This paper deals with a novel method for Bat Algorithm (BA) based on optimal tuning of Fractional-Order Proportional Integral Derivative (FOPID) controller for governing the rotor speed of sensorless Brushless Direct Current (BLDC) motor. The BA is used for developing a novel optimization algorithm which can generate five degrees of freedom parameters namely [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] of FOPID controller. The desired speed control and robust performance are achieved by using the FOPID closed loop speed controller with the help of BA for optimal tuning. The time domain specifications of a dynamic system for unit step input to FOPID controller for speed response such as peak time ([Formula: see text]), Percentage of overshoot (PO), settling time ([Formula: see text]), rise time ([Formula: see text]) have been evaluated and the steady-state error ([Formula: see text]) of sensorless speed control of BLDC motor has been measured. The simulation results are compared with Artificial Bee Colony (ABC) optimization method and Modified Genetic Algorithm (MGA) for evaluation of transient and steady state time domain characteristics. The proposed BA-based FOPID controller optimization technique is more efficient in improving the transient characteristic performance and reducing steady state error.
Test scheduling of System-on-Chip (SoC) is a major problem solved by various optimization techniques to minimize the cost and testing time. In this paper, we propose the application of Dragonfly and Ant Lion Optimization algorithms to minimize the test cost and test time of SoC. The swarm behavior of dragonfly and hunting behavior of Ant Lion optimization methods are used to optimize the scheduling time in the benchmark circuits. The proposed algorithms are tested on p22810 and d695 ITC’02 SoC benchmark circuits. The results of the proposed algorithms are compared with other algorithms like Ant Colony Optimization, Modified Ant Colony Optimization, Artificial Bee Colony, Modified Artificial Bee Colony, Firefly, Modified Firefly, and BAT algorithms to highlight the benefits of test time minimization. It is observed that the test time obtained for Dragonfly and Ant Lion optimization algorithms is 0.013188 Sec for D695, 0.013515 Sec for P22810, and 0.013432 Sec for D695, 0.013711 Sec for P22810 respectively with TAM Width of 64, which is less as compared to the other well-known optimization algorithms.
A System-on-Chip (SoC) is an integrated circuit that combines various electronic components in a single die. The SoC components mostly involve user-defined logic, embedded memories, analog, digital and mixed-signal blocks. The testing of an SoC for manufacturing defects is an important task due to IC design complexity, further, it also affects the final cost of the chip. Due to the high complexity involved in SoC test scheduling, various techniques were suggested to reduce the testing time. This paper introduces a novel SoC test scheduling technique based on a Modified Ant Colony Optimization (MACO) algorithm. The testing is performed on the benchmark circuits of ITC'02. The experiments performed on d695 and p22810 SoC benchmark circuits. The results show that the MACO algorithm can achieve reduced test time compared to the ACO algorithm. When compared with ACO, the proposed algorithm MACO reduces the testing time by 47% and 10% for d695 and p22810 SoC benchmark circuits respectively.
In general, unexpected failures in sensorless brushless DC (BLDC) motors can result in production downtime, costly repairs, and safety concerns. BLDC motors are commonly used in home appliances, the medical sector, aerospace, small-scale, and large-scale industries under uncertain operating conditions. Therefore, the fault detection and diagnosis (FDD) of BLDC motor drives can play a very important role in increasing their performance, reliability, robustness control, and operational safety under uncertain operating conditions in critical real-time applications. To satisfy these issues of hall effect sensor, misplacement of a hall-effect sensor, inverter IGBT open-switch fault diagnosis, failure of hall effect sensor, lack of robustness speed control of BLDC motor, which has received substantial interest in academic and industry sectors to establish the proposed work optimization techniques approach FDD strategy for speed control of sensorless BLDC motor under uncertain operating conditions. The proposed optimization techniques such as Bat Algorithm (BA), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) approach FDD strategies for BLDC motor drives. These FDD strategies simulated by the above optimization techniques on a sensorless BLDC motor with numerical Matlab/Simulink 2020a simulation results are verified. From the simulation results, out of three optimization techniques, the WOA-based FDD strategies are very effective for both bearing and stator winding faults detection and diagnosis in sensorless BLDC motor drives.
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