In this paper a 5th-Order single-loop Sigma-Delta Modulator with low distortion structure is presented. This structure, which uses integrator and IIR filter concurrently, has relatively less feedforward paths and modulator coefficients. Thus, its sensitivity to coeffici More
In this paper a 5th-Order single-loop Sigma-Delta Modulator with low distortion structure is presented. This structure, which uses integrator and IIR filter concurrently, has relatively less feedforward paths and modulator coefficients. Thus, its sensitivity to coefficient mismatching is reduced. To lower the power consumption of the modulator, the 2-order IIR filter block is implemented by single OTA, and a passive adder is used to realize input quantizer adder. Simulation results show that this structure can achieve 15-bit of resolution and 6 MHz input signal bandwidth, with 1.2 V supply voltage using a 0.13 µm CMOS technology. Power consumption of modulator is 53 mW. Comparing with other structures, the proposed modulator has higher performance because of increasing the DR and input bandwidth of modulator without extra increasing the power consumption.
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So far, comprehensive and extensive studies have been conducted on the brushless DC motor (BLDC), and a part of these studies focuses on the estimation of the parameters of the transfer function of this motor. Estimation of BLDC motor transfer function parameters is ess More
So far, comprehensive and extensive studies have been conducted on the brushless DC motor (BLDC), and a part of these studies focuses on the estimation of the parameters of the transfer function of this motor. Estimation of BLDC motor transfer function parameters is essential to study motor performance and predict its behavior. Therefore, an efficient, accurate and reliable parameter estimation method is needed. In this article, the problem of estimating the parameters of the transfer function of the inverter-fed BLDC motor set has been solved using particle swarm algorithms (PSO). The results of using this algorithm have been compared with the results of other optimization algorithms. The comparison of these results has shown that the PSO algorithm is an efficient, accurate and reliable method for solving the transfer function parameter estimation problem.
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