Mixed H2/H∞ Optimal Voltage Control Design for Smart Transformer Low-Voltage Inverter

The smart transformer has been widely applied for the integration of renewables and loads. For the smart transformer application, the voltage control of low-voltage inverter is important for feeding the load. In this paper, a multi-objective optimization control design approach which comprehensively considers all aspects of indexes, such as linear quadratic (LQ) index, H∞ norm, and closed-loop poles placement, is proposed based on the linear matrix inequality (LMI) solution. The proposed approach is able to alleviate the weight of the designer from the tedious design process of the multiple resonant controllers and the selection of the weighting matrix for the LQ control. Besides that, some excellent performances such as fast recovering time, low total harmonic distortion (THD) and high robustness are achieved by the proposed approach. The THD are 0.5% and 1.7% for linear and non-linear loads, respectively. The voltage drop for linear load step is reduced to 10 V. The proposed approach is applied to a 5 kVA three-phase inverter to yield an optimal control law. Results from the simulation and experiment presented herein will illustrate and validate the proposed approach.

A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements

Objective. Musculoskeletal model (MM) driven by electromyography (EMG) signals has been identified as a promising approach to predicting human motions in the control of prostheses and robots. However, muscle excitations in MMs are generally derived from the EMG signals of the targeted sensor covering the muscle, inconsistent with the fact that signals of a sensor are from multiple muscles considering signal crosstalk in actual situation. To identify more accurate muscle excitations for MM in the presence of crosstalk, we proposed a novel excitation-extracting method inspired by muscle synergy for simultaneously estimating hand and wrist movements. Approach. Muscle excitations were firstly extracted using a two-step muscle synergy-derived method. Specifically, we calculated subject-specific muscle weighting matrix and corresponding profiles according to contributions of different muscles for movements derived from synergistic motion relation. Then, the improved excitations were used to simultaneously estimate hand and wrist movements through musculoskeletal modeling. Moreover, the offline comparison among the proposed method, traditional MM and regression methods, and an online test of the proposed method were conducted. Main results. The offline experiments demonstrated that the proposed approach outperformed the EMG envelope-driven MM and three regression models with higher R and lower NRMSE. Furthermore, the comparison of excitations of two MMs validated the effectiveness of the proposed approach in extracting muscle excitations in the presence of crosstalk. The online test further indicated the superior performance of the proposed method than the MM driven by EMG envelopes. Significance. The proposed excitation-extracting method identified more accurate neural commands for MMs, providing a promising approach in rehabilitation and robot control to model the transformation from surface EMG to joint kinematics.

High Order Spatial Weighting Matrix Using Google Trends

This research aimed to form a high-order spatial weighting matrix based on various simulations. The simulation was the determination of the center of the country based on the capital and google trend data. The keywords that have been used in the Google Trends data are "gold price" and "deposit". These keywords have been translated into 6 official languages of the United Nation including Arabic, Chinese, English, French, Russian, and Spanish. Each language has been represented by 1 country. The determination of the country center that has been used based on the capital as well as keywords and time influenced the form of the high-order spatial weighting matrix. In simulations 1, 2, 4, and 5 the highest spatial order formed was 6. It was different with simulations 3, 6, and 7 the highest spatial order formed was 5. Keywords: language, simulation, gold price, deposit.

Learning Spectral Dictionary for Local Representation of Mesh

For meshes, sharing the topology of a template is a common and practical setting in face-, hand-, and body-related applications. Meshes are irregular since each vertex's neighbors are unordered and their orientations are inconsistent with other vertices. Previous methods use isotropic filters or predefined local coordinate systems or learning weighting matrices for each vertex of the template to overcome the irregularity. Learning weighting matrices for each vertex to soft-permute the vertex's neighbors into an implicit canonical order is an effective way to capture the local structure of each vertex. However, learning weighting matrices for each vertex increases the parameter size linearly with the number of vertices and large amounts of parameters are required for high-resolution 3D shapes. In this paper, we learn spectral dictionary (i.e., bases) for the weighting matrices such that the parameter size is independent of the resolution of 3D shapes. The coefficients of the weighting matrix bases for each vertex are learned from the spectral features of the template's vertex and its neighbors in a weight-sharing manner. Comprehensive experiments demonstrate that our model produces state-of-the-art results with a much smaller model size.