Designing a hierarchical fuzzy logic controller using the differential evolution approach

In conventional fuzzy logic controllers, the computational complexity increases with the dimensions of the system variables; the number of rules increases exponentially as the number of system variables increases. Hierarchical fuzzy logic controllers ( HFLC) have been introduced to reduce the number of rules to a linear function of system variables. However, the use of hierarchical fuzzy logic controllers raises new issues in the automatic design of controllers, namely the coordination of outputs of sub- controllers at lower levels of the hierarchy. In this paper, a method is described for the automatic design of an HFLC using an evolutionary algorithm called differential evolution ( DE). The aim in this paper is to develop a sufficiently versatile method that can be applied to the design of any HFLC architecture. The feasibility of the method is demonstrated by developing a two- stage HFLC for controlling a cart - pole with four state variables. The merits of the method are automatic generation of the HFLC and simplicity as the number of parameters used for encoding the problem are greatly reduced as compared to conventional methods

Evolutionary optimization of a motorcycle traction control system based on fuzzy logic.

Política de acceso abierto tomada de: https://v2.sherpa.ac.uk/id/publication/3451Braking and traction control systems are fundamental vehicle safety equipments. The first ones prevent the wheels from locking, maintaining, when possible, the handling of the vehicle under emergency braking. While the second ones control wheel slip when excessive torque is applied on driving wheels. The aim of this work is to develop and implement a new control model of a traction control system to be installed on a motorcycle, regulating the slip in traction and improving dynamic behavior of two-wheeled vehicles. This paper presents a novel traction control algorithm which makes use of a fuzzy logic control block. Two strategies to create the control block have been carried out. In the first one, the parameters that define the fuzzy logic controller have been tuned according to experience. In the second one, the parameters have been obtained by means of an Evolutionary Algorithm (EA) in order to design an augmented traction controller. It has been proved that the use of EA can improve the fuzzy logic based control algorithm, obtaining better results than those produced with the control tuned only by experience