Runmin Hou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Runmin Hou has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Hao Li
Chongqing Changan Wangjiang Industry Group Co., Ltd., Chongqing 400020, China
1 shared publication - Qiang Gao
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
1 shared publication - Qiang Gao
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
1 shared publication - Qiang Xu
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
1 shared publication - Xiaohui Gu
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
1 shared publication - Yuanlong Hou
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
1 shared publication
Latest Publications
- Open Access
Disturbance-Derivative-Driven Gain Scheduling for Adaptive Super-Twisting Sliding Mode Control of PMSM
This paper addresses a specific dynamic limitation in conventional adaptive super-twisting sliding mode control (ASTSMC) for permanent-magnet synchronous motor (PMSM) speed regulation: the reactive lag of gain adaptation. In standard ASTSMC, controller gains are adjusted based solely on the sliding variable, which grows only after a disturbance has already induced a tracking error. This reactive behavior may produce a non-negligible transient speed droop during abrupt load variations. To alleviate this limitation, a proactive gain-scheduled ASTSMC (PDG-ASTSMC) strategy is proposed. A second-order nonlinear extended state observer (NESO) is employed to estimate the lumped disturbance and to extract its time derivative . This disturbance-derivative signal is incorporated into the gain adaptation law to increase the controller gains during the incipient phase of a load change, before significant speed error accumulates. Stability analysis based on a composite Lyapunov function establishes uniformly ultimately bounded convergence of the closed-loop system, and a quantitative relationship between the proactive index and transient droop reduction is derived. Experimental validation on a 1.42 kW PMSM platform shows that, compared with conventional reactive ASTSMC, the proposed PDG-ASTSMC reduces transient speed droop by over 17% (from 10.5 rpm to 8.7 rpm) and shortens load recovery time by approximately 69% (from 140 ms to 44 ms), without increasing steady-state chattering or current ripple.Open AccessDisturbance-Derivative-Driven Gain Scheduling for Adaptive Super-Twisting Sliding Mode Control of PMSM
This paper addresses a specific dynamic limitation in conventional adaptive super-twisting sliding mode control (ASTSMC) for permanent-magnet synchronous motor (PMSM) speed regulation: the reactive lag of gain adaptation. In standard ASTSMC, controller gains are adjusted based solely on the sliding variable, which grows only after a disturbance has already induced a tracking error. This reactive behavior may produce a non-negligible transient speed droop during abrupt load variations. To alleviate this limitation, a proactive gain-scheduled ASTSMC (PDG-ASTSMC) strategy is proposed. A second-order nonlinear extended state observer (NESO) is employed to estimate the lumped disturbance and to extract its time derivative . This disturbance-derivative signal is incorporated into the gain adaptation law to increase the controller gains during the incipient phase of a load change, before significant speed error accumulates. Stability analysis based on a composite Lyapunov function establishes uniformly ultimately bounded convergence of the closed-loop system, and a quantitative relationship between the proactive index and transient droop reduction is derived. Experimental validation on a 1.42 kW PMSM platform shows that, compared with conventional reactive ASTSMC, the proposed PDG-ASTSMC reduces transient speed droop by over 17% (from 10.5 rpm to 8.7 rpm) and shortens load recovery time by approximately 69% (from 140 ms to 44 ms), without increasing steady-state chattering or current ripple. - Open Access
- 24 March 2022
- Published by MDPI AG in Electronics
- Vol. 11 (7) , 1010
TdmTracker: Multi-Object Tracker Guided by Trajectory Distribution Map
With the great progress of object detection, some detection-based multiple object tracking (MOT) paradigms begin to emerge, including tracking-by-detection, joint detection and tracking, and attention mechanism-based MOT. Due to the separately executed detection, embedding, and data association, tracking-by-detection-based methods are much less efficient than other end-to-end MOT methods. Therefore, recent works are devoted to integrating these separate processes into an end-to-end paradigm. Some of the transformer-based end-to-end methods introducing track queries to detect targets have achieved good results. Self-attention and track query of these methods has given us some inspiration. Moreover, we adopt optimized class query instead of static learned object query to detect new-coming objects of target category. In this work, we present a novel anchor-free attention mechanism-based end-to-end model TdmTracker, where we propose a trajectory distribution map to guide position prediction, and introduce an adaptive query embedding set and query-key attention mechanism to detect tracked objects in the current frame. The experimental results on MOT17 dataset show that the TdmTracker achieves a good speed-accuracy trade-off compared with other state-of-the-arts.
- 24 March 2022
- Published by MDPI AG in Electronics
- Vol. 11 (7) , 1010
Open AccessTdmTracker: Multi-Object Tracker Guided by Trajectory Distribution Map
With the great progress of object detection, some detection-based multiple object tracking (MOT) paradigms begin to emerge, including tracking-by-detection, joint detection and tracking, and attention mechanism-based MOT. Due to the separately executed detection, embedding, and data association, tracking-by-detection-based methods are much less efficient than other end-to-end MOT methods. Therefore, recent works are devoted to integrating these separate processes into an end-to-end paradigm. Some of the transformer-based end-to-end methods introducing track queries to detect targets have achieved good results. Self-attention and track query of these methods has given us some inspiration. Moreover, we adopt optimized class query instead of static learned object query to detect new-coming objects of target category. In this work, we present a novel anchor-free attention mechanism-based end-to-end model TdmTracker, where we propose a trajectory distribution map to guide position prediction, and introduce an adaptive query embedding set and query-key attention mechanism to detect tracked objects in the current frame. The experimental results on MOT17 dataset show that the TdmTracker achieves a good speed-accuracy trade-off compared with other state-of-the-arts.