Permanent magnet synchronous motors (PMSMs) are increasingly used in high performance variable speed drives of many industrial applications. PMSM has many features, like high efficiency, compactness, high torque to inertia ratio, rapid dynamic response, simple modeling and control, and maintenance free operation. Presence of position sensors presents several disadvantages, such as reduced reliability, susceptibility to noise, additional cost and weight and increased complexity of the drive system. For these reasons, the development of alternative indirect methods for speed and position control becomes an important research topic. Advantages of sensorless control are reduced hardware complexity, low cost, reduced size, cable elimination, increased noise immunity, increased reliability and decreased maintenance. The key problem in sensorless vector control of ac drives is the accurate dynamic estimation of the stator flux vector over a wide speed range using only terminal variables (currents and voltages). The difficulty comprises state estimation at very low speeds where the fundamental excitation is low and the observer performance tends to be poor. Moreover, the noises of system and measurements are considered other main problems. This paper presents a comprehensive study of the different sliding mode observer methods of speed and position estimations for sensorless control of PMSM drives.
A modified Topology to acquire high efficiency of a bidirectional method of DC–DC converter of non-isolated approach is proposed. The modified circuit involves four numbers of switches with their body diodes, passive elements as two inductors as well as a capacitor and the circuit arrangements double boost converters to progress the voltage gain. The input current of the proposed topology divided amongst the two dissimilar values of inductors produces greater efficiency. In the step-down mode, an apparent lessening in voltage gain and also enhanced efficiency can be realized in the recommended system by expending a synchronous rectification. The modified topology shields the technique for presentation of easy control configurations and is used for truncated output voltage with a large current of energy storage systems in the renewable applications as well as hybrid energy source electric vehicle applications. The simulation of the projected structure has been conducted through MATLAB/Simulink software and has been corroborated through a 12 V/180 V, 200 Watts experimental prototype circuit.
The direct current (DC) microgrids are gaining reputation over traditional existing as well as centralized alternating current (AC) systems due to the usage of green sources. DC microgrids are categorized by the usage of intermediate DC-DC converter, which acts as power conditioning units. Hence, the selection of a suitable DC-DC converter becomes substantial as the overall system efficiency is strongly dependent on the converter's performance. This article proposes a non-isolated enhanced voltage gain of DC-DC converter for DC micro-grid applications. The proposed circuit comprises two equal values of inductors, two voltage-lift circuits, two diodes and three switches operates under two dissimilar duty cycles, which exhibits a major benefit to enhance the voltage gain. The analysis under steady-state operation of the proposed work under dissimilar duty cycles is carried out in detail. Simulation of the proposed work has been carried out by using MATLAB/SIMULINK software platform. A prototype circuit rating of about 40 V/1.1 kV, 1KW converter model has been designed, and its performance is validated.
The requirement of many data communication systems is to employ an adaptive equalizer to minimize the inter-symbol interference. Several adaptive Kalman equalizers have been reported in literature. In these works either the Least Mean Square (LMS) or the Recursive Least Squares (RLS) or the Kalman algorithm have been adopted for channel estimation. The Kalman estimation method can lead to significant improvement in the receiver bit error rate (BER) performance. The use of a Kalman filter for channel estimation leads to a state model of size 2nx2n, where n is the number of filter taps. These solutions are computationally intensive and follow a nonlinear relation in the observation equation. New methods have to be followed to solve the nonlinear model resulting in complex parallel structures. This paper proposes a new approach for the real time implementation of the adaptive Kalman equalizer by providing two Kalman filters that run concurrently to perform the estimation and detection. Thus the Kalman estimator operates in parallel with the Kalman filter based equalizer following a linear model and the size of the state matrix reduces to nxn. Parallel-Pipelined architectures are proposed to perform the time update and measurement update equations of the Kalman equalizer and Kalman estimator. The functionality of the proposed architecture has been verified through VHDL simulation. The synthesis results are presented. It is shown that the convergence performance of the proposed approach is superior to that of the Kalman-RLS and Kalman-LMS adaptive equalizers.
Kalman Filters have been used in a wide range of one-dimensional signal processing applications. This paper deals with the application of the Kalman Adaptive Algorithm to the field of two-dimensional (2-D) signal processing. The results obtained on applying the aforesaid algorithm for the enhancement of an image distorted by noise are discussed. The Kalman algorithm is used to first estimate the coefficients of the unknown 2-D 3x3 Tap FIR Channel across which the image is assumed to be transmitted, and then to estimate the image itself. The results presented show that there is an improvement of more than 8 dB in the Signal to Noise Ratio (SNR) of the image, measured before and after filtering. Further, the Mean Square Error (MSE) and Minimum Mean Square Error (MMSE) plots show that the error in the estimation process converges quickly to a very low value.
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