Ribhan Zafira Abdul Rahman

Optimization of fuzzy model using genetic algorithm for process control application

Abstract A technique for the modeling of nonlinear control processes using fuzzy modeling approach based on the Takagi–Sugeno fuzzy model with a combination of genetic algorithm and recursive least square is proposed. This paper discusses the identification of the parameters at the antecedent and consequent parts of the fuzzy model. For the antecedent fuzzy parameters, genetic algorithm is used to tune them while at the consequent part, recursive least squares approach is used to identify the system parameters. This approach is applied to a process control rig with three subsystems: a heating element, a heat exchanger and a compartment tank. Experimental results show that the proposed approach provides better modeling when compared with Takagi Sugeno fuzzy modeling technique and the linear modeling approach.

Traffic signal control based on Adaptive Neural-Fuzzy Inference System applied to intersection

Adaptive Neural-Fuzzy Inference System (ANFIS) that integrates the best features of fuzzy systems and neural networks has been widely applied in many areas. It can be applied to synthesize controllers, which are able to tune the fuzzy control system automatically, and models that learn from past data to predict future behavior. The aim of this research is to develop an ANFIS traffic signals controller for multilane intersection in order to ease traffic congestions at traffic intersections. The new concept to generate sample data for ANFIS training is introduced in this research. The sample data is generate based on fuzzy rules and can be analysed using tree diagram. This controller is simulated on multilane traffic intersection model developed using M/M/1 queuing theory and its performance in terms of average waiting time, queue length and delay time are compared with traditional controllers and fuzzy controller. Simulation result shows that the average waiting time, queue length, and delay time of ANFIS traffic signal controller are the lowest as compared to the other three controllers. In conclusion, the efficiency and performance of ANFIS controller are much better than that of fuzzy and traditional controllers in different traffic volumes.

Dual-fuzzy based MPPT for dual-load operation with photovoltaic SEPIC converter

In this paper, a dual load operation with dual maximum power point tracking (MPPT) for standalone photovoltaic (PV) system is introduced. This work investigates performance of PV system with dual load operation and variable irradiance by using proposed fuzzy based dual MPPT. The algorithm together with PV module of Kyocera KD210GH-2PU connected to PV SEPIC dc-dc converter was simulated in MATLAB-Simulink. Its performance has been compared with perturb and observe (P&O). Analysis on the effect of input power and output voltage at the load side when duty cycle and irradiance change have been discussed. From the results and analysis, dual fuzzy MPPT with dual load shows that maximum power can be obtained at lower irradiance by using higher resistance load and during high irradiance, lower resistance is needed in order to achieve maximum power. Different irradiance levels requires different values of resistance load to achieve maximum power and these only can be done using dual fuzzy MPPT with dual loads.

Model-based fault detection and diagnosis optimization for process control rig

One of the challenges research on model based fault detection and diagnosis of a system is finding the accurate models. In this paper, fuzzy logic based model using genetic algorithm for optimizing the membership function is used in the development of fault detection and diagnosis of a process control rig. The model is used to generate various residual signals, which relate to the faults of the system. These residual signals are used by artificial neural networks to classify the respective faults and finally to determine the faults of the system. Comparisons of the fault classification technique are done for two different models of the process control rig that are the conventional fuzzy model and the optimized fuzzy-GA model. The results show that the fuzzy-GA model gives more accurate fault classifications as compared to the conventional fuzzy logic model.

Optimizing effect of frying cycles on cooking oil properties using particle swarm optimization

In this paper, Particle swarm optimization (PSO) technique has been proposed to evaluate the quality of recycled cooking palm oil. This technique proved to be the most powerful and robust technique in the modern era. The sensitivity of the threat by used cooking oil on public health and the complex nature and laborious approaches of evaluating the oil will be overcome through the proposed algorithm. The findings in this study have revealed that the appropriate temperature in degree centigrade (°C), of grading cooking oil is higher than 200°C for both the TPC and FFA. The study also portrays none significant differences between PSO and GA in terms of optimum estimated value, fitness and temperature. The findings conclude that it is better to evaluate TPC and FFA of used oil at higher temperatures than at low temperatures.