A new adaptive fuzzy controller with saturation employing influential rule search scheme (IRSS)
International Journal of Knowledge-based and Intelligent Engineering Systems
Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm
Fuzzy Sets and Systems
A self-generating fuzzy system with ant and particle swarm cooperative optimization
Expert Systems with Applications: An International Journal
Designing fair flow fuzzy controller using genetic algorithm for computer networks
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Designing fuzzy-rule-based systems using continuous ant-colony optimization
IEEE Transactions on Fuzzy Systems
International Journal of Bio-Inspired Computation
Computational intelligence approach to PID controller design using the universal model
Information Sciences: an International Journal
Structural and Multidisciplinary Optimization
Tracking control of uncertain DC server motors using genetic fuzzy system
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Optimization of fuzzy systems using group-based evolutionary algorithm
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Differential evolution with local information for neuro-fuzzy systems optimisation
Knowledge-Based Systems
International Journal of Intelligent Information and Database Systems
International Journal of Intelligent Information and Database Systems
Hi-index | 0.00 |
In this paper, a genetic algorithm (GA) based optimal fuzzy controller design is proposed. The design procedure is accomplished by establishing an index function as the consequent part of the fuzzy control rule. The inputs of the controller, after scaling, are utilized by the index function for computing the output linguistic value. This linguistic value can then be used to map the suitable fuzzy control actions. This proposed novel fuzzy control rule has crisp input and fuzzified output characteristics. The index function plays a role in mapping the desired fuzzy sets for defuzzification resulting in a controlled hypersurface in the linguistic space formed by the input fuzzy variables. Two types of index functions, both linear and nonlinear, are introduced for controlling systems with different degrees of nonlinearity. The parameters of the index function are obtained by applying a simple GA with a suitable fitness function. Various controlled systems result in various parameter sets depending on their dynamics. Under the acquired optimal parameter set the optimal index function can be used to generate the desired control actions. Several simulation examples are given to verify the performance of the proposed GA-based fuzzy controller.