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Peste des petits ruminants virus (PPRV; family Paramyxoviridae, genus Morbillivirus) causes a highly contagious disease with a high death rate in wild and domestic small ruminants. Four PPRV lineages (L1-L4) [...]

This work deals with minimizing fluctuations of propulsion force and improving the motion quality in a linear switched reluctance motor. In order to minimize the jerks in the moving part of the motor, a new profile is used to generate an appropriate reference speed profile. Then, a simple fuzzy logic system which is able to overcome the uncertainties problem in nonlinear systems is designed and applied to the motor. The fuzzy control system can regulate the motor performance so that it tracks the reference speed with minimum error and fluctuation. The system is designed and its efficiency is shown through simulation and experimental tests in different performance situations. The obtained results confirm that the proposed strategy outperforms other conventional methods. [ABSTRACT FROM AUTHOR]

In this work, a new elevator system created by a linear switched reluctance motor is presented. Unlike conventional linear motors, the proposed linear motor has more than one translator. Each phase in a translator generates a pulsing force; hence, the total generated force has noticeable ripple making it difficult to soft motion. In order to solve this problem, similar phases in different translators have been located in positions with specified distances together. As a result, similar force pulses would be shifted so that they can compensate together. An appropriate control strategy has been designed for the system, which can control the current, force, speed, and position simultaneously. A conventional proportional–integral controller and an adaptive fuzzy controller have been used to control the speed. In order to have a precise study, a prototype elevator system along with its control unit has been made and the obtained results have been compared with the simulation results. These results confirm that the proposed structure can significantly produce force and speed ripples. [ABSTRACT FROM AUTHOR]

A new adaptive control strategy for a double‐sided linear switched reluctance motor is proposed in this work. Switched reluctance motors inherently have non‐linear dynamics that complicates their analysis and control. The Takagi–Sugeno fuzzy logic system has been used in this work to achieve an appropriate adaptive control system for the proposed linear motor. The fuzzy system has been heightened by non‐linear rule consequences and a time‐varying parameter. Updated laws have been designed to refresh the amount of the parameter consequently the suggested control system shows a stable performance in control of switched reluctance motors. The proposed control strategy is independent of the uncertainties of the motor dynamics and has an acceptable adaptability. This control method along with a conventional fuzzy logic based system have been studied on the proposed linear switched reluctance motor and their performances were compared in different performance conditions. An appropriate evaluation method has been used to compare the obtained results. The efficiency of the proposed method in different velocity commands has been examined and confirmed using appropriate simulation and experimental results. [ABSTRACT FROM AUTHOR]

The study used fuzzy logic to extract information and analyze nuclear medicine imaging. Data were extracted for accurate and rapid diagnosis of left ventricular cavity volume (LVV), which is very effective in rapid and accurate diagnosis of left ventricular disease. The proposed method has three steps: First, a wavelet-based analysis was performed on existing medical imaging to reduce the noise in them and improve image resolution. The brighter image of this step was used as the second step input. Image segmentation was performed using a neural network algorithm. At this stage, image classification was performed using the Gaussian function. Finally, in the third step, the area corresponding to the left ventricle was selected, and fuzzy logic was used for this selection. [ABSTRACT FROM AUTHOR]

This study presents a new control system for a linear switched reluctance motor (LSRM). Type‐2 Takati‐Sugeno fuzzy system is used for the motor approximation. The uncertain parameters of the motor are described by the lower and upper membership functions. By constructing an integral sliding mode surface, an adaptive sliding mode controller is designed and dynamic stability of the sliding motion is verified using the Lyapunov function. The design of the proposed sliding mode surface is independent of the number of fuzzy rules and thus, computational complexity is significantly decreased. The control system is implemented on a double‐sided four‐phase LSRM and its performance is demonstrated by means of modelling and experimental results. [ABSTRACT FROM AUTHOR]

Linear switched reluctance motors (LSRMs) are attractive machines for industrial applications. Their structure is simple, robust, and low cost. Despite these advantages, LSRMs suffer from inherent high force ripples which induce noise and vibration problems. The force performance of these motors is largely influenced by their geometry. Therefore, the dimensions must be optimised to improve the force performance. The aim of the present work was to determine the optimum dimensions of a double‐sided LSRM using non‐dominated sorting genetic algorithm‐II and multi‐objective seeker optimisation algorithm along with a three‐dimensional (3D) finite element analysis. The analysis and modelling results are presented, and a laboratory prototype is built. The results revealed that the optimised motor has a higher average force, lower force ripple, and lower total mass [ABSTRACT FROM AUTHOR]

A new direct adaptive fuzzy speed control method for a linear switched reluctance motor with unknown control gain sign is presented. The proposed control system consists of two parts: a fuzzy system and an adaptive law. Fuzzy system approximates an unknown ideal control law with unknown adjustable parameters. After determination of the control law, an appropriate adaptation law is designed to update the adjustable parameters to achieve control objectives. The adaptation law is designed using the error between the unknown ideal control law and fuzzy control law. Unlike previous works, in this study, the unknown control gain sign is taken as an unknown constant parameter and is determined online by an appropriate adaptation law. Proposed control method along with a fuzzy sliding mode speed control and a proportional integral speed control strategies are experimentally studied and the performance index is used to evaluate each strategy. The robustness of the proposed method to the parameters and external load force change has been verified via simulation and experiments. The results indicate that the proposed control method has an acceptable performance. [ABSTRACT FROM AUTHOR]

The linear switched reluctance motors (LSRMs) have many benefits such as simple structure, low cost, comparatively high force‐to‐mass ratio, and no need for mechanical rotary to linear motion converters. However, application of these motors is limited because of their force ripple. In this study, the LSRM is used in vertical motion and a new control strategy is proposed for reducing the force ripple. This method is based on instantaneous control of motor position, speed, phase current, and force. In order to realise the good motion quality of elevator, this study applies a speed profile based on minimum jerk model. Also, a new force distribution function is proposed that can properly distribute the total force among the phases. Using this function, phase currents can properly track the corresponding command currents and then, decrease undesirable ripples of output force considerably. The control system was implemented on a double sided four phase LSRM. Its performance is demonstrated by means of modelling and experimental results in elevator application. [ABSTRACT FROM AUTHOR]