This paper presents three different robust controller techniques for high performance brushless DC (BLDC) motor. The purpose is to test the ability of each control technique to force the rotor to follow a preselected speed/position track. This objective should be achieved regardless the parameter variations, and external disturbances. The first technique is conventional PID controller. The second controller technique use genetic algorithm to adjust the PID controller parameters based on three different cost functions. Finally a self-tuning fuzzy PID controller is developed and tested. These controllers are tested for both speed regulation and speed tracking. Results shows that the proposed self-tuning fuzzy PID controller has better performance.

The development of environmentally benign silicone composites from sugar palm fibre and silicone rubber was carried out in this study. The mechanical, physical, and morphological properties of the composites with sugar palm (SP) filler contents ranging from 0% to 16% by weight (wt%) were investigated. Based on the uniaxial tensile tests, it was found that the increment in filler content led to higher stiffness. Via dynamic mechanical analysis (DMA), the viscoelastic properties of the silicone biocomposite showed that the storage modulus and loss modulus increased with the increment in filler content. The physical properties also revealed that the density and moisture absorption rate increased as the filler content increased. Inversely, the swelling effect of the highest filler content (16 wt%) revealed that its swelling ratio possessed the lowest rate as compared to the lower filler addition and pure silicone rubber. The morphological analysis via scanning electron microscopy (SEM) showed that the sugar palm filler was evenly dispersed and no agglomeration could be seen. Thus, it can be concluded that the addition of sugar palm filler enhanced the stiffness property of silicone rubber. These new findings could contribute positively to the employment of natural fibres as reinforcements for greener biocomposite materials.

This paper presents an efficient coronavirus optimization algorithm (CVOA) to find the optimal values of the PID controller to track a preselected reference speed of a brushless DC (BLDC) motor under several types of disturbances. This work simulates how the coronavirus (COVID-19) spreads and infects healthy people. The initial values of PID controller parameters consider the zero patient, who infects new patients (other values of PID controller parameters). The model aims to simulate as accurately as possible the coronavirus activity. The CVOA has two major advantages compared to other similar strategies. First, the CVOA parameters are already adjusted according to disease statistics to prevent designers from initializing them with arbitrary values. Second, the approach has the ability to finish after several iterations where the infected population initially grows at an exponential rate. The proposed CVOA was investigated with well-known optimization techniques such as the genetic algorithm (GA) and Harmony Search (HS) optimization. A multi-objective function was used to allow the designer to select the desired rise time, the desired settling time, the desired overshoot, and the desired steady-state error. Several tests were performed to investigate the obtained proper values of PID controller parameters. In the first test, the BLDC motor was exposed to sudden load at a steady speed. In the second test, the continuous sinusoidal load was applied to the rotor of the BLDC motor. In the third test, different operating points of reference speed were selected to the rotor of the BLDC motor. The results proved that the CVOA-based PID controller has the best performance among the techniques. In the first test, the CVOA-based PID controller has a minimum rise time (0.0042 s), minimum settling time (0.0079 s), and acceptable overshoot (0.0511%). In the second test, the CVOA-based PID controller has the minimum deviation about the reference speed (±4 RPM). In the third test, the CVOA-based PID controller can accurately track the reference speed among other techniques.

The aerospace metal cutting industry’s search for environmentally friendly practices that do not compromise machining performance is well known. One of the major objectives is the reduction in use of cutting fluids, which play a major role in containing the harsh effects of severe heat generated during machining. Machining performance and product quality can be improved by controlling heat during machining. The purpose of this study was to determine the effectiveness of various environmentally friendly metalworking fluid (MF) strategies for the sustainable turning of aerospace aluminum alloy (Al-5.6Zn-2.5Mg-1.6Cu-0.23Cr-T6) for automotive, marine, and aerospace industrial applications. The SEM images were analyzed for worn tool surfaces and machined surfaces. Under dry conditions, heat does not dissipate well, and will enter the workpiece due to the absence of coolant. This causes extreme damage beneath a turned workpiece. Thus, at 10 µm, a drop in microhardness of approximately 20% can be observed. A similar observation was made in a Ranque-Hilsch vortex tube (RHVT) and in compressed air; however, the drop in hardness was relatively low compared to dry conditions. This evaluation of microhardness indicated a heat-based attention in the turned workpiece, and thus, the heat-based effect was found to be lowest in RHVT and compressed air compared to dry conditions. Results showed that RHVT reduces temperature up to 10%, surface roughness 13%, and tool wear 20% compared to dry turning. Overall, RHVT was identified as more effective environmentally friendly cooling strategy than dry and compressed air for the turning of aluminum alloy 7075-T6.

This study presents a new auto-tuning nonlinear PID controller for a nonlinear electric vehicle (EV) model. The purpose of the proposed control was to achieve two aims. The first aim was to enhance the dynamic performance of the EV regarding internal and external disturbances. The second aim was to minimize the power consumption of the EV. To ensure that these aims were achieved, two famous controllers were implemented. The first was the PID controller based on the COVID-19 optimization. The second was the nonlinear PID (NPID) optimized controller, also using the COVID-19 optimization. Several driving cycles were executed to compare their dynamic performance and the power consumption. The results showed that the auto-tuning NPID had a smooth dynamic response, with a minimum rise and settling time compared to other control techniques (PID and NPID controllers). Moreover, it achieved low continuous power consumption throughout the driving cycles.