Implementation of X-Parameters Principle for Non-linear Vibroacoustic Membrane Using Two-Port Measurement

Moezzi, Reza; Hlava, Jaroslav; Vu, Trieu Minh

doi:10.18280/ts.360401

Cited by 3 publications

(2 citation statements)

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“…Simulations show that the system with MPC can be used for study of different individual gaits for the diagnosis of diseases and for autonomous imaging of human gait. Further studies with different MPC algorithms and parameters by varying the weighting matrices, lengths of prediction, constraints are needed to perform in the next phase of this research, furthermore, applying non-linear based signal processing [21] combined with other innovative technologies like virtual and augmented reality [22] can extend the concept to the advanced phase.…”

Section: Discussionmentioning

confidence: 99%

Model Predictive Control for Modeling Human Gait Motions Assisted by Vicon Technology

Minh¹,

Tamre²,

Musalimov³

et al. 2020

JESA

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Human muscles and the central nervous system (CNS) play the key role to control the human movements and activities. The human CNS determines each human motion following three steps: estimation of the movement trajectory; calculation of required energy for muscles; then perform the motion. In these three step tasks, the human CNS determines the first two steps and the human muscles conduct the third one. This paper efforts the use of model predictive control (MPC) algorithm to simulate the human CNS calculation in the case of gait motion. We first build up the human gait motion mathematical model with 5-link mechanism. This allows us to apply MPC to calculate the optimal torques at each joint and optimal trajectory for muscles. Outcomes of simulations simultaneously are compared with the real human movements captured by the Vicon motion capture technology which is the novelty of this study. Results show that tracking errors are not excessed 7%.

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Section: Discussionmentioning

confidence: 99%

Model Predictive Control for Modeling Human Gait Motions Assisted by Vicon Technology

Minh¹,

Tamre²,

Musalimov³

et al. 2020

JESA

Self Cite

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“…In 2016, X-parameter was used to accurately predict the output power of a pseudomorphic highelectron-mobility transistor (pHEMT) under different loads by Chie-In Lee [12]. In 2019, X-parameter was introduced in the sound field to process large signals with high nonlinearity [13]. Therefore, X-parameter can supplement the nonlinear theory and has become an essential approach for modeling of microwave power devices.…”

Section: Introductionmentioning

confidence: 99%

Approach for Extreme Learning Machine-Based Microwave Power Device Modeling

Lin

Wang

Wu³

et al. 2022

IEEE Access

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The nonlinear representation of active devices plays an important role in microwave circuit design. Whereas, it takes a long time to extract a large amount of large signal data, and the problem of memory resource and CPU occupancy becomes significant. In order to address the problems in traditional large-signal modeling methods, in this paper an X-parameter modeling method for microwave power devices based on extreme learning machine (ELM) is proposed. To demonstrate the effectiveness of this method, a double layer back propagation (BP) neural network model is established. Then, harmonic balance simulations are used to verify the accuracy of these two models. After comparisons, it is proved that the three harmonic errors of double layer BP neural network model are 9.525dBm, 1.309dBm and 14.593dBm, respectively, and the three harmonic errors of ELM model are 0.673 dBm, 0.314 dBm, 3.09 dBm, respectively. Furthermore, the three harmonic modulus errors of double layer BP neural network model are 0.031, 0.002, 7.665e-4, respectively, and the errors of ELM model are 0.005, 0.001, 8.38e-5, respectively. Finally, in order to verify the accuracy of the predicted model in circuit design, the predicted X-parameter is used in the design of power amplifier. Moreover, the errors of the double layer BP neural network prediction model at 2.5 GHz, 5 GHz and 7.5 GHz are 1.142 dBm, 1.436 dBm and 2.294 dBm, respectively. The output power error of the ELM model at 2.5 GHz, 5 GHz and 7.5 GHz are 0.089 dBm, 0.311 dBm and 0.309 dBm, respectively. These experimental results demonstrate that the established ELM model is an efficient and valid approach for modeling GaN high electron mobility transistor types of nonlinear microwave devices.

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