Y-Source Two-Stage Matrix Converter and Its Modulation Strategy

Wang, Rutian; Zhao, Pengfei; Wang, Jian; Hao, Henan

doi:10.1109/access.2020.3036117

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…In the H∞ filtering algorithm, the noise covariance matrix is artificially set based on experience, and its initial value affects the accuracy and convergence of the algorithm [25][26][27]. In this chapter, assuming a fixed process noise covariance matrix, we design a dynamic forgetting factor to weight the combination of the initial and current measurement noise covariance matrices.…”

Section: Forgetting Factor H∞ Filtering Algorithmmentioning

confidence: 99%

Parameter Identification of Permanent Magnet Synchronous Motor with Dynamic Forgetting Factor Based on H∞ Filtering Algorithm

Yuan,

Chang,

Zhang

2023

Actuators

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To address system parameter changes during permanent magnet synchronous motor (PMSM) operation, an H∞ filtering algorithm with a dynamic forgetting factor is proposed for online identification of motor resistance and inductance. First, a standard linear discrete PMSM parameter identification model is established; then, the discrete H∞ filtering algorithm is derived using game theory reducing state and measurement noise influence. A cost function is defined, solving extremes values of different terms. A dynamic forgetting factor is introduced to the weighted combination of initial and current measurement noise covariance matrices, eliminating identification issues from different initial values. On this basis, a dynamic forgetting factor is added to weigh the combination of the initial measurement noise covariance matrix and the current measurement noise covariance matrix, which eliminates the influence of the discrimination error caused by the different initial values. Finally, the identification model is built in MATLAB/Simulink for simulation analysis to verify the feasibility of the proposed algorithm. The simulation results show the proposed H∞ filtering algorithm rapidly and accurately identifies resistance and inductance values with significantly improved robustness. The forgetting factor enables quick stable recognition even with poor initial values, enhancing PMSM control performance.

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Section: Forgetting Factor H∞ Filtering Algorithmmentioning

confidence: 99%

Parameter Identification of Permanent Magnet Synchronous Motor with Dynamic Forgetting Factor Based on H∞ Filtering Algorithm

Yuan,

Chang,

Zhang

2023

Actuators

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“…The brushless DC (BLDC) motor owns superior control performance, mainly manifested in good speed control performance, wide speed regulation range, large starting torque and high efficiency [1][2][3][4]. The BLDC motor is widely used in many high-precision industries, such as defense, aerospace, robotics, industrial control processes and other fields [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Research on the Current Control Strategy of a Brushless DC Motor Utilizing Infinite Mixed Sensitivity Norm

Yuan,

Chang,

Zhang

2023

Electronics

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During the brushless DC (BLDC) motor working process, the system encounters inevitable uncertainties. These ambiguities stem from potential fluctuations, random occurrences, measurement inaccuracies, varying operational conditions, environmental shifts such as temperature alterations, among other factors. Uncertainties, an inherent aspect of any real control system, can be broadly classified into two categories: sensor signal uncertainties and discrepancies between the mathematical and actual models due to parameter perturbations. To mitigate the impact of sensor noise and parameter perturbations on the BLDC motor, a robust control strategy utilizing infinite norm mixed sensitivity based on PI control strategy (PI-H∞-MIX) is proposed in this paper. Firstly, the closed-loop control structure and transfer function model of the BLDC motor control system current loop are analyzed based on the current loop circuit topology, and then, the model parameters perturbation is analyzed, and the multiplicative uncertainty bound is given. In addition, the appropriate weighting function is selected to ensure the robustness of the system. In this case, the controller design problem is transformed into the H∞ standard control problem, and then, the system augmentation matrix is established, and the controller is solved by Matlab/Simulink. Finally, the performances of the traditional PI control strategy and the PI-H∞-MIX are compared and analyzed. The results show that (1) the proposed PI-H∞-MIX strategy can improve the control system robustness under the parameter perturbation condition effectively, and (2) the proposed PI-H∞-MIX strategy can suppress the noise signal of the sensor.

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“…The networks found in references [11] and [12] respectively were functional where an additional switch was used without an inverter. A broad review of the impedance network topologies is presented in [14,15], amongst other newly developed solutions based on impedance networks [16][17][18][19][20]. Additionally, several methods of controlling impedance networks have been considered which can be reviewed in [21,22].…”

Section: Introductionmentioning

confidence: 99%

Controlled Energy Flow in Z-Source Inverters

2021

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This paper proposes a method to reduce the output voltage distortions in voltage source inverters (VSI) working with impedance networks. The three main reasons for the voltage distortions include a discontinuous current in the coils of the impedance network, the double output frequency harmonics in the VSI’s voltage output caused by insufficient capacitance in the impedance network, and voltage drops on the bridge switches during the shoot-through time. The first of these distortions can be reduced by increasing the current of the impedance network when the output VSI current is low. This method requires storing energy in the battery connected to the DC link of the VSI during the “non-shoot through” time. Furthermore, this solution can also be used when the Z-source inverter works with a photovoltaic cell to help it attain a maximum power point. The Z-source inverter is essentially a voltage source inverter with the Z-source in the input. In this paper, the theory behind basic impedance networks of Z-source and quasi-Z-source (qZ-source) is investigated where simulations of the presented solutions and experimental verification of the results are also presented.

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Y-Source Two-Stage Matrix Converter and Its Modulation Strategy

Cited by 5 publications

References 18 publications

Parameter Identification of Permanent Magnet Synchronous Motor with Dynamic Forgetting Factor Based on H∞ Filtering Algorithm

Parameter Identification of Permanent Magnet Synchronous Motor with Dynamic Forgetting Factor Based on H∞ Filtering Algorithm

Research on the Current Control Strategy of a Brushless DC Motor Utilizing Infinite Mixed Sensitivity Norm

Controlled Energy Flow in Z-Source Inverters

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