Navigation and obstacle avoidance are very important issues for the successful use of an au- tonomous mobilerobot in a dynamic and unstructured environment. Mobilerobot researchers aim to build an autonomous and intelligent robot which can plan its motion in a dynamic en- vironment. A successful use of an autonomous mobilerobot depends on its controller. Mobilerobotcontrol is diﬃcult as they are subjected to non-holonomic (non-integrable) kinematic con- straints involving the time derivates of conﬁguration variables  and dynamic constraints. Both analytical like potential ﬁeld method as well as graph-based techniques have been used to solve the navigation problems of robot involving both static and dynamic obstacles. But, all such methods may not be suitable for on-line implementations due to their inherent computational complexity and limitations. Mobilerobot researchers have carried out various researches in this direction using various intelligent techniques methods such as fuzzy logic, neural network and genetic algorithm and their diﬀerent hybrids. Because of the non-linear kinematics of the robot, the uncertainty in sensors readings, and unstructured environmental constrains in the control of mobilerobot navigation; researchers have found fuzzy logic as one of the best intelligent tech- nique for handling the constraints. However, fuzzy logic needs tuning for optimal performance. Hand tuning is very diﬃcult and time consuming therefore there is need for automation of the tuning process. The process of tuning requires learning brought about by training or adaptation of the robot to adapt to its dynamic environment. The poor learning capability of fuzzy logic is compensated for by hybridizing fuzzy logic with other soft computing techniques with excellent learning features such as neural network. In this paper, we present an adaptiveneuro-fuzzy controller with genetic algorithm learning for the navigation of Khepera mobilerobot.
The flowchart of ANFIS procedure is shown in Figure 4. AN FIS distinguishes itself from normal fuzzy logic systems by the adaptive parameters, i.e., both the premise and consequent parameters are adjustable. The most remarkable feature of the ANFIS is its hybrid learning algorithm. The adaptation process of the parameters of the ANFIS is divided into two steps. For the first step of the consequent parameters training, the Least Squares method (LS) is used, because the output of the ANFIS is a linear combination of the consequent parameters. The premise parameters are fixed at this step. After the consequent parameters have been adjusted, the approximation error is back-propagated through every layer to update the premise parameters as the second step. This part of the adaptation procedure is based on the gradient descent principle, which is the same as in the training of the BP neural network. The consequence parameters identified by the LS method are optimal in the sense of least squares under the condition that the premise parameters are fixed.
Electroencephalography (EEG) is that the measuring of electrical activity within the living brain. During this project we tend to use a brainwave device MW001 to analyze the EEG signals .This style discuss regarding process and recording the raw EEG signal from the Mind Wave device within the MATLAB setting and through ZigBee transmission management commands are passed to the mechanism section. Mind wave sensors aren't employed in clinical use, however area unit employed in the Brain management Interface (BCI) and neuro feedback (one of training program types). The BCI may be a direct communication pathway between the brains associated an external device .
In deciding how to control a mobile robots eﬀectively, a number of approaches have been proposed by using artiﬁcial intelligence system such as neural net- works, fuzzy logic, and genetic algorithm. Between those techniques, fuzzy logic can be the best candidates for navigation[4–7] as it requires less computational
In this section, the structure of our system and function- alities of each component are described. The structure of ANFIS-based implicit authentication system is presented in Fig. 2 which includes an activity monitoring module, a scor- ing module, a reference computation module and an ANFIS module. The learning capability of ANFIS allows for training of specific fuzzy inference model based on given input-output user data without manual interference. Therefore, this trained fuzzy model represents well the user’s behaviour and can then be used to make final authentication decision to provide access control. The deployment of the system can be divided into two phases: training phase and deployment phase. During the training phase, we use generated input (scores, references) and output (classification labels) variables to train ANFIS module offline. When the training is completed the deployment phase starts, this enables the system to infer the authenticity of the current user behaviour by taking in real-time computed scores and references.
Nowadays, robots are used in applications that require precise techniques such as in surgical operations. To achieve this target, high precision robots need to be employed and modern controller such as intelligent controller is frequently used. Yusuf SAHIN et al., (2010) used Neuro-Fuzzy controller for 3-DOF SCARA Robot, they used three adaptive networks based fuzzy logic controllers for the control strategy as Neuro-Fuzzy controllers but the third controller for wrist of robot was ineffective to track the desired circular tool trajectory. These controllers were designed by training and checking the data sets obtained from PID control of SCARA robot. In this paper, Neuro-Fuzzy controller will be used for the control of 6-DOF robot arm CAD model.
This paper present a new control method for path tracking of mobilerobot based on using fuzzy logic and FOPID controller . Two FOPID controllers are used. Parameters of the two FOPID controllers are optimized offline using genetic algorithm.. These optimized FOPID controllers are used for speed control and azimuth control. Parameters of these controller are adjusted online via fuzzy system. Each FOPID controller is supported by a fuzzy controller for adjusting the parameters online. The adjusting mechanism in the designed control scheme work well when there are variations in the plant parameters and changes in operating conditions.
The main problems in controllers design  are; i) their stability; ii) their tuning. The former problem is not treated in this paper. The latter one is usually approached in off-line mode and also from the point of view of adpative control theory  which is well developed for the linear case . In a dynamcially changing environment eTS fuzzy systems have their advantage of flexibility and open structure. Moreover, they have been used in conjunction with so called indirect learning proposed by Psaltis in 1998  described in  and . While Psaltis and Anderson et al.  used off-line pre-trained and with fixed structure neural networks for their indirect learning scheme in  and  evolving FLC is used that learns ‘on fly’, ‘from scratch’ based on the operational data alone and no pre-training.
The neuro-fuzzy system uses the linguistic knowledge of fuzzy inference system and the learning capability of neural network. To describe the architecture a neurofuzzy system, consider Figure 1. For simplicity, we assume that our fuzzy system has two inputs and one output. Furthermore, we assume that the defuzzification of the variables is a linear combination of the first order of input variables (approach of Takagi - Sugeno).
In this, the system is represented using block diagram using Simulink. Then the mathematical model is derived taking in consideration the entire control circuit Represented and tested on PSpice. Transfer function of PID controller is estimated using Z-N , ,  and Routh’s Stability criteria , , . The time response analysis is carried out to calculate response parameters, frequency response and Stability Analysis is carried out theoretically and by simulation.
ABSTRACT: Condition monitoring systems using vibration measurements and supervised classifiers can be used to auto- mate the diagnosis process of rotating machines. In this paper, we describe an automatic diagnosis system for detection and classification of defects in ball bearings using a time varying parametric spectrum estimation method for analyzing nonstationary vibration signals. The classification task is accomplished by an adaptive neural fuzzy inference system. The designed system was developed to be able to classify four types of preestablished defects in ball bearings operating under several shaft speeds and load conditions. The system was tested with experimental data collected from drive end ball bearing of an induction motor driven by mechanical system.
available information effectively. For many practical problems, however, an important portion of information comes from human experts which is usually not precise and is represented by fuzzy terms like small, large, not very new, and so on. In addition, in controlling complex systems such as mobilerobot navigation, we are faced with the problem of inadequate modeling of the systems, a large quantity of uncertain sensory measurements that are difficult to interpret accurately, and lacking efficient computations of control actions to achieve a desired performance of the systems. This means that, effective control of mobile robots and their associated sensors demands the synthesis and satisfaction of several complex constraints and objectives in real-time, particularly in unstructured, unknown, or dynamic environments such as those typically encountered by outdoor mobile robots. The publication of Professor Zadeh (Zadeh, 1 965) on fuzzy sets has spurred a great interest in the development of fuzzy logic controllers as an alternative to existing advanced model-based controllers for controlling such
On the other hand, the program model of the control station is used to manage the interface between computer-B and the second Nokia GSM-modem. Its flowchart is shown in figure 5. It follows the same steps as in the flowchart of figure 4 by acquiring the GSM port settings, opening the port for data retrieval, checking for available PIN codes and the extraction of the GSM modem and service provider related data. Then it will extract the received text message stored in the GSM-modem inbox. After that it will process and display the GPS position coordinates of the mobile unit using any text message editor application available such as the Nokia text message application then the coordinates can be displayed graphically on the Garmin digital map source if required by the user.
A lot of the engineers and researchers interested in neural networks (NNs) and fuzzy logic (FL) increased, during the late 1980s, to introduce the NN and FL technologies into several application fields.  These two technologies are widely used and are considered fundamental technologies of engineering. Within several years, NN and FS fusing technologies were already being used in commercial products and industrial systems. Today these techniques are very popular in biomedical field like medical diagnosis. This work illustrates a reliable prediction methodology to diagnose Cancer disease and classify between different stages of Cancer using AdaptiveNeuroFuzzy Inference System (ANFIS) classification techniques .
Neurofuzzy Architecture : In ANFIS, Takagi-Sugeno type fuzzy inference system is used . The output of each rule can be a linear combination of input variables plus a constant term or can be only a constant term. The final output is the weighted average of each rule’s output. Basic ANFIS architecture that has two inputs x and y and one output z is shown in Figure 1. The rule base contains two Takagi-Sugeno if-then rules as follows.
Being an under-actuated, non-linear and unstable system, the inverted pendulum (IP) has been examined by many researchers to study the behavior and performance of different and new types of control algorithms , . The inverted pendulum has several forms and types where each type has its own characteristics and degree of freedom. The most common types are the single IP, double IP, single rotary IP, and double rotary IP . Even though these types may have different shapes and sizes, their main objective is the same, namely, to balance the whole system. Since the inverted pendulum is a basic form of any advanced balancing systems -, its applications widely vary from simple robots like scooters and robot arms, to more sophisticated systems such as satellites and rocket launch , -.
The simulation results for this disturbance are illustrated in Figures 10, 11, 12 and 13. As it is shown in these figures, the ANFIS system has better performance compared to passive system, fuzzy and LQR systems which can cause good handling for the vehicle. Also, the proposed system has significant reduction in control force as compared with LQR and fuzzy controllers. Figure 14 shows the body displacement for the first road disturbance with different magnitude. It is shown that the ANFIS controller allows a fast rise time and quick settling time without oscillatory behavior. Simulation show that the ANFIS controller givessuitable results for pulse and sinusoidal function road disturbances effectively, and hence it can be said that this controller could handle other real road situations.
The genetic operators that were applied to this system are ranking based selection, single point crossover and genetic mutation. Individuals are selected from the population for reproduction based on their fitness values. The fitter individuals are the ones with the least cost, that is, individuals that get closest to the target position. The population is ranked based on the fitness of the individuals and this ranking is used to select individuals for crossover. When two individuals have been selected single point crossover randomly selects a position in which to split the chromosomes and swaps the two individuals' genes. This is illustrated in figure 2 for two 9-bit chromosomes with the crossover point between the 4 th and 5 th gene.
Abstract: In this paper a genetic algorithm based self-tuned Neurofuzzy controller (NFC) for the speed control of an induction motor drive (IMD) is presented. The normalization parameters and the membership functions of fuzzy controller were translated into binary bit strings which are primitive by the genetic algorithm (GA) in order to be optimized for the fitness (or) objective function. In the proposed NFC system, a Fuzzy logic and Artificial Neural Network (ANN) structure based on Genetic Algorithm scheme is used.Speed error is given as input to the proposed NFC unlike conventional NFCs which employ both speed error and its derivative speed error as inputs of NFC. A genetic algorithm based NFC controller for an indirect vector control of induction motor is simulated in order to observe the validity or reliability of the proposed NFC method. The simulation results shows a very important improvement in shortening development time and system performance in the proposed NFC over a conventional NFC. In the practical applications the proposed NFC based Genetic Algorithm has less computational burden and it was easier to implement in the Simulink. Using MATLAB/SIMULINK software the effectiveness of the proposed NFC based induction motor drive is tested at various operating conditions.