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Barrier Lypanunov Function-based Adaptive Control of an Ankle Exoskeleton

Barrier Lypanunov Function-based Adaptive Control of an Ankl...

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automation, Control and robotics Engineering (CACRE), international conference on

作者： Yapeng Wang Yunhai Geng Research Center of Satellite Technology Harbin Institute of Technology Harbin China Beijing Xinfeng Aerospace Equipment Co. Ltd. Beijing China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

the application of exoskeleton robots in industrial assembly and other fields plays a significant role in alleviating low back pain caused by work-related musculoskeletal disorders and improving work efficiency. Currently, the ankle joints of lower limb exoskeleton robots used in this field are all treated as passive joints. Ankle is important for weight-bearing joints of human lower limbs in the stability and flexibility of daily activities. To this end, the authors of this paper have designed a cable-driven ankle exoskeleton structure to achieve active dorsiflexion and plantarflexion movements in the sagittal plane. To compensate the modeling error and estimate the perturbation, neural network (NN) and nonlinear disturbance observer (NDO) are used. A time-varying barrier Lyapunov function is used to design an adaptive backtracking controller to constrain the system output error and ensure the tracking performance and safety of the robot. A healthy subject participates in the experiment wearing the cable-driven ankle exoskeleton. the experimental is conducted to validate performance of the proposed controller.

关键词： Ankle Backtracking Adaptation models Service robots Perturbation methods Exoskeletons Artificial neural networks

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Multi-Objective Optimization Design of Cable Driven Parallel Robot Based on Aircraft Spraying

Multi-Objective Optimization Design of Cable Driven Parallel...

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automation, Control and robotics Engineering (CACRE), international conference on

作者： Zonghui Yang Ting Liu Sen Jiao Xudong Chang College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China College of Aerospace Engineering Jilin Institute of Chemical Technology Jilin China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

this article focuses on the cable driven parallel robot for aircraft spraying. Based on a multi-objective optimization model, the performance of the cable driven parallel robot (CDPR) is optimized using Prairie Dogs optimization algorithm. Firstly, a static model of 8-cable 6-degree-of-freedom CDPR suitable for aircraft spraying was established, and corresponding evaluation indicators were designed for four performance indicators: workspace, average stiffness, stiffness fluctuation, and flexibility. Establish a multi-objective optimization model by processing performance indicators and solve it using the Prairie Dogs optimization algorithm. the final results indicate that the groundhog optimization algorithm based on multi-objective optimization design has a good effect on the performance optimization of CDPR, providing important reference for the design and optimization of aircraft spraying robots. this study has important theoretical and practical significance in the field of aircraft spraying robots, providing new ideas and methods for optimizing robot performance, and has certain guiding significance for engineering practice.

关键词： Parallel robots Grasslands Fluctuations Optimization models Atmospheric modeling Spraying Dogs

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Anesthesia Control Using Fractional Order Controller

Anesthesia Control Using Fractional Order Controller

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Eva-H. Dulf Paul A. Pintea Cristina I. Muresan Department of Automation Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Anesthesia plays a crucial role in every surgery. Clinicians are faced with numerous conditions that need to be monitored and controlled. A computerized monitoring and control system would help them in this challenging task. Designing a control law for general anesthesia presents numerous challenges, particularly due to the nonlinear mapping of outputs and the complexity of achieving a feasible input trajectory. While the internal states of the mathematical model are linear, the output exhibits nonlinear behavior. this paper introduces a decomposition of the standard pharmacokinetic-pharmacodynamic (PK-PD) model to develop an effective control strategy for drug administration in anesthesia. It is proposed a method to control the bispectral index (BIS), the main measure of unconsciousness of the patient. this approach aims to optimize anesthesia delivery by maintaining the BIS within a desired range, thereby enhancing patient safety and comfort. the methodology involves designing a fractional order controller to accurately track BIS reference values and adjust anesthetic infusion rates. the effectiveness of the controller is validated through simulations, demonstrating its potential to enhance the precision of anesthesia management. Key findings reveal that such type of control outperforms traditional methods in maintaining target BIS levels, minimizing the risks of over- or under-dosing.

关键词： Drugs Robust control Target tracking Surgery Anesthesia Mathematical models Trajectory Safety Standards Robots

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Multi-Scale-Rhythm Attention Feature Contrastive Learning for Epileptic Seizure Prediction

Multi-Scale-Rhythm Attention Feature Contrastive Learning fo...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Yifan Wang Wenkang Liu Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

the electroencephalogram (EEG) signals based epileptic seizure prediction remains a pivotal technique to improve the living standards for epilepsy patients. However, recent prediction models focus on the local feature extraction with smaller receptive field, which ignores multi-level global feature changes in pre-ictal period. Besides, traditional supervised or semi-supervised learning strategies requires sufficient data in individual patient, and it is difficult to handle the representation learning of imbalanced samples of intra-patient EEG data, yielding a suboptimal performance in predicting seizures. To overcome above limitations, a multi-scale-rhythm attention feature contrastive learning (MSR-AFCL) model is proposed for the prediction of seizures in individual patients. First, a feature extractor called the multi-scale-rhythm (MSR) employs multi-scale dilated convolution module and multi-rhythm power spectrum density (PSD) estimate module, so as to extract multi-level spatio-temporal features and rhythmic temporal-spectral features with large receptive field. then, an attention feature contrastive learning (AFCL) strategy is developed, and it has the capability to dynamically adjust the intra-class and inter-class distances of attention fusion representations. the experimental results indicate that our proposed MSR-AFCL model achieves outstanding seizure warning when evaluated on the publicly available CHB-MIT dataset, and it outperforms the state-of-the-art methods.

关键词： Representation learning Service robots Epilepsy Contrastive learning Predictive models Semisupervised learning Feature extraction Brain modeling Electroencephalography Standards

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Chat, Summary and Diagnosis: A LLM - Enhanced Conversational Agent for Interactive Depression Detection

Chat, Summary and Diagnosis: A LLM - Enhanced Conversational...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Xiaoheng Zhang Weigang Cui Junjie Wang Yang Li Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Depression is a significant mental illness that affects how individuals express their emotions and engage with others, making communication challenging. Most depression assessment tools utilize self-report questionnaires, such as the Patient Health Questionnaire (PHQ-9). these psychometric instruments can be easily adapted to electronic forms. However, this approach cannot provide human-like explanations and interactions, leading to a poor interactivity. Furthermore, we have identified critical limitations in previous prompting methods. they are either constrained to queries using a single identifiable relation, or being agnostic to input contexts, making it difficult to capture variabilities that occur across different inference steps. To solve these issues, we develop a large language models LLM-enhanced conversational agent for depression detection, which makes it more effective and interactive. Specifically, we first explore an iterative knowledge-aware prompter (IKP), a new prompting paradigm which inject specific knowledge from language models progressively for multi-step reasoning, which learns to synthesize prompts conditioned on the current step's contexts. Second, our proposed system introduces a multi-step diagnosis (MSD) approach. Our system not only delivers a diagnosis but also generates a symptom summary through interactive conversations. Our proposed agent enables users to have interactive natural language dialogues with the system, enhancing their personalized comprehension of mental states. Our experiments demonstrate the effectiveness of the iterative knowledge-aware prompter design.

关键词： Control engineering Service robots Large language models Instruments Natural languages Oral communication Depression Iterative methods Next generation networking Context modeling

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FedTMatch: Self-Supervised Federated Semi-Supervised Learning with Dynamic threshold

FedTMatch: Self-Supervised Federated Semi-Supervised Learnin...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Qing Zhao Jielei Chu Zhaoyu Li Hua Yu Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China CREC R&D Center for Carbon Peaking and Carbon Neutrality China Railway Eryuan Engineering Group Co. Ltd. Chengdu China China E-port Data Center Chengdu Branch Chengdu China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Federated semi-supervised learning (FSSL) has emerged as a promising paradigm to address the challenges of data privacy and the scarcity of labeled data. Most existing algorithms adapt the pseudo-labeling and consistency regularization methods in traditional semi-supervised learning (SSL) to exploit raw data from unlabeled clients. However, these methods always use the same high fixed threshold to filter out noisy pseudo-labels for each class, without considering the varying learning conditions and difficulty levels associated with different classes, resulting in a decline in the performance of global model, especially under non-Independent and Identically Distributed (Non-IID) settings. To this end, we propose FedTMatch, a learning framework which adjusts the threshold for each class based on the learning status of each client. Furthermore, we introduce a self-supervised rotation loss in unlabeled clients to increase the utilization of raw data and correct the feature space formed by pseudo-label data to weaken the impact of Non-IID data distribution on global model's performance. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our approach.

关键词： Data privacy Control engineering Service robots Computational modeling Semisupervised learning Predictive models Filtering algorithms Data models Noise measurement Convergence

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Parameters optimization design of high-speed reluctance coil launchers

Parameters optimization design of high-speed reluctance coil...

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Pulsed Power conference, international conference on High-Power Particle Beams and international Symposium on Electromagnetic Launch Technology (EAPPC/BEAMS/EML), Euro-Asian

作者： Yadong Zhang Ao Zhou Zhiqiang Sun State Key Laboratory of Power Grid Environmental Protection School of Electrical Engineering and Automation Wuhan University Wuhan China Guangzhou GuoYao Technology Co. Ltd Guangzhou China

ISBN: (数字)9789038661353

ISBN: (纸本)9798350352733

Muzzle speed and efficiency are two important factors that constrain the development of reluctance coil launchers. In order to improve the muzzle speed of the reluctance coil launcher, the number of stages of the launcher is bound to increase, which leads to the complication of the simulation model, long simulation time consuming and low optimisation efficiency. Aiming at the above problems, this paper proposes a fast optimisation method for high-speed reluctance coil launchers based on genetic algorithm and machine learning. Firstly, based on a 7-stage reluctance coil launcher, determine that the wire diameter and the number of coil layers are the optimization objects; based on machine learning, the wire diameter and the number of coil layers are the optimization objects. Based on machine learning, the fast calculation model is trained with the wire diameter and the number of coil layers as inputs and the muzzle speed as output; the fast calculation model is invoked with the genetic algorithm, and the maximum muzzle speed of the launcher is taken as the fitness function, so as to achieve the fast optimisation of the parameters of the high-speed reluctance coil launcher stage by stage. the results show that after optimisation, an muzzle speed of 113.59m/s can be achieved under the condition of 32-stage launcher with an efficiency of 9.96%.

关键词： Coils Training Particle beams Optimization methods Machine learning Predictive models Finite element analysis Wire Electromagnetics Genetic algorithms

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis

Personalized Federated Learning with Collaborative Aggregati...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Qian Si Yang Li School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Advanced Institute of Information Technology Peking University Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). this framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. the shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. this approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques.

关键词： Training Adaptation models Data centers Federated learning Service robots Collaboration Brain modeling Data models Servers Medical diagnostic imaging

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Enhancing Photovoltaic Array Performance under Partial Shading through Dynamic Reconfiguration and Layer Equalization Algorithm

Enhancing Photovoltaic Array Performance under Partial Shadi...

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international Carpathian Control conference (ICCC)

作者： Fatimah Ameen Attila Trohák Abdulrahman Siddiq Rabab Benotsmane Institute of Automation and Info-Communication University of Miskolc Miskolc Hungary Dept. of Electronic and Control Eng. Technical College Northern Technical University Kirkuk Iraq

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

Partial shading (PS) significantly impacts the performance of photovoltaic (PV) systems, leading to reduced efficiency. Traditional mitigation methods involve bypass diodes or PV module reconfiguration. Accordingly, the highest and lowest layer-based exchange (HLLBE) algorithm was proposed. the HLLBE system employs a Switching Block Array (SBA) for flexible reconfiguration without rewiring. By sorting and swapping irradiance readings, the HLLBE algorithm achieves uniform current distribution across PV array layers. Unlike static configurations, HLLBE dynamically adapts to shade patterns, offering an efficient solution. Simulations on a 3 × 3 PV array demonstrated HLLBE's effectiveness in enhancing output power and reducing multiple peaks compared to conventional total-cross-tied (TCT) configurations. Additionally, HLLBE minimizes operating switches through a switching matrix during dynamic reconfiguration (DR).

关键词： Photovoltaic systems Adaptation models Heuristic algorithms Switches Current distribution Control systems Solar system

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Robust Object Recognition in Open Environments Based on Causal Inference

Robust Object Recognition in Open Environments Based on Caus...

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robotics, Control and automation Engineering (RCAE), international conference on

作者： Ruonan Liu Shuai Wu Ping Kong Wei Xie Weidong Zhang Department of Automation Shanghai Jiao Tong University Shanghai China College of Intelligence and Computing Tianjin University Tianjin China School of Information and Communication Engineering Hainan University Hainan China Department of Automation Shanghai Jiaotong University Shanghai China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

the rapid development of artificial intelligence technology has drawn widespread attention to the problem of target recognition in open environments. Traditional target recognition methods are easily affected by environmental changes and interference noise, leading to insufficient recognition accuracy and robustness. this paper proposes a robust target recognition method based on causal inference, aiming to improve the robust performance of image recognition models in open environments. the method first extracts features from images, analyzes the impact of noise features such as the environment in image features, and derives the essential feature properties of the target. then, the causal relationship is introduced into the target recognition, and by analyzing the relationship between the environment, the target, and the observation data, the features of the target are combined with the observation data to achieve accurate and stable recognition of the target. the proposed method has achieved good target recognition effects on multiple open datasets, and the experimental results show that the algorithm can effectively solve the problem of poor robustness of the target recognition model in open environments, providing strong support for practical applications. through the application of this method, the stability and robustness of the target recognition model can be significantly improved, which has important practical application value.

关键词： Accuracy Image recognition automation Target recognition Noise Interference Feature extraction Robustness Stability analysis Robots

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