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Twin‐roll strip casting is a near‐net‐shape casting technology that can produce thin steel strips directly from molten steel. Stably controlling the molten steel level is regarded as an important issue to ensure strip quality and casting process stability. As the control of the molten steel level is a time‐varying, nonlinear, and multidisturbance complex system, it is difficult to establish an accurate process model for designing a model‐based controller. Top side‐pouring twin‐roll casting is a new kind of twin‐roll strip casting technology. This study introduces the control system of the top side‐pouring twin‐roll casting process. A fuzzy logic controller (FLC) with its fuzzy rules optimized by particle swarm optimization (PSO) is developed to regulate the molten steel level. Simulation results show that the performance of the FLC can be improved while its fuzzy rules are optimized by PSO. The objective function of PSO has a great influence on the optimization of the fuzzy rules. The top side‐pouring twin‐roll casting experiments are carried out using the FLC with its fuzzy rules optimized by PSO; the results show that strip quality and casting process stability are guaranteed. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) emulator (PVE) is a non‐linear power supply that produces a similar output to that of the PV module. It requires a closed‐loop controller such as the proportional–integral (PI) and fuzzy logic controllers to operate properly. The PI controller has a low‐computational burden and is not robust since the transient response varies under different load conditions. Although the fuzzy logic controller is robust, it requires high‐computation capability. This study proposed an alternative approach called the shift controller for the PVE to easily compute and maintain a fast transient response under various load conditions. The performance of the proposed shift controller for the PVE is compared with the performance of the PVEs using the PI and fuzzy logic controllers, respectively. The controllers are simulated using MATLAB/Simulink and implemented using dSPACE ds1104 for experimental validation. The results show that the proposed shift controller computes up to 18 times faster than the fuzzy logic controller. The proposed controller also responds faster to load change than the PI controller. [ABSTRACT FROM AUTHOR]

Utilisation of renewable energy sources (RES) is increasing day by day to reduce greenhouse emissions. The toxic emission from ship is the main concern in marine sector. Here, utilisation of renewable energy for propulsion and electrification of accessories in a ship are proposed. Microgrid with AC and DC bus is developed using solar panels, wind mills, fuel cell, diesel generator, and energy storage devices. Energy management system with two fuzzy logic controllers (FLCs) is used to select and manage energy in the microgrid. Selection of source is decided by FLC1 based on the availability of RES. Generation of control pulses for inter‐linking converters is decided by FLC2 based on variation in solar irradiance and wind velocity. The microgrid with RES is simulated using MATLAB/SIMULINK. The results show that uncertainty in RES can be handled by FLCs to provide a continuous power supply for transportation of ship and its accessories. [ABSTRACT FROM AUTHOR]

The future wireless communication systems require an intelligent and service adaptive network selection to perform vertical handover (HO) in heterogeneous environments. Since multiple parameters are considered to select the best suitable network, reducing the complexity in multi‐attribute decision making algorithm becomes apparent. In the proposed scheme, fuzzy logic controllers are used to estimate the necessity of HO and to determine the user satisfaction degree based on the critical parameters, such as mobile terminal speed, network load and service cost. Fuzzy analytic hierarchy process is used to calculate the weights of each attribute according to the traffic. Since it handles the uncertainty and vagueness in mapping the user preference to the priority scales, consistency level of network selection can be improved. Quality of service factor is calculated for each network by processing the weighted decision matrix using principal component analysis. Simulation results show that proposed scheme can ensure the best network selection by providing trade‐off between complexity and consistency. [ABSTRACT FROM AUTHOR]

SUMMARY This paper presents the use of evolutionary optimization approach to design and tune smart fuzzy controllers for heating, ventilation, and air conditioning systems or HVAC. The objective is to optimize energy consumption while accounting for user comfort requirements. The problem of energy conservation in air conditioning systems becomes a multi-objective optimization constrained problem, which enlarges the solution search space. To solve this problem, a multi-objective evolutionary optimization technique based on genetic algorithm (GA) is proposed. A physical experimental setup is constructed for data collection and formulation of mathematical model. A fuzzy controller is initially designed through expert knowledge, and GA is then used to tune the rules and membership functions of the fuzzy controller in order to optimize multiple objectives. Simulations and real experiments are compared to determine the effectiveness of the proposed strategy. As compared to the controller present in the real experimental air conditioner, approximately 15% energy is successfully saved with no increase in average individual dissatisfaction or discomfort index. Also, a decrease in peak individual dissatisfaction or discomfort index from 91% to 62% is observed. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The design analysis of a multi‐input converter using an intelligent controller based on fuzzy logic control algorithm is encompassed. The use of dedicated dc–dc converters is going through a transitory phase in smart‐grid applications. Different characteristics of inputs can be combined to give desired output operation by using a multi‐input converter which is more or less the combination of individual converters sharing a common load thus simultaneously transferring power to the load. The converter with the help of an intelligent algorithm will ensure the buffering time for each input thus multiplexing between various inputs according to the set demand and fluctuating conditions on the input side. Such a converter will behave as a power electronic interface between the utility, user and the renewable energy sources. A three‐input cuk converter has been designed for interfacing wind energy sources with the dc load bus and the main grid. Sending power to the main grid incorporates the power quality enhancement features which will be accommodated using the bi‐directional inverter connected with the grid to perform the active filtration. Moreover, three fuzzy logic controllers will set the output currents corresponding to each input current by changing their reference voltages according to the power demand. [ABSTRACT FROM AUTHOR]