An Intelligent Fuzzy Controller Based On Genetic Algorithms

IEEE Transactions on Industrial Electronics, 1999

This paper is concerned with the design of automated vehicle guidance control. First, we propose to implement the guidance tasks using several individual controllers. Next, a neural fuzzy network (NFN) is used to build these controllers, where the NFN constructs are neural-network-based connectionist models. A two-phase hybrid learning algorithm which combines genetic and gradient algorithms is employed to identify the NFN weightings. Finally, simulations are given to show that the proposed technology can improve the speed of learning convergence and enhance the performance of vehicle control.

Fuzzy Sets and Systems, 1996

The synthesis of genetics-based machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multi-variate non-linear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to genetics-based machine learning of fuzzy controllers, called a Pittsburgh Fuzzy Classifier System # 1 (P-FCS1) is proposed. P-FCS1 is based on the Pittsburgh model of learning classifier systems and employs variable length rule-sets and simultaneously evolves fuzzy set membership functions and relations. A new crossover operator which respects the functional linkage between fuzzy rules with overlapping input fuzzy set membership functions is introduced. Experimental results using P-FCS l are reported and compared with other published results. Application of P-FCS1 to a distributed control problem (dynamic routing in computer networks) is also described and experimental results are presented.

2009 6th International Multi-Conference on Systems, Signals and Devices, 2009

This paper presents an optimal fuzzy logic controller design using optimization techniques. The fuzzy logic controller (FLC) has been developed and implemented for the motion of the robot from an initial position towards another desired position, taking into account the kinematic constraints. First, we have carried out a simulation of a fuzzy logic based controller which determines the speed values of each driving wheel, while seeking the goal. Secondly, an optimization of this controller has been realized using gradient method and genetic algorithms. A comparison between these methods has been effected. Not only simulation results are shown in this paper, but the "realtime" implementation has been realized onto the mini robot Khepera II. Simulation results verify successfully the application of the proposed method to real motion situations.

It is known that techniques under the topic of Soft Computing have a strong capability of learning and cognition, as well as a good tolerance to uncertainty and imprecision. Due to these properties they can be applied successfully to Intelligent Transportation Systems (ITS), a broad range of diverse technologies that hold answers many transportation problems. The unmanned control of the steering wheel is one of the most important challenges faced by researchers in this area. This paper presents a method to adjust automatically a fuzzy controller to manage the steering wheel of a mass-produced vehicle. Information about the car state while a human driver is handling the car is taken and used to adjust, via genetic algorithms, an appropriate fuzzy controller. To evaluate the fuzzy controller, it will be considered its adjustment to the human driver actions and the fact of not having strong changes in its control surface, in order to carry out not only a good route tracking, even a smooth and comfortable driving for the vehicle's occupiers.

Applied Soft Computing, 2007

The design of fuzzy controllers for the implementation of behaviors in mobile robotics is a complex and highly time-consuming task. The use of machine learning techniques, such as evolutionary algorithms or artificial neural networks for the learning of these controllers allows to automate the design process. In this paper, the automated design of a fuzzy controller using genetic algorithms for the implementation of the wall-following behavior in a mobile robot is described. The algorithm is based on the Iterative Rule Learning (IRL) approach, and a parameter (d) is defined with the aim of selecting the relation between the number of rules and the quality and accuracy of the controller. The designer has to define the universe of discourse and the precision of each variable, and also the scoring function. No restrictions are placed neither in the number of linguistic labels nor in the values that define the membership functions. #

In recent years, soft computing methods, like fuzzy logic and neural networks have been presented and developed for the purpose of mobile robot trajectory tracking. In this paper we will present a fuzzy approach to the problem of mobile robot path tracking for the CEDRA rescue robot with a complicated kinematical model. After designing the fuzzy tracking controller, the membership functions and rule weights will be optimized by genetic algorithm in order to obtain more acceptable results. Simulation results have demonstrated significant improvements in controller efficacy.

… of the 1994 ACM Symposium on …, 1994

Fuzzy rules and inferences provide a powerful framework for controlling complex processes. Like symbolic AI systems, they offer a comprehensible representation, which facilitates transfer of knowledge between the system and human experts and users. Moreover, unlike most symbolic systems, the responses are more gradual. In this fuzzy representation, knowledge is distributed among three different levels: symbolic rules, numerical weights, and fuzzy linguistic definitions. When human expertise is used to build a fuzzy controller, it must often be tuned to fit particular objectives or simply be corrected when it is incomplete or otherwise incorrect. When such expertise does not exist or is difficult to acquire and model in the controller, the knowledge must be generated. In either case, it is desired to perform these tasks automatically. This can be accomplished by searching the space of all controllers, while sampling their performance to provide heuristic quality measures. This requires a robust search mechanism. Moreover, the search should be conducted simultaneously at all representation levels to avoid any assumptions about the knowledge. Genetic algorithms provide the necessary robustness. Their previous applications concentrated on manipulations of a single knowledge level. In this paper, we investigate their applications to manipulations of two different levels: the symbolic rules and the numerical weights. This is accomplished adapting a standard numerical genetic algorithm. A number of experiments is conducted and reported, which illustrate both tuning and learning capabilities of the system.

Journal of Intelligent and Robotic Systems, 2009

An important issue not addressed in the literature, is related to the selection of the fitness function parameters which are used in the evolution process of fuzzy logic controllers for mobile robot navigation. The majority of the fitness functions used for controllers evolution are empirically selected and (most of times) task specified. This results to controllers which heavily depend on fitness function selection. In this paper we compare three major different types of fitness functions and how they affect the navigation performance of a fuzzy logic controlled real robot. Genetic algorithms are employed to evolve the membership functions of these controllers. Further, an efficiency measure is introduced for the systematic analysis and benchmarking of overall performance. This measure takes into account important performance results of the robot during experimentation, such as the final distance from target, the time needed to reach its final position, the time of sensor activation, the mean linear velocity e.t.c. In order to examine the validity of our approach a low cost mobile robot has been developed, which is used as a testbed.

Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375)

A variety of evolutionary algorithms, operating according to Darwinian concepts, have been proposed to approximately solve problems of c o w n engineering applications. Increasingly common applications involve automatic learning of nonlinear mappings that govern the behavior of control systems. In m y cases where robot control is of primary concern, the systems used to demonstrate the effectiveness of evolutionary algorithms often do not represent practical robotic systems. In this paper, genetic programming (GP) is the evolutionary strategy of interest. It is applied to learn fuzzy control rules for a practical autonomous vehicle steering control problem, namely, path tracking. GP handles the simultaneous evolution of membership functions and rule bases for the fuzzy path tracker. As a matter of practicality, robustness of the genetically evolved fizzy controller is demonstrated by examining the efJects of sensor measurement noise and an increase in the robot's nominal forward velocity.

Proceedings of the 16th IFAC World Congress, 2005, 2005

In this paper, an embedded genetic fuzzy motion control system is developed for autonomous navigation and obstacle avoidance of a mobile robot built in the ARIS Lab. A combination of four infrared sensors is equipped to measure the distance to obstacles around the mobile robot. The distance information is processed by the proposed genetic fuzzy controller to adjust the velocities of two separately-driven wheels of the robot. Here the fuzzy logic is used to process the sensor information generating the control commends; and the genetic algorithm is exploited to tune the membership functions of the fuzzy rules. Local search techniques are utilised to enhance the tuning process. The proposed motion controller has been applied on the mobile robot. Experiments have demonstrated the effectiveness of the developed genetic fuzzy controller.