Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle

Chengdu, China

Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle

Chengdu, China
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Zheng Z.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Zheng Z.,Southwest Jiaotong University | Zhao H.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Zhao H.,Southwest Jiaotong University
Circuits, Systems, and Signal Processing | Year: 2017

This paper proposes a robust set-membership affine projection algorithm with coefficient vector reuse (RSM-APA-CVR) for high background noise environment. In the proposed algorithm, the sum of the squared L2 norms of the differences between the updated weight vector and past weight vectors is minimized and a new robust error bound is designed. Simulation results in acoustic echo cancellation context show that the proposed algorithm has faster convergence rate and smaller steady-state misalignment as compared to the conventional RSM-APA. © 2016, Springer Science+Business Media New York.


Yu Y.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Yu Y.,Southwest Jiaotong University | Zhao H.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Zhao H.,Southwest Jiaotong University
International Journal of Adaptive Control and Signal Processing | Year: 2017

To improve the performance of the recently presented individual weighting factors sign subband adaptive filter (IWF-SSAF) algorithm, its 2 combination algorithms using different step sizes are proposed. The first algorithm is to convexly combine the weight vectors of a large step-size IWF-SSAF filter and a small step-size one; and the second algorithm is to obtain a time-varying step size for the IWF-SSAF by combining a large step size and a small one. The minimization of the sum of the l1-norm of subband errors is used to indirectly update the mixing parameters in these 2 algorithms through a modified sigmoidal function. Moreover, in the first algorithm, to implement a smooth transition from the large step-size IWF-SSAF filter to the small step-size one, the component filters receive a cyclic feedback of the combined weight vector. Both proposed algorithms have almost the same convergence performance, but the second algorithm saves computational cost. Simulation results in impulsive noise scenarios demonstrate the superiority of our proposed algorithms. Copyright © 2017 John Wiley & Sons, Ltd.


Shi L.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Shi L.,Southwest Jiaotong University | Zhao H.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Zhao H.,Southwest Jiaotong University
Circuits, Systems, and Signal Processing | Year: 2017

Variable step size norm-constrained adaptive filtering algorithms are proposed in the paper. A variable step size is derived by minimizing the variance of the noise-free a posterior error. Thus, the update equation can obtain a reasonable step size at each iteration. Due to the introduction of variable step size, the proposed algorithms based on the constrained conditions of L1 and L0 norm have a significant advantage that the convergence rate is faster than some well-known algorithms in the sparse system. The simulation results illustrate the good performance of the proposed algorithms. © 2017, Springer Science+Business Media New York.


Shi L.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Shi L.,Southwest Jiaotong University | Zhao H.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Zhao H.,Southwest Jiaotong University
Circuits, Systems, and Signal Processing | Year: 2017

Recently, the diffusion sign subband adaptive filtering (DSSAF) algorithm has attracted great attention because of its robustness against the impulsive noise and its decorrelation power to the correlated input signals. However, the DSSAF converges slowly when the system to be identified is sparse. In order to solve the problem of slow convergence rate in the sparse system, this paper proposes a diffusion proportionate sign subband adaptive filtering (DPSSAF) algorithm by minimizing L1-norm of the subband a posteriori error vector subject to weighted constraint for each node in the diffusion network. The proposed DPSSAF algorithm is robust against the impulsive noise in the sparse system. To further improve the performance of the DPSSAF in the very sparse system, an efficient version (EDPSSAF) based on L0-norm is obtained, which can better measure the sparseness level of the unknown system with high sparsity. Simulation results are presented to illustrate the good performance of proposed algorithms. © 2017, Springer Science+Business Media New York.


Lu L.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Lu L.,Southwest Jiaotong University | Zhao H.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Zhao H.,Southwest Jiaotong University
Journal of Sound and Vibration | Year: 2016

The filtered-x least mean lp-norm (FxLMP) algorithm is proven to be useful for nonlinear active noise control (NANC) systems. However, its performance deteriorates when the impulsive noises are presented in NANC systems. To surmount this shortcoming, a new nonlinear adaptive algorithm based on Volterra expansion model (VFxlogLMP) is developed in this paper, which is derived by minimizing the lp-norm of logarithmic cost. It is found that the FxLMP and VFxlogLMP require to select an appropriate value of p according to the prior information on noise characteristics, which prohibit their practical applications. Based on VFxlogLMP algorithm, we proposed a continuous lp-norm algorithm with logarithmic cost (VFxlogCLMP), which does not need the parameter selection and thresholds estimation. Benefiting from the various error norms for 1≤p≤2, it remains the robustness of VFxlogLMP. Moreover, the convergence behavior of VFxlogCLMP for moving average secondary paths and stochastic input signals is performed. Compared to the existing algorithms, two versions of the proposed algorithms have much better convergence and stability in impulsive noise environments. © 2015 Elsevier Ltd.


Shu Z.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Shu Z.,Southwest Jiaotong University | Ding N.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Ding N.,Southwest Jiaotong University | And 4 more authors.
IEEE Transactions on Industrial Electronics | Year: 2013

In this paper, a space vector pulsewidth modulation (PWM) (SVPWM) algorithm is proposed, which is in α′β′ frame with dc-link capacitor voltage equalization for diode-clamped multilevel converters (DCMCs). The α′β′ frame is a coordinate system similar to the αβ frame. In this frame, some original complex calculations are substituted by integer additions, integer subtractions, truncations, etc. It brings the time and area efficiency to fixed-point digital realization, particularly for the application in a field-programmable gate array. Meanwhile, a minimum energy property of multiple dc-link capacitors is applied as the basic principle for voltage equalization based on a capacitor current prediction algorithm. By evaluating the redundant vectors in each pulse dwelling period, the balancing algorithm chooses an optimal vector, generates the optimal PWM signals, and sustains the voltage stability. After that, an arbitrary multilevel SVPWM intellectual property core is designed and analyzed in the α′β ′ frame. At the end of this paper, a five-level DCMC-based static synchronous compensator is built and tested. The experimental results verify the balancing algorithm and the system steady-state and dynamic performances. © 2012 IEEE.


Liu D.,Southwest Jiaotong University | Feng Q.,Southwest Jiaotong University | Jiang Q.,Southwest Jiaotong University | Jiang Q.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle
Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University | Year: 2010

To reduce the effect of nonlinearization on maglev gap control, the PSO (particle swarm optimization) algorithm was used to optimize the parameters of a maglev controller, and an improved algorithm was proposed based on the linear decreasing weight particle swarm optimization (LDW-PSO). In order to improve the optimization speed and convergence performance, neighborhood topologies, stagnation detection and global best perturbation were adopted to build the improved algorithm. The simulation and experiment results show that the output overshoot of an optimized PID (proportional-integral-derivative) controller based on the improved algorithm is 45% smaller than that of a traditional PID maglev controller.


Jing Y.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Zhang K.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Xiao J.,Southwest Jiaotong University
Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011 | Year: 2011

In this brief, we propose a fuzzy neural network (FNN) modeling approach which is applied for the modeling of gap sensor in the high-speed maglev train. The gap sensor plays an important role for electro-magnetic levitation system which is a critical component of high-speed maglev train. Artificial neural network is a promising area in the development of intelligent sensors. In this paper, we present a model of gap sensor based on fuzzy neural network. The proposed model based fuzzy network scheme incorporates intelligence into the sensor. The fuzzy neural network, as an inverse model compensator if connected in series to the output terminal of the gap sensor, would estimate the correct true gap in a range of temperature after proper training. We trained the network by gradient descent learning algorithm with momentum. It is revealed from the simulation studies that this gap sensor model can provide correct gap within the error less than 0.4mm over a range of temperature variations from 20 C to 80C and within 0.2mm only considering the work gap 8mm to 12mm. The experimental results show that the compensated gap signal meets the requirement of levitation control system. © 2011 IEEE.


Mai R.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Lu L.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Li Y.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | Year: 2016

A phasor control method is employed in this paper to eliminate the circulating current caused by the parallel connected high frequency inverters. The topology of the parallel dual-inverter LCCL-based inductive power transfer (IPT) system and the cause of circulating current are analyzed in detail. Secondly, the virtual active/reactive power based on the current of the primary coil are calculated without using the phase-locked loop, the relationship between the goal of the phasor control and the virtual active/reactive power is analyzed, and then the circulating current eliminating approach is provided. The performance of the proposed approach is evaluated by the experimentation of two parallel connected high frequency inverters with 1.4 kW maximum transmission power and overall 89.82% DC-DC transmission efficiency. The experimental results demonstrate that the circulating current between the parallel connected inverters is dramatically reduced by the proposed algorithm. © 2016, The editorial office of Transaction of China Electrotechnical Society. All right reserved.


Jing Y.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Xiao J.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle | Zhang K.,Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle
Transactions of the Institute of Measurement and Control | Year: 2013

The gap sensor plays an important role for a electro-magnetic levitation system, which is a critical component of high-speed maglev trains. An artificial neural network is a promising area in the development of intelligent sensors. In this paper, a radial basis function (RBF) neural network modelling approach is introduced for the compensation of the non-contact inductive gap sensor of the high-speed maglev train. As an inverse model compensator, the designed RBF-based model is connected in series to the output terminal of the gap sensor. The network is trained by using a gradient descent learning algorithm with momentum. This scheme could estimate accurately the correct air-gap distance in a wide range of temperatures. The simulation studies of this model show that it can provide a compensated gap value with an error of less than ±0.4 mm at any temperature from 20 to 80 C. In particulr, the maximum estimation error can be reduced to ±0.1 mm when the working gap varies from 8 to 12 mm. The experimental results indicate that the compensated gap signal could meet the requirements of the levitation control system. © The Author(s) 2013.

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