Department of Electrical and Electronics Engineering Paper presentation on: Automatic Car Parking Mechanism using Neuro Fuzzy Controller tuned by Genetic Algorithm By Aran Glenn.J Rajathurai.A Second year (EEE) Email id: firstname.lastname@example.org [email_address] ST. XAVIER’S CATHOLIC COLLEGE OF ENGINEERING Chunkankadai, Kanyakumari district,T.N
Artificial Intelligence is concerned with automation of Intelligent behaviour.Due to their powerful optimization, the genetic algorithm(GA's) are currently being investigated for the development of adaptive /self-tuning logic control system.Our paper presents a Neuro Fuzzy Logic Controller (NFLC) based on Gaussian type - Radial Basis Function (RBF) neural network simultaneously using GA..Using this network we propose the timing statergies for automatic car parking mechanism where the controller is used to decide the steering angle.
The Radial Basis Function (RBF) neural network forms the basis of NFLC,with Gaussian membership function.The architecture of RBF network is similar to multi-layer feedfoward network with interconnections 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 and mutation probability rates for better exploitation of optimal NFLC parameters.Comparing with conventional Fuzzy Logic Controller,NFLC eliminates laborious design steps such as manual tuning of the membership functions and selection of fuzzy rules.
“ Artificial Intelligence “ is an area of computer science concerned with designing intelligent computer system.i.e. systems that exhibit the characteristics associated with intelligence in human behaviour. Due to the drawbacks of conventional PID controller ie., Complexity in calculation of Kd, Ki, Kd. Intelligent controllers are currently being investigated for the development of adaptive or self tuning fuzzy logic control system.
As GA deals with the coded parameters,all the NFLC parameters that need to be tuned must be encoded into final length of string,the linear mapping can be used for this purpose which is
Gq = Gqmin + (Gqmax - Gqmin).Aq/(2N-1)
where gq is the actual value of the qth parameter and Aq is the integer represented by aN-bit string gene.Gqmaxand Gqmin are userdefined upper and lower limits of the gene respectively .the encoded genes are concatenated to form a complete chromosome.Each of the parametera is encoded into 8bit strings,resulting in a complete chromosomes of 360 bits.
At the begining the initial populations comprise a set of chromosomes that are scattered all over the search space.the initial population may be randomly generated or may be partly supplied by the user.
The proposed methodology can be used to automate a car parking mechanism where the controller is used to decide the steering angle of he car in the parking process. A car model in 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 e x , 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.(x o ,y o )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 .
This paper has being presented a neuro fuzzy controller based on Gaussian type RBF neural network,where all the parameters can be simultaneously tuned by GA. By appropriate coding of NFLC parameters it can achieve self tuning properties from an initial random state . By employing dynamic crossover and mutation,probability rates the tuning process by GA can be further improved