Showing papers by "Azura Che Soh published in 2012"

Adaptive P&O-fuzzy control MPPT for PV boost dc-dc converter

Abstract: This paper proposes an adaptive perturb and observe (P&O)-fuzzy control maximum power point tracking (MPPT) for photovoltaic (PV) boost dc-dc converter. The proposed technique combines both advantages of P&O and fuzzy logic technique. P&O is known as very simple and widely used in the market and fuzzy is also simple and provides fast response. Combination of them should improve MPPT's performance especially with existing of noise. For comparative evaluation, conventional P&O and conventional fuzzy logic have been developed. All the algorithms together with PV module of Kyocera KD210GH-2PU connected to PV boost dc-dc converter were simulated in MATLAB-Simulink respectively. Performance assessment covers overshoot, time response, oscillation and stability as described further in this paper.

Reduction of ANFIS-rules based system through K-Map minimization for traffic signal controller

Abstract: Adaptive-Neural Fuzzy Inference System (ANFIS) traffic signal controller is an intelligent traffic signal controller which has the ability to coordinate the traffic problem by adapting the traffic changes. The number of design rule sets increases the accuracy of the controller. Thus, the complexity of the system and the computation time also increase. Rule minimization based on Karnaugh Map (K-Map) has been done and applied to design the rules of ANFIS for traffic signal controller in order to have optimized performance. The ANFIS traffic signal controller is developed using FIS editor in MATLAB. This controller is applied to control the traffic flow in multilane-multiple traffic intersection. The performance of the developed ANFIS traffic signal controller is compared to existing ANFIS traffic signal controller with the original rules. Indeed, the developed ANFIS traffic signal controller is proven to have a better performance.