aN-bit string gene.Gqmaxand Gqmin are userdefined upper and lower limits of the gene respectively .the userdefined upper and lower limits of the gene respectively .the encoded genes are concatenated to form a complete encoded genes are concatenated to form a complete chromosome.Each of the parametera is encoded into 8bit chromosome.Each of the parametera is encoded into 8bit strings,resulting in a complete chromosomes of 360 bits.strings,resulting in a complete chromosomes of 360 bits.

OPTIMISATION BY GAOPTIMISATION BY GA

At the begining the initial populations comprise a set of At the begining the initial populations comprise a set of chromosomes that are scattered all over the search chromosomes that are scattered all over the search space.the initial population may be randomly generated space.the initial population may be randomly generated or may be partly supplied by the user.or may be partly supplied by the user.

•After each chromosome is evaluated with After each chromosome is evaluated with the fitness,the current population the fitness,the current population

undergoes reproduction,builts the mating undergoes reproduction,builts the mating pool which is followed by mutation pool which is followed by mutation

INITIALISATION OF GA PARAMETERSINITIALISATION OF GA PARAMETERS

Dynamic cross over and mutation probability are used in Dynamic cross over and mutation probability are used in GA because of its faster convergenceGA because of its faster convergence

Here as the generation increases the probability rates Here as the generation increases the probability rates decreases exponentiallydecreases exponentially

The performance index is related to fitness asThe performance index is related to fitness as

f = A/(1 +F)g f = A/(1 +F)g

Where f –fitnessWhere f –fitness

F- performance indexF- performance index

A, g - constant A, g - constant

APPLICATION TO AN AUTOMATIC CAR APPLICATION TO AN AUTOMATIC CAR PARKING MECHANISM PARKING MECHANISM

The proposed methodology can be used to The proposed methodology can be used to automate a car parking mechanism where the automate a car parking mechanism where the controller is used to decide the steering angle of controller is used to decide the steering angle of he car in the parking process. A car model in he car in the parking process. A car model in Cartesian coordinates is shown in Fig. Cartesian coordinates is shown in Fig.

The car parking dynamics which consist of nonlinear characteristics the car length L, the constant velocity of the car v and sampling period T.The Cartesian parking space is defined as –n < (x,y)<n and car angle α and steering angle , S to indicate the forward or backward movement, with ; with for forward movement parking trials are performed in a normalized parking space. The position of the car on the plane is indicated by an (x,y )coordinate system. We can park the car at a specified parking lot (xt ,yt ) with desired car angle t.The NFLC can be configured to accept two inputs, i.e., the error of x-position ex, , and the car angle , and to produce the steering angle as the controller output.

Each of the fuzzy input variables ex, and , has five fuzzy

membership functions in their respective universes of discourse. The centers and widths of all the fuzzy membership functions are determined by GA.(xo,yo)with the initial car angle o, The

performance index can be formulated as

Where e x(k) is the error of x -position and e o(k) is the error

of car angle at the k th sampling instant,N i is the total number of

iterations for the i th trial and L is the total number of tests carried out .

CONCLUSIONCONCLUSION

This paper has being presented a neuro This paper has being presented a neuro fuzzy controller based on Gaussian type fuzzy controller based on Gaussian type RBF neural network,where all the RBF neural network,where all the parameters can be simultaneously tuned parameters can be simultaneously tuned by GA. By appropriate coding of NFLC by GA. By appropriate coding of NFLC parameters it can achieve self tuning parameters it can achieve self tuning properties from an initial random state . By properties from an initial random state . By employing dynamic crossover and employing dynamic crossover and mutation,probability rates the tuning mutation,probability rates the tuning process by GA can be further improved process by GA can be further improved

““Art of Teaching is the art of assisting Discovery”Art of Teaching is the art of assisting Discovery”

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