No abstract

This paper presented the brushless direct current motor torque ripple reduction based on the speed and torque control using hybrid technique. The dynamic behavior of the brushless direct current motor is analyzed in terms of the parameters such as the speed, current, back electromotive force and torque. Based on the parameters, the motor speed is controlled and minimized the torque ripples. For controlling the speed of the brushless direct current motor is utilized the fractional-order proportional-integral-derivative controller for generating the optimal control pulses. With the use of fractional-order proportional-integral-derivative controller, the optimal gain parameters are needed to reduce the torque ripples and control the speed of brushless direct current motor. By utilizing the hybrid technique, the gain parameters are utilized to analyze the optimal gain parameters of fractional-order proportional-integralderivative controller. The hybrid technique is the combination of adaptive neuro-fuzzy inference system with firefly algorithm. The proposed strategy is simple in structure and robust to reduce the complexities of the mathematical computations. Initially, the nature inspired optimization algorithm of firefly algorithm is analyzed for finding the error function. In addition, the efficient adaptive neuro-fuzzy inference system controller which becomes an integrated method of approach is performed to control the error functions in order to yields excellent optimized gain values. After that, the control signals are applied to the input of voltage source converter of brushless direct current motor. With this control strategy, the harmonics and torque ripples are minimized. Based on the proposed control strategy, the speed and torque performance is analyzed. The effectiveness of the proposed technique is implemented in MATLAB/Simulink platform and evaluates their performance. The performance analysis of the proposed method is demonstrated and contrasted with the existing techniques such as bat algorithm, particle swarm optimization algorithm and ant-lion optimizer algorithm with fractional-order proportional-integral-derivative controller techniques. KeywordsBrushless direct current motor, torque ripple minimization, speed and torque control, pulse-width modulation, fractional-order proportional-integral-derivative controller, adaptive neuro-fuzzy inference system, firefly algorithm Date

No abstract

Wireless multimedia sensor networks (WMSNs), with its exceptional properties, have found a sturdy and steady spot in automation and surveillance applications. The WMSN had made a massive drift of concentration from the wireless sensor networks with its capability of retrieving multimedia files through multimedia devices. The huge capacity data transfer through the multimedia sensors drain the power capacity of the sensors and is considered to be the vital challenge of the WMSNs. The challenges associated with the WMSNs are the restrictions in resources, connectivity, and security and maintaining a good level of quality of service (QoS). This paper proposes a novel protocol, optimized compressed sensing routing protocol (OCSRP), that performs the routing of multimedia data in a highly efficient manner through which the aforementioned research challenges like resource restrictions and QoS can be addressed. In addition, the security methods were incorporated in routing mechanism, and the results were compared with the existing LEACH algorithm. The quantitative analysis proves that the proposed model possesses high level of QoS with high level of security than the existing security algorithms.

Purpose The brushless direct current motor (BLDCM) is widely accepted and adopted by many industries instead of direct current motors due to high reliability during operation. Brushless direct current (BLDC) has outstanding efficiency as losses that arise out of voltage drops at brushes and friction losses are eliminated. The main factor that affects the performance is temperature introduced in the internal copper core windings. The control of motor speed generates high temperature in BLDC operation. The high temperature is due to presence of ripples in the operational current. The purpose is to present an effective controlling mechanism for speed management and to improve the performance of BLDCM to activate effective management of speed. Design/methodology/approach The purpose is to present an optimal algorithm based on modified moth-flame optimization algorithm over recurrent neural network (MMFO-RNN) for speed management to improve the performance. The core objective of the presented work is to achieve improvement in performance without affecting the design of the system with no additional circuitry. The management of speed in BLDCM has been achieved through reduction or minimization of ripples encircled with torque of the motor. The implementation ends in two stages, namely, controlling the loop of torque and controlling the loop of speed. The MMFO-RNN starts with error optimization, which arises from both the loops, and most effective values have been achieved through MMFO-RNN protocol. Findings The parameters are enriched with Multi Resolution Proportional Integral and Derivative (MRPID) controller operation to achieve minimal ripples for the torque of BLDC and manage the speed of the motor. The performance is increased by adopting this technique approximately 12% in comparison with the existing methodology, which is the main contributions of the presented work. The outcomes are analyzed with the existing methodologies through MATLAB Simulink tool, and the comparative analyses suggest that better performance of the proposed system produces over existing techniques, and proto type model is developed and cross verifies the proposed system. Originality/value The MMFO-RNN starts with error optimization, which arises from both the loops, and most effective values have been achieved through MMFO-RNN protocol. The parameters are enriched with MRPID controller operation to achieve nil or minimal ripples and to encircle the torque of Brushless Direct Current and manage the speed.