Tianfu Sun | Searcharxiv

arXiv2026

Hybrid Virtual Space Vector Modulation With Extended Power Factor Range for Coupled Eight-Switch Three-Phase Three-Level Inverter

With the development of photovoltaic (PV) and motor drive, the coupled eight-switch three-phase three-level inverter (TP-TLI) is receiving more attentions. However, the previous modulation strategies for coupled eight-switch TP-TLI are suitable for high power factor (PF), which cannot satisfy the requirement for PV and motor drive. Aiming at this, this paper proposes a hybrid virtual space vector modulation (HVSVM) for the coupled eight switch TP-TLI with extended PF range. The mechanism of PF limitation is analyzed in deep. Two virtual vectors are constructed in each sector, and three basic VSVMs with different PF range are proposed according to the number of virtual small vectors used. Combined with three VSVMs, the HVSVM is proposed by detecting the three-phase current polarities. The PF angle range of the HVSVM is extended from about [-30{\deg}, 30{\deg}] to [-90{\deg}, 90{\deg}]. Besides, an active neutral-point voltage control (NPVC) is also presented by using two coefficients to adjust the dwell times of the two pairs of redundant small vectors. This approach considers the NP current and the derivation of NPV, and thereby it is accurate to control the NPV. The correctness and feasibility of the proposed HVSVM and active NPVC are verified by experiments through a 3 kVA prototype.

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arXiv2025

Multi-Goal Dexterous Hand Manipulation using Probabilistic Model-based Reinforcement Learning

This paper tackles the challenge of learning multi-goal dexterous hand manipulation tasks using model-based Reinforcement Learning. We propose Goal-Conditioned Probabilistic Model Predictive Control (GC-PMPC) by designing probabilistic neural network ensembles to describe the high-dimensional dexterous hand dynamics and introducing an asynchronous MPC policy to meet the control frequency requirements in real-world dexterous hand systems. Extensive evaluations on four simulated Shadow Hand manipulation scenarios with randomly generated goals demonstrate GC-PMPC's superior performance over state-of-the-art baselines. It successfully drives a cable-driven Dexterous hand, DexHand 021 with 12 Active DOFs and 5 tactile sensors, to learn manipulating a cubic die to three goal poses within approximately 80 minutes of interactions, demonstrating exceptional learning efficiency and control performance on a cost-effective dexterous hand platform.

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