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Path-Follower-2: A Narrow Environment Navigation System for Large Mobile Inspection Robot

Path-Follower-2: A Narrow Environment Navigation System for ...

引用

data driven control and learning systems (ddcls)

作者： Shuang Wei Junqiang Chen Muhua Zhang Yun Gan Hongrui Chen Deqing Huang School of Electrical Engineering Southwest Jiaotong University Chengdu P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In industrial settings, mobile inspection robots, often large and heavy, are specifically designed for operation in confined environments. However, their considerable size often limits their ability to perform autonomous global path planning and self-directed obstacle avoidance. Typically, these robots adhere to pre-defined routes, executing high-precision maneuvers, halting for obstacles, and stopping at designated points for data collection. Maneuvering safely and efficiently in these restricted spaces is vital for their detection tasks and largely depends on their strict adherence to the pre-set global path. this paper introduces Path-Follower-2, a narrow environment navigation system for such robots, overcoming the limitations of existing methods that focus mainly on single functionalities like obstacle parking and avoidance. It integrates a novel obstacle descriptors, a global path preprocessing method for early narrow space detection, and adaptive collision detection for precise trajectory tracking in tight spaces. Tested in the Gazebo simulation against static and dynamic obstacles, Path-Follower-2 exhibits exceptional accuracy and smoothness in path tracking within narrow spaces, significantly surpassing most existing solutions.

关键词： Accuracy Service robots Navigation Trajectory tracking Inspection Aerospace electronics Robot sensing systems

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Enhancing Underwater Acoustic Signal Classification with CAM++ and Change Point Features

Enhancing Underwater Acoustic Signal Classification with CAM...

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data driven control and learning systems (ddcls)

作者： Yu Li Qiyang Xiao Kexue Hu Yuhao Fang Junwei Duan School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the effective classification of vessel acoustic signals is critical for marine environment monitoring. In this paper, we introduce two innovations: First, we developed a novel preprocessing method that combines Constant Q Transform (CQT) with a feature extraction technique introduced through dynamic programming, aiming to enhance the representation of signals by identifying statistical characteristics among signal change points. Second, we applied the CAMPPlus model, originally designed for speaker verification, to the classification of vessel acoustic signals, demonstrating its capability to significantly improve classification performance. Utilizing a dataset from Ocean Networks Canada, which contains signals recorded in the Strait of Georgia during the summer and fall of 2017, we classified the acoustic data into five categories: Tug, Passenger Ship, Cargo, Tanker, and Background Noise. A comparative performance analysis of the CAMPPlus model against the traditional ResNETSE model on this dataset revealed that CAMPPlus, combined with our proposed preprocessing and feature extraction techniques, significantly outperforms ResNETSE in terms of classification accuracy. this research not only enhances the accuracy of acoustic signal classification but also successfully transfers a mature deep learning model to a new application scenario. It demonstrates the potential of deep learning in model transfer and provides a new perspective for further research in the field of marine traffic monitoring, adhering to the standards of academic presentation in the process.

关键词： Deep learning Analytical models Accuracy Pattern classification Transforms Feature extraction data models

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On Prescribed Performance ADRC for a Class of Nonlinear systems with Uncertain control Input Gain

On Prescribed Performance ADRC for a Class of Nonlinear Syst...

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data driven control and learning systems (ddcls)

作者： Zhixiang Chen Wenchao Xue Ruiqiao Yao Sen Chen Xiaojing Song Qingzhou High-tech Institute Weifang P. R. China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing P. R. China School of Mathematics and Statistics Shaanxi Normal University Xi'an P. R. China Beijing Institute of Control and Electronic Technology Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper considers the prescribed performance tracking problem for nonlinear systems with uncertain control input gain. A novel prescribed performance active disturbance rejection control design featured with low computational complexity and large feasible range of the nominal control input gain is proposed. To deal with the tracking error constraint, an output error transformation is introduced to transform the constrained variable into an unconstrained one. A high-gain observer and sliding surface technique are combined to stabilize the converted unconstrained control system. To reduce the computational complexity, a reduced-order extended state observer is designed to estimate the system uncertainties and the high-order derivatives terms of error transformation, which arise from the ‘explosion of terms’. Moreover, compared with the existing prescribed performance ADRC, the proposed ADRC method needs only the control direction, relaxing the strong assumption on the nominal control input gain. It can be proved that the resulting closed-loop system is semi-globally uniformly ultimately bounded (SGUUB), which ensures the prescribed performance of the tracking error. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.

关键词： Robust control learning systems Uncertainty Simulation Transforms Observers Explosions

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Interval Weight State Update Approach with Deep Reinforcement learning for Dynamic Load Change Tasks in Air Separation Processes

Interval Weight State Update Approach with Deep Reinforcemen...

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data driven control and learning systems (ddcls)

作者： Ruiyu Qiu Guanghui Yang Zuhua Xu Zhijiang Shao State Key Lab of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou P. R. China HuZhou Research Institute of Industrial Control Technology

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the air separation unit (ASU) is important for producing high-purity oxygen, nitrogen, and argon, and dynamic load change operation helps meet the needs of downstream industries. the ASU has an extremely long delay, and the requirements for this operation are strict, and model predictive control (MPC) is usually used for this task. However, the burden of online MPC computation is a key issue in improving production and economic efficiency. In this paper, a framework based on Reinforcement learning (RL) is proposed to solve the long-delayed task in ASU. the state and reward function are specially designed for this task. First, training increases the sampling time of the original model to create a low resolution environment, which makes training more efficient. As a sacrifice, the control precision is reduced in the original high resolution environment. then, an algorithm called Interval Weighted State Update (IWSU) is proposed to restore the agent and keeps the action accuracy in the original environment. the dynamic load change task works as the case in this paper. the performance of RL is compared with that of MPC, and results show that RL is almost exactly the same as MPC in key flow indicators, and can well meet the remaining content and purity range requirements. IWSU shows good performance compared with directly applying the agent to the original high resolution environment.

关键词： Training Target tracking Switches Production control systems Separation processes Nitrogen

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UGV 3D Scene Mapping and Real-Time Target Detection in Underground Space

UGV 3D Scene Mapping and Real-Time Target Detection in Under...

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data driven control and learning systems (ddcls)

作者： Shuai Duan Jingbang Xu Zhixin Huang Jing Wang Meng Zhou School of Electrical and Control Engineering North China University of Technology Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper focuses on 3D scene mapping and real-time target detection techniques for underground space based on small UGV. Both techniques are essential for small UGV to accomplish tasks in underground spaces. Complex environment and insufficiently light make some techniques inapplicable in underground space. the limited calculation power of small UGV leads to complex technique that cannot satisfy real-time requirements. In this paper, LiDAR is chosen as the sensor for mapping and target detection to enable small UGV accomplish real-time tasks in underground space. Considering factors such as calculation power and accuracy, this paper applies the LeGO-LOAM algorithm for localization and mapping, and the PointPillars algorithm for target detection. the experimental environments in this paper are chosen in an underground garage and NCUT campus to verify the algorithm deployed in small UGV. the experimental results show that the deployed algorithm has good robustness to build accurate maps and target detection in real time. In summary, the work in this paper provides important technical support for underground space mapping and target detection based on small UGV.

关键词： Location awareness three-dimensional displays Accuracy Pipelines Object detection Space mapping Aerospace electronics

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Two-Dimensional Statistical Pattern Analysis for Effective Modeling and Monitoring of Limited Batch Process data in Food Industry

Two-Dimensional Statistical Pattern Analysis for Effective M...

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data driven control and learning systems (ddcls)

作者： Jinlin Zhu Zheng Zhang State Key Laboratory of Food Science and Resources Jiangnan University Wuxi P.R.China School of Food Science and Technology Jiangnan University Wuxi P.R.China Department of Chemical and Biological Engineering Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this study presents a novel two-dimensional statistical pattern analysis (2D-SPA) method, specially designed to address the challenges inherent in monitoring and modeling processes within low-volume, high-quality food manufacturing settings. By ingeniously integrating robust statistical techniques within a two-dimensional sliding window framework, this approach significantly expands the analytical dataset by operating across both batch and time dimensions, effectively overcoming the constraints posed by limited batch sizes. this innovative strategy not only augments the detection and diagnosis capabilities for subtle process deviations and emerging trends but also capitalizes on the dynamic statistical variances observed across batches and over time, thus offering a comprehensive view of the process variability. the application of 2D-SPA is meticulously validated through an industrial case study focused on lactic acid bacteria fermentation, where it demonstrates marked improvements in accuracy and sensitivity for process monitoring. the results underscore the method's potential to significantly enhance quality assurance measures in food batch production.

关键词： Temperature sensors Temperature measurement Analytical models Quality assurance Food manufacturing Batch production systems Market research

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Design and Stability Analysis of Vehicle Platooning control in Zero-Trust Environment

Design and Stability Analysis of Vehicle Platooning Control ...

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data driven control and learning systems (ddcls)

作者： Ling Zhu Darong Huang Yuhong Na Xinrong Li Institute of Information Science and Engineering Chongqing Jiao tong University Chongqing P. R. China School of Artificial Intelligence Anhui University Hefei P.R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Vehicle platoon control constitutes a pivotal aspect of intelligent transportation system development, most of the previous research on vehicle platoon control is carried out under mutual default trust, without considering the trust factor between interconnected vehicles, the trust factor between vehicles has a significant influence on the cooperative behavior of the vehicles, the way of information sharing, and the security of the vehicles, etc. In the actual traffic scenario, vehicle cooperative process is often In real transportation scenarios, the process of vehicle collaboration is often subject to various network attacks, so vehicle collaboration is always facing security threats. Zero-trust network is a kind of de-boundary network, which requires individuals in the network to “never trust and verify in real-time”, and vehicle platoon control can be placed in the zero-trust environment to avoid these problems. Firstly, we employ the C-F uncertainty inference method to assess the trust of nodes of interconnected vehicles. Secondly, we constructed a vehicle platoon dynamics model containing node trust, and based on this, we constructed a vehicle platoon dynamics model containing node trust, and designed a distributed controller. Finally, the effectiveness of the method proposed in this paper is verified by numerical simulation experiments.

关键词： Uncertainty Collaboration Process control Transportation Numerical simulation Stability analysis Real-time systems

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A Fault Diagnosis Method for Key Equipment of Bearer Network Transmission Based on Knowledge Graph and Multi-modal Neural Network

A Fault Diagnosis Method for Key Equipment of Bearer Network...

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data driven control and learning systems (ddcls)

作者： Weilin Chen Qiang Qu Huifeng Guo Haibin Li Zehui Mao College of Automation Engineering Nanjing University of Aeronautics and Astronautics Naniing P. R. China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Aiming to address the challenge of incorporating multiple fault phenomena and utilizing accompanying information such as negation and time adverbials in the process of diagnosing faults in key equipment for bearer network transmission, this study proposes a fault diagnosis method based on knowledge graph and multi-modal neural network. By constructing a knowledge graph from a fault diagnosis knowledge base, this method retrieves candidate fault root causes based on the fault phenomena and extracts subgraphs along the reasoning paths from the fault phenomena to the candidate fault root causes in the knowledge graph. Subsequently, leveraging both the reasoning paths within the subgraphs and the fault maintenance texts, the multi-modal neural network calculates the confidence levels of the candidate fault root causes, selecting the fault root cause with the highest confidence as the reasoning conclusion. Finally, the proposed method constructs a knowledge graph based on the fault diagnosis knowledge base of key equipment for bearer network transmission provided by a domestic enterprise, and validates the efficacy of the approach using fault data employed to train the multi-modal neural network. Results demonstrate that this method effectively leverages multiple fault phenomena and their accompanying information in fault maintenance texts for accurate fault diagnosis, yielding favorable performance.

关键词： Fault diagnosis learning systems Neural networks Knowledge based systems Knowledge graphs Predictive models control systems

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Information Entropy-Based Fault Detection for Quality-Related Processes

Information Entropy-Based Fault Detection for Quality-Relate...

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data driven control and learning systems (ddcls)

作者： Yongye Zhu Jinglin Zhou College of Information Science & Technology Beijing University of Chemical Technology Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Canonical Correlation Analysis (CCA) is currently very popular in the field of data-driven fault detection. the CCA method usually assumes that the data obeys a Gaussian distribution, but the sample data collected in the actual complex industrial production process often has non-Gaussian characteristics, and the CCA method is not very suitable for modeling this kind of data. In order to extend the applicability of the CCA method from Gaussian data space to non-Gaussian data space, this paper proposes an information entropy-based quality-related process fault detection method (ECCA) based on CCA. First, the ECCA approach takes as its objective function to maximize the mutual information of typical pairs of variables in the space of process and quality variables. the objective function is also optimized using Particle Swarm Optimization (PSO) algorithm to solve for the optimal pairs of typical variables; Second, when considering the extraction of the next pair of independent typical variable pairs, the extraction of independent typical variable pairs can be achieved by resorting to the framework of the Partial Least Squares (PLS) algorithm in combination with the minimization of the information entropy of the residuals in the space of the process and quality variables; third, a truncation criterion for the number of typical variables is identified; Fourth, T 2 and Te 2 statistics are constructed to monitor the system; Finally, the ECCA method is applied to synthetic data and TE process data for comparison with the traditional CCA method. Experimental validation shows that the ECCA method has better monitoring performance than the traditional CCA method in monitoring quality-related process faults.

关键词： Correlation Fault detection Process control Linear programming data mining Object recognition Information entropy

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Structure-constrain Source Free Domain Adaptation with Regularized Knowledge Distillation of Source Model

Structure-constrain Source Free Domain Adaptation with Regul...

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data driven control and learning systems (ddcls)

作者： Dan Yang Xin Peng Cheng Su Dayu Tan Chaoyang Chen Weimin Zhong Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Transfer learning aims to leverage the knowledge of the source domain to help the learning of unlabeled target domain models. However, all conventional transfer learning methods assume that samples from source and target domains are always freely accessible. However, this assumption is rarely plausible in real-world due to data privacy. To combat this challenge, we introduce Structure-constrain Source Free Domain Adaptation with regularized Knowledge Distillation of source model (SSFDA-KD). Our method is based on the observations that a deterministic classification result can be obtained when the target data distribution aligns with the source data distribution, and a well-trained model not only yields a deterministic classification for a sample but also has clear category boundaries, which implies samples that are close together belong to the same category. From those, we first generate the source model with labeled source data, which is regarded as a well-trained model. then, we identify target prototypes with lower self-entropy and assign the pseudo-labels to other samples according to the neighborhood structure. Finally, we refine the target model by minimizing prediction loss, constraining internal structure via triplet loss, and reducing knowledge distillation loss. Experiments of fault diagnosis based on WWTPs are conducted to validate its effectiveness.

关键词： Fault diagnosis learning systems Adaptation models data privacy Transfer learning Prototypes Predictive models

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