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2022 China Automation Congress (CAC)最新文献

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A Complex Heterogeneous Network-based Analysis Approach for Exploring Railway Operational Accidents 基于复杂异构网络的铁路运营事故分析方法
Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055256
Y. Qi, Jintao Liu, Jiuhong Li
Railway operational accidents are caused by a variety of related hazards’ interactions, which can be shown in the form of a complex network. By analyzing the topological characteristics of such a network, we can understand the causes of the accidents more deeply and make effective countermeasures. In this paper, a new analysis approach to understanding railway operational accidents based on a complex heterogeneous network is proposed. Its originality is to analyze accidents through topological indicators which are more suitable for heterogeneous networks and to design the targeted analysis process based on these indicators. Besides, the causality between other hazards or accidents directly related to one hazard in the network is quantified, which can describe the causal relationship in the network more accurately. This analysis approach provides a decision-making basis for ensuring the safety of railway operations. This paper takes the British railway operational accidents as a case study. The results show that this approach can effectively determine the key factors affecting the accidents and give a feasible decision-making basis.
铁路运营事故是由各种相关危险因素的相互作用造成的,这些相互作用可以以复杂网络的形式表现出来。通过分析此类网络的拓扑特征,可以更深入地了解事故发生的原因,并制定有效的对策。本文提出了一种新的基于复杂异构网络的铁路运行事故分析方法。其独创性在于通过更适合异构网络的拓扑指标分析事故,并基于这些指标设计有针对性的分析流程。此外,对网络中与某一危害直接相关的其他危害或事故之间的因果关系进行了量化,可以更准确地描述网络中的因果关系。该分析方法为保障铁路运营安全提供了决策依据。本文以英国铁路运营事故为个案进行研究。结果表明,该方法可以有效地确定影响事故的关键因素,为决策提供可行的依据。
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引用次数: 0
Adaptive Traffic Signal Control Through Time Period Division and Deep Reinforcement Learning 基于时间段划分和深度强化学习的自适应交通信号控制
Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055131
Baolin Gong, Wenxing Zhu
In this paper, we propose an adaptive traffic signal control model combining time period division and deep reinforcement learning to improve the efficiency of traffic by dynamically changing the traffic phase duration according to the real-time situation. In our model, a day-time period is divided into two overlap period parts representing the morning situation and the evening situation, then the deep reinforcement learning algorithm-TD3 is selected to train the corresponding agent in each part, and finally a fuzzy method is used to coordinate these two agents at different time. In order to get better performance, we make some improvements in TD3. We improve the algorithm’s experience-replay mechanism and use some tricks in training. Simulation results shows that our model can effectively reduce vehicles’ accumulative waiting time, queue length and alleviate CO2 emission.
本文提出了一种结合时间段划分和深度强化学习的自适应交通信号控制模型,通过根据实时情况动态改变交通相位持续时间来提高交通效率。在我们的模型中,将一个白天时间段划分为代表早晨和晚上情况的两个重叠时间段部分,然后选择深度强化学习算法- td3在每个部分训练相应的智能体,最后使用模糊方法在不同时间协调这两个智能体。为了获得更好的性能,我们在TD3中做了一些改进。我们改进了算法的经验重放机制,并在训练中使用了一些技巧。仿真结果表明,该模型可以有效地减少车辆的累计等待时间和排队长度,减少CO2排放。
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引用次数: 0
Learning Greenhouse Climate Control Policy from Monitored Data 从监测数据学习温室气候控制政策
Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055372
Xiaoxuan Zhao, Haoyu Wang, Xiujuan Wang, U. Lewlomphaisarl, Dong Li, Jing Hua, Mengzhen Kang
The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar configuration.
日光温室种植者的环境控制知识在温室生产和管理中起着重要的作用。提出了一种通过建立长短期记忆(LSTM)模型从温室监测数据中提取控制策略的方法。根据某日光温室的实际监测数据对模型进行了验证,表明该模型能够学习日光温室通风机的控制策略。通过监测数据和模型,可以学习温室通风控制的知识,并在配置相似的温室中实现自动控制。
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引用次数: 1
Fast Dense Mapping Based on Signed Distance Function Submaps 基于符号距离函数子映射的快速密集映射
Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055118
Zhenbo Liu, Changwei Cheng, Zhenhui Yi
In order to decrease computational complexity of dense mapping in large-scale environment based on Euclidean Signed Distance Function(ESDF) submap model, a registration algorithm based on octree sampling and sliding window structure is designed. By introducing the octree structure, the surface points on ESDF model with larger weights are evenly selected uniformly to reduce the number of residuals. The sliding window structure keeps the number of optimization variables constant, keeping the optimization time within the controllable range. We integrate these algorithms into the Voxgraph framework to build a new fast mapping system. The experimental results show that the octree sampling algorithm can decrease registration speed by 50% and the sliding window structure can decrease mapping speed by 33%.
为了降低大规模环境下基于欧几里得签名距离函数(ESDF)子地图模型的密集映射的计算复杂度,设计了一种基于八叉树采样和滑动窗结构的配准算法。通过引入八叉树结构,均匀选择ESDF模型上权值较大的表面点,减少残差数量。滑动窗口结构使优化变量数量保持不变,使优化时间保持在可控范围内。我们将这些算法集成到Voxgraph框架中,构建了一个新的快速映射系统。实验结果表明,八叉树采样算法可使配准速度降低50%,滑动窗结构可使映射速度降低33%。
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引用次数: 0
MRAC-Based Adaptive Feedback Linearization Control Method for Continuous-Time Nonlinear Systems with Uncertain Parameters 参数不确定连续非线性系统的自适应反馈线性化控制方法
Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055000
Boyu Wen, Xin Chen, Yipu Sun
The feedback linearization method can accurately linearize the nonlinear system. However, feedback linearization needs the exact dynamic of nonlinear systems, it is difficult to apply to unknown nonlinear systems. To be capable to perform the feedback linearization of the continuous-time nonlinear system containing uncertain parameters, a model reference adaptive control (MRAC) scheme is introduced in this paper. First, we construct a state feedback controller by the knowledge of the system model structure and form it into an adjustable system together with the nonlinear object. The reference model is decided upon to be a standard linear system. Second, based on the output errors of reference model and adjustable system, we adaptively modify the state feedback controller’s unknown parameters using the gradient descent approach. Finally, the simulations and real-world experiments on a first-order inverted pendulum system are carried out to assess the effectiveness of given methods.
反馈线性化方法可以精确地对非线性系统进行线性化。然而,反馈线性化需要非线性系统的精确动态,难以应用于未知的非线性系统。为了能够对含不确定参数的连续非线性系统进行反馈线性化,提出了一种模型参考自适应控制(MRAC)方案。首先,利用系统模型结构的知识构造状态反馈控制器,并与非线性对象组成可调系统。确定参考模型为标准线性系统。其次,基于参考模型和可调系统的输出误差,采用梯度下降法自适应修正状态反馈控制器的未知参数;最后,对一阶倒立摆系统进行了仿真和实际实验,验证了所提方法的有效性。
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引用次数: 0
Application of WOA-VMD-SVM in Fault Diagnosis of Generator Inter-turn Short Circuit WOA-VMD-SVM在发电机匝间短路故障诊断中的应用
Pub Date : 2022-11-25 DOI: 10.1109/cac57257.2022.10055106
Jing Huang, Ruping Lin, Zhiguo He, Huishu Song, Xiaosheng Huang, Binyi Chen
This paper proposes a feature extraction method based on whale optimization algorithm and variational mode decomposition (WOA-VMD) to overcome the low feature extraction accuracy of generator early inter-turn short circuit fault. WOA-VMD process the current signal, and the sample entropy is taken as the fitness function of WOA to optimize the VMD parameter combination of modal components' number K and penalty parament α. Then, the optimized VMD decomposes current signals into K intrinsic mode functions (IMFs). IMFs with higher kurtosis values are selected to extract energy entropy as the feature vectors. Finally, the whale optimization algorithm and support vector machine (WOA-SVM) pattern recognition model is used to classify the feature vectors and diagnose generator inter-turn short circuit degree. The experiments show that the proposed method extracts the weak fault features in the early inter-turn short circuit signal and improves the fault diagnosis accuracy, reaching 97.75%.
针对发电机早期匝间短路故障特征提取精度低的问题,提出了一种基于鲸鱼优化算法和变分模态分解(WOA-VMD)的特征提取方法。WOA-VMD对电流信号进行处理,以样本熵作为WOA的适应度函数,优化模态分量K和惩罚分量α的VMD参数组合。然后,优化后的VMD将电流信号分解为K个本征模态函数(IMFs)。选取峰度较高的imf提取能量熵作为特征向量。最后,利用鲸鱼优化算法和支持向量机(WOA-SVM)模式识别模型对特征向量进行分类,诊断发电机匝间短路程度。实验表明,该方法提取出匝间早期短路信号中的微弱故障特征,提高了故障诊断准确率,达到97.75%。
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引用次数: 1
Beyond-line-of-sight Perception Enhancement via Information Interaction in Connected Autonomous Driving Environment 互联自动驾驶环境下基于信息交互的超视距感知增强
Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10054747
Yu Zha, W. Shangguan
On account of occlusion and limited visual range, the independent perception of the single vehicle is restricted, which cannot meet the requirements of high-level autonomous driving. In view of the characteristics of information interaction in connected environment, a vehicle-vehicle based beyond-line-of-sight fusion perception framework is proposed. Effective data fusion of multi-source heterogeneous sensor is realized based on D-S evidence theory. Precise object detection and recognition is achieved based on lightweight object detection Faster R-CNN algorithm with backbone used MobileNetV2. Finally, the beyond-line-of-sight perception enhancement method in typical scenes is verified and analyzed on Prescan. Results show that the presented method helps autonomous vehicles make full use of sensory data effectively, expand perception scope, avoid blind fields, which plays a supporting role in the safe and efficient operation of autonomous vehicles.
由于遮挡和视觉距离有限,限制了单个车辆的独立感知,无法满足高水平自动驾驶的要求。针对互联环境中信息交互的特点,提出了一种基于车-车超视距融合感知框架。基于D-S证据理论,实现了多源异构传感器的有效数据融合。基于轻量级目标检测的更快R-CNN算法,采用MobileNetV2作为主干,实现了精确的目标检测和识别。最后,在Prescan上对典型场景下的超视距感知增强方法进行了验证和分析。结果表明,该方法有助于自动驾驶汽车有效地充分利用感知数据,扩大感知范围,避免盲区,对自动驾驶汽车的安全高效运行起到支撑作用。
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引用次数: 0
Stochastic High-order Fully-actuated Systems: Model, Equivalence and Stabilization 随机高阶全驱动系统:模型、等价与稳定
Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055307
Xueqing Liu, Maoyin Chen, Li Sheng, Donghua Zhou
This paper develops a stochastic high-order fully-actuated systems model that complements the existing highorder fully-actuated system methodology. Different from the deterministic model, stochastic signals can be considered in the proposed version. By employing a high-order operator, the equivalent control and stabilization control laws are obtained to guarantee the global asymptotical stability in probability of the closed-loop system. Finally, the simulation results show the effectiveness of the proposed control schemes.
本文建立了一种随机高阶全驱动系统模型,补充了现有的高阶全驱动系统方法。与确定性模型不同,该模型可以考虑随机信号。利用高阶算子,得到了保证闭环系统全局概率渐近稳定的等效控制律和镇定控制律。最后,仿真结果验证了所提控制方案的有效性。
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引用次数: 2
Fixed-Time Event-Based Cooperative Control Algorithm Design for Multi-Agent Systems based on Time Base Generator 基于时基生成器的多智能体系统固定时间事件协同控制算法设计
Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10056106
Ruitian Yang, Lizhang Peng, Yongqing Yang, Lei Wang
Practical fixed-time bipartite consensus problem for multi-agent systems on undirected graphs is studied in this article. A new event-triggered control protocol is constructed, which incorporates fully distributed way and time base generator(TBG). The application of fully distributed control mechanism makes the controller based on local information, rather than global information. The settling time can be determined in advance due to the application of TBG, which is not affected by initial states. Some conditions are proposed to guarantee the achievement of practical fixed-time bipartite consensus and the avoidance of Zeno behavior. Finally, a numerical example is given to illustrate the effectiveness of the control algorithm.
研究无向图上多智能体系统的实际定时二部一致性问题。构造了一种新的事件触发控制协议,该协议结合了全分布式方式和时基发生器(TBG)。全分布式控制机制的应用使得控制器基于局部信息而不是全局信息。由于TBG的应用,沉降时间可以提前确定,不受初始状态的影响。提出了保证实现实际的定时二部共识和避免芝诺行为的条件。最后通过数值算例说明了控制算法的有效性。
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引用次数: 0
Path Tracking Control of Four-wheel Independent Driving High Ground Clearance Sprayer Considering Rollover 考虑侧翻的四轮独立驱动高离地间隙喷雾器路径跟踪控制
Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10056028
Hepei Zhang, Guohai Liu, Duo Zhang, Yue Shen, Zijie Wang, Yan Xu
In this research, the path tracking control and the anti-roll control are constructed based on the kinematic model of the sprayer, respectively. The upper layer uses model predictive control (MPC) to output the desired steering angle and speed of the sprayer based on the current state of the sprayer and the desired path to achieve path tracking control. The lower layer controller uses lateral load transfer rate (LTR) as a measure of rollover to judge whether the vehicle state produces rollover, and compensates for steering angle by introducing a fuzzy controller, which in turn controls the LTR to stabilize at a certain threshold value. So that the sprayer has both better control accuracy and better safety in the process of path tracking, and ensure that the sprayer does not roll. The combined simulation results of ADAMS/MATLAB show that the LTR can be controlled within 0.5 when the sprayer adopts anti-roll control under complex road conditions, which ensures the safety of the sprayer, and the lateral deviation reaches 0.13m, which has a high path tracking accuracy.
在本研究中,基于喷雾器的运动学模型,分别构建了路径跟踪控制和防侧倾控制。上层采用模型预测控制(MPC),根据喷雾器的当前状态和期望路径输出喷雾器的期望转向角度和速度,实现路径跟踪控制。下层控制器以横向载荷传递率(LTR)作为侧翻的度量来判断车辆状态是否产生侧翻,并通过引入模糊控制器对转向角进行补偿,模糊控制器控制LTR稳定在某一阈值。使喷雾器在轨迹跟踪过程中既具有更好的控制精度,又具有更好的安全性,保证喷雾器不打滚。ADAMS/MATLAB联合仿真结果表明,在复杂路况下,喷雾器采用防侧倾控制时,LTR可控制在0.5以内,保证了喷雾器的安全性,横向偏差达到0.13m,具有较高的路径跟踪精度。
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引用次数: 0
期刊
2022 China Automation Congress (CAC)
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