Genetic Tuning of a Fuzzy Controller

Many non-linear, inherently unstable systems exist whose control using conventional methods is both difficult to design and unsatisfactory in implementation. Fuzzy Logic Controllers are a class of non-linear controllers that make use of human expert knowledge and an implicit imprecision to apply control to such systems. The performance of Fuzzy Logic Controller depends on its control rules and membership functions. Hence it is very important to adjust these parameters. The incorporation of genetic algorithm into a fuzzy design process adds an 'intelligent' dimension to the fuzzy controller enabling it to create and modify its rules. Genetic algorithms give the possibility of adjusting membership functions down to the level of individual rules. In this paper, the idea of model generation and optimization is explored. Fuzzy process models will be generated and parameters of fuzzy logic controller such as centre of membership function and weight of the rules are optimized using the power of genetic algorithms. The Inverted pendulum system is used as a test system for this approach.

IEEE Transactions on Industry Applications, 2000

Fuzzy control has been applied to various industrial processes, however, its control rules and membership functions are usually obtained by trial and error. Proposed in this paper is an optimal design for membership functions and control rules simultaneously by a genetic algorithm (GA). GA's are search algorithms based on the mechanics of natural selection and natural genetics. They are easy to implement and efficient for multivariable optimization problems, such as fuzzy controller design. The simulation result shows that the fuzzy controller thus designed can achieve good performance merely by using a few fuzzy variables.

Lecture Notes in Computer Science, 2006

Genetic Algorithms have been successful in several practical appliances and obtained great prominence among the techniques of Evolutionary Computation. A large portion of methods and parameters adopted in its use are heuristic or random choices, which remain unchanged throughout the execution once they are set. This can lead to a certain lack of exploration of the search surface and also compromise the variability of the population. For that matter, this work introduces the implementation of an intelligent agent based on fuzzy logic, enabling dynamic monitoring and regulation of six GA parameters. The results obtained surpass the GA traditional implementation in many aspects, and open a wide new space for research and study.

Algerian journal of signals and systems, 2017

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1998

The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy rules, and the thrid one is a tuning process for adjusting the membership functions of the fuzzy rules.

European Symposium on Intelligent …, 1999

In most Fuzzy System applications the structure of the system is chosen non-systematically by an expert according to his knowledge. Nevertheless the system parameters are rich enough to ensure the desired behavior. In the following, we introduce an automatic learning algorithm by de nition of a 1 well behaved and 2 minimal Fuzzy System. A Genetic Algorithm is used to estimate the Fuzzy Systems which capture the above t wo desired properties best. In contrast to other known approaches where only parametrical tuning takes place, here optimization of the entropy of the fuzzy rule base leads to a minimal number of rules, of membership functions and of subpremises together with an optimal input output behavior. The resulting Fuzzy System is comparable to systems designed by an expert but with a better performance. Our algorithm is compared to others by a standard benchmark a system identi cation process and di erent results for symmetric and non-symmetric membership functions are presented.

Fuzzy Sets and Systems, 1998

The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the rst one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy rules, and the third one is a tuning process for adjusting the membership functions of the fuzzy rules. The three components of the learning process are developed formulating suitable Genetic Algorithms.

1996

Abstract. The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms have been built for inducing exploitation/exploration relationships that avoid the premature convergence problem and improve the nal results. One of the most widely studied adaptive approaches are the adaptive parameter setting techniques. In this paper, we study these techniques in depth, based on the use of fuzzy logic controllers.

… 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.

2010

Genetic Algorithm is an important optimization technique, though its application in Fuzzy system is usually limited by problems like local optimal and premature convergence. With an aim to improve the performance of simple Genetic Algorithm, we propose a multi-population genetic algorithm MP-GA which uses two populations collaborating with each other, and apply it to fuzzy controller design to optimize its control rules. The simulation results of Inverted Pendulum demonstrate the effectiveness of this proposed method.