A multicriteria genetic tuning for fuzzy logic controllers

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.

2002

The specification of Fuzzy Logic Controllers (FLC) generally requires a specialist or close collaboration with a specialist who holds the linguistic operation rules of the system. Many times perhaps the rules may be not expressed efficiently in formal means, due to complexity or excessive number of variations and/or combinations involved, but sometimes there may be no specialist at all for the system in study. For these cases, we suggest an autonomous method of definition and optimization of Fuzzy Logic Controllers, making use of h euristics, concepts of Evolutionary Search, Genetic Algorithms and Multi-Agents in determination and optimization of parameters for an automated control system.

Robot and Human Communication, …

A method is developed to tune and optimise the membership functions of Fuzzy Logic Controllers (FLC) by using Genetic Algorithms (GAS). The set of fuzzy If-Then rules and their membership functions of the truck back-upper problem is considered.

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.

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.

A multi-objective genetic algorithm is developed for the purpose of optimizing the rule-base of a Self-Organising Fuzzy Logic Control algorithm (SOFLC). The tuning of the SOFLC optimization is based on selection of the best shaped performance index for modifying the rulebase on-line. A comparative study is conducted between various methods of multi-objective genetic optimisation using the SOFLC algorithm on the muscle relaxant anaesthesia system, which includes a severe nonlinearity, varying dynamics and time-delay.

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.

The VHDL-AMS based genetic optimization of fuzzy logic controller for movement control systems is discussed here. The designs have been carried out in the digital domain with HDL. The basic components of the fuzzy logic controller are designed using VHDL-AMS. The proposed work focuses on control of speed with respect to input parameter such as Alignment & distance with triangular membership functions. This is beneficial for autonomous systems which increases traffic safety and the capacity of a pre-existing road infrastructure. Here distance and tracking control method has been used, which enables a number of autonomous systems to work together.. A case study applying this novel method to an movement control system has been investigated to obtain a new type of fuzzy logic membership function with irregular shapes optimized using Genetic algorithm for best Performance. This paper presents the movement control of vehicle application, the fuzzy control approach and the design using high-level synthesis.

International Conference on Electrical and Electronics Engineering, 2017

1 blank line using 9-point font with single spacing There are many different design parameters such as membership functions, scaling factors, inference and defuzzification methods in the structures of fuzzy logic controllers. Most of the time, it is difficult to determine the parameters accurately even with the help of experts. For this purpose, genetic algorithm one of the heuristic optimization techniques is used to facilitate the design of optimal fuzzy logic controller in this study. Fuzzy logic controllers used in the studies are designed with entirely user-defined software instead of toolboxes. Performances of the designed controllers have been analyzed through simulation studies performed on the permanent magnet synchronous motor. Results obtained from the simulation studies have showed that fuzzy logic controllers optimized based on ITAE performance indice have better performance.

International Journal of Advancements in Computing Technology, 2011

In this work we consider the design of neural network and Takagi Sugeno fuzzy logic controller, TSFLC, with Multi-Obejctive Genetic Algorithm, MOGA. For the neural network, the MOGA has to minimize three objectives, the cumulated error, the number of neurons in the hidden layer and the number and type of inputs to the network. In the case of the TS FLC, the objectives are the cumulated error and the parameters of the rules consequence. Both algorithms are applied for the control of temperature system.