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SSRN 2023年

作者： Li, Yisha Chen, Guoxi Zhang, Ya School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

This article studies adaptive traffic signal control problem of single intersection in dynamic environment. A novel cycle-based signal timing method with traffic flow prediction (CycleRL) is proposed to improve the traffic efficiency under dynamic traffic flow. Firstly, the empirical mode decomposition is applied to denoise the flow data. Then a data-model hybrid driven traffic flow prediction strategy is designed to predict the traffic flow, which combines a model-based Kalman filter and an LSTM network-based predictor and adopts another Kalman filter to fuse both prediction results to improve the prediction precision. Besides, a robust signal cycle timing strategy based on human-machine collaboration is developed to deal with dynamic traffic flow, which firstly designs a rule-based signal cycle scheme according to the predicted flow data as the preliminary scheme, and then finetunes the preliminary scheme based on Soft Actor-Critic (SAC) algorithm according to the real-time traffic dynamics. The experiments in both synthetic scenario and real-world scenario show that the proposed data-model hybrid driven traffic flow prediction algorithm has better prediction performance and the proposed CycleRL method outperforms rule-based methods, flow-based allocation methods and traditional reinforcement learning method. Moreover, it is also shown that the proposed CycleRL method has better transferability to bridge the discrepancy across domains. © 2023, The Authors. All rights reserved.

关键词： Kalman filters

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Intrinsic-feature-guided 3D Object Detection

arXiv

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arXiv 2025年

作者： Zhang, Wanjing Wang, Chenxing School of Automation Southeast University 2# Sipailou Xuanwu Nanjing210096 China Key laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

LiDAR-based 3D object detection is essential for autonomous driving systems. However, LiDAR point clouds may appear to have sparsity, uneven distribution, and incomplete structures, significantly limiting the detection performance. In road driving environments, target objects referring to vehicles, pedestrians and cyclists are well-suited for enhancing representation through the complete template guidance, considering their grid and topological structures. Therefore, this paper presents an intrinsic-feature-guided 3D object detection method based on a template-assisted feature enhancement module, which extracts intrinsic features from relatively generalized templates and provides rich structural information for foreground objects. Furthermore, a proposal-level contrastive learning mechanism is designed to enhance the feature differences between foreground and background objects. The proposed modules can act as plug-and-play components and improve the performance of multiple existing methods. Extensive experiments illustrate that the proposed method achieves the highly competitive detection results. Code will be available at https://***/zhangwanjingjj/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Object recognition

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Adaptive Augmented Kalman Filter-Based control for Electronic Throttle systems with Disturbances and Noises

Adaptive Augmented Kalman Filter-Based Control for Electroni...

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Chinese control Conference (CCC)

作者： Wen Li Jinya Su Shihua Li School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

In this study, we investigate the control problem of electronic throttle systems in the presence of practical challenges such as disturbances and measurement noises. To address these challenges, we propose an adaptive augmented Kalman filter (AAKF) based control approach that combines the strengths of extended state observer in disturbance estimation and adaptive Kalman fil-ter in adaptive noise filtering. The outputs of AAKF are integrated into the Backstepping control design, resulting in a composite control that concurrently achieves fast disturbance rejection and noise suppression. We conduct a comparative simulation study against conventional methods without adaptive filtering to validate the effectiveness of the proposed AAKF-based control strat-egy, which exhibits superior position control accuracy and disturbance attenuation performance. We envision that our proposed control strategy will contribute to improving vehicle power, fuel economy, and emission performance.

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Phase-Shifting Coder: Predicting Accurate Orientation in Oriented Object Detection

Phase-Shifting Coder: Predicting Accurate Orientation in Ori...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yi Yu Feipeng Da School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

With the vigorous development of computer vision, oriented object detection has gradually been featured. In this paper, a novel differentiable angle coder named phase-shifting coder (PSC) is proposed to accurately predict the orientation of objects, along with a dual-frequency version (PSCD). By mapping the rotational periodicity of different cycles into the phase of different frequencies, we provide a unified framework for various periodic fuzzy problems caused by rotational symmetry in oriented object detection. Upon such a framework, common problems in oriented object detection such as boundary discontinuity and square-like problems are elegantly solved in a unified form. Visual analysis and experiments on three datasets prove the effectiveness and the potentiality of our approach. When facing scenarios requiring high-quality bounding boxes, the proposed methods are expected to give a competitive performance. The codes are publicly available at https://***/open-mmlab/mmrotate.

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Ensuring Port Safety: Improved YOLOX and DeepSORT for Accurate Detection and Tracking of Trucks and Truck Drivers

Ensuring Port Safety: Improved YOLOX and DeepSORT for Accura...

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Chinese control Conference (CCC)

作者： Xueqin Zheng Jinya Su Jun Yang School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

An optimized YOLOX+DeepSORT method is proposed to accurately detect and track container trucks and truck drivers at the working position of automated rubber tire gantries in ports, while ensuring their safety during the whole working process. In the proposed method, the improved YOLOX performs object detection and its output is used as the input for multi -object tracking using DeepSORT. The improved YOLOX model is developed through replacing standard convolution with depthwise separable convolution, adding the convolutional block attention module to enhance feature extraction, and using Focal Loss in the loss function to address sample imbalances. Comparative experiments were carried out on a self-built dataset, showing a 4.32% increase in mAP and improved reasoning speed for improved YOLOX compared to the original YOLOX. Furthermore, the optimized method shows a 3.57% increase in Multi-Object Tracking Accuracy and a 1.73% increase in Multi-Object Tracking Precision compared to the benchmark YOLOX+DeepSORT.

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Robust Speed Regulation of PMSM Under Disturbances and Noises Using Composite Model Predictive control

Robust Speed Regulation of PMSM Under Disturbances and Noise...

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Chinese control Conference (CCC)

作者： Linxuan Mao Jinya Su Shihua Li School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

This paper presents a single-loop Model Predictive control strategy that incorporates a reduced-order Generalized Proportional Integral Observe and a Kalman filter to enhance the speed regulation of Permanent Magnet Synchronous Motor systems in the presence of complex disturbances and measurement noises. The proposed controller design seamlessly integrates the predictive control, disturbance observer, and state filter components, and it was evaluated through simulation comparisons. The performance of the proposed method is evaluated using various metrics, including maximum velocity drop, recovery time, and variance of steady-state error, which demonstrate its superior response performance and anti-disturbance ability when compared to other existing methods without state filtering.

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Finite Time Disturbance Observer-Based Tube Model Predictive control for Vehicle Platoon systems with Guaranteed L∞ String Stability

Finite Time Disturbance Observer-Based Tube Model Predictive...

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第34届中国控制与决策会议

作者： Qian Chen Jinhao Liu Jun Yang Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

This paper investigates optimal longitudinal control problems for a vehicle platoon in presence of parameter uncertainties and external ***,a multi-constraint multi-objective optimization model is developed,where physical limits,safety constraints,driving comfort,and fuel economy are taken into *** reduce communication burden and avoid network congestion,the preceding vehicle's acceleration is obtained by employing a finite time disturbance observer(FTDO).As for the parameter uncertainties as well as external disturbances,they are estimated as a lumped disturbance by exploiting a ***,under a predecessor following communication topology,a FTDO-based tube model predictive control method with explicit consideration of string stability is ***,numerical simulations illustrate the effectiveness and superiority of the proposed control approach.

关键词： Vehicle Platoon Longitudinal control Finite Time Disturbance Observer Model Predictive control Predecessor-follower L_∞ String Stability

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Maximum power point tracking of photovoltaic array in partial shading condition based on improved Gray Wolf optimization algorithm

Maximum power point tracking of photovoltaic array in partia...

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第34届中国控制与决策会议

作者： Chuang Kou Chenxi Li Kanjian Zhang Jinxia Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of AutomationSoutheast University

The partial shading condition(PSC) makes it challenging for the PV system to find the maximum power *** this paper,an improved gray wolf algorithm(GWO) is proposed by introducing elimination mechanism,greedy mechanism and adjusting the convergence factor which overcomes the contradiction between global exploration ability and convergence *** fast varying irradiance and PSC,IGWO has been compared with GWO,particle swarm optimization(PSO),and adaptive particle swarm optimization(APSO),the results shows the superiority of IGWO in the MPPTs of the PV system.

关键词： MPPT IGWO Population selection mechanism Greedy mechanism Convergence factor

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MALT: Multi-scale Action Learning Transformer for Online Action Detection

arXiv

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arXiv 2024年

作者： Yang, Zhipeng Wang, Ruoyu Tan, Yang Xie, Liping The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518063 China

Online action detection (OAD) aims to identify ongoing actions from streaming video in real-time, without access to future frames. Since these actions manifest at varying scales of granularity, ranging from coarse to fine, projecting an entire set of action frames to a single latent encoding may result in a lack of local information, necessitating the acquisition of action features across multiple scales. In this paper, we propose a multi-scale action learning transformer (MALT), which includes a novel recurrent decoder (used for feature fusion) that includes fewer parameters and can be trained more efficiently. A hierarchical encoder with multiple encoding branches is further proposed to capture multi-scale action features. The output from the preceding branch is then incrementally input to the subsequent branch as part of a cross-attention calculation. In this way, output features transition from coarse to fine as the branches deepen. We also introduce an explicit frame scoring mechanism employing sparse attention, which filters irrelevant frames more efficiently, without requiring an additional network. The proposed method achieved state-of-the-art performance on two benchmark datasets (THUMOS’14 and TVSeries), outperforming all existing models used for comparison, with an mAP of 0.2% for THUMOS’14 and an mcAP of 0.1% for TVseries. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Event-Triggered Energy Optimization of Wireless Sensor Networks 19

Event-Triggered Energy Optimization of Wireless Sensor Netwo...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Liu, Lu Song, Ruizhuo Wei, Qinglai School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665472432

Aiming at limited communication and energy re-sources in wireless sensor networks (WSN s), this paper proposes an energy management scheme of WSNs via adaptive dynamic programming (ADP) based on event-triggered mecha-nism (ETM). The optimal control strategy obtained by iteration can schedule the sensor nodes and make the nodes switch between working and sleeping situations, thus improving the energy utilization and extending the service life of the energy-constrained WSNs. Firstly, the mathematical model of WSNs is established, and the state is estimated by extended Kalman filter (EKF) algorithm to improve the measurement accuracy. Then, ADP solves the designed value function to achieve the scheduling plan. On the premise of system stability, ETM is applied to activate the controller on demand, which can reduce communication burden and save WSNs energy consumption. Finally, the simulation experiment reveals that the proposed algorithm can reduce the unnecessary triggering times of the controller effectively while ensuring the requirements, and avoid data congestion and interaction resource waste. © 2022 IEEE.

关键词： Energy utilization

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