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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Jianzhe Fu Yanmei Jia Zongyang Lv Yuhu Wu School of Control Science and Engineering Dalian University of Technology Dalian China School of Science Dalian Minzu University Dalian China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This study presents an adaptive control system for controlling the motion of a quadrotor transporting a cable-suspended payload. The technique uses adaptive updating rules to estimate the unknown disturbance operating on the pay-load. This work employs the Lyapunov method and LaSalle’s invariance theorem to examine and demonstrate that the suggested algorithm ensures the control system’s asymptotic stability. The simulation at the end of the paper validates the performance of the constructed adaptive controller.

关键词： Asymptotic stability Estimation error Automation Stability analysis Closed loop systems Adaptive control Payloads

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Differentiable Optimization Based Time-Varying control Barrier Functions for Dynamic Obstacle Avoidance

arXiv

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arXiv 2023年

作者： Dai, Bolun Khorrambakht, Rooholla Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

control barrier functions (CBFs) provide a simple yet effective way for safe control synthesis. Recently, work has been done using differentiable optimization (diffOpt) based methods to systematically construct CBFs for static obstacle avoidance tasks between geometric shapes. In this work, we extend the application of diffOpt CBFs to perform dynamic obstacle avoidance tasks. We show that by using the time-varying CBF (TVCBF) formulation, we can perform obstacle avoidance for dynamic geometric obstacles. Additionally, we show how to extend the TVCBF constraint to consider measurement noise and actuation limits. To demonstrate the efficacy of our proposed approach, we first compare its performance with a model predictive control based method and a circular CBF based method on a simulated dynamic obstacle avoidance task. Then, we demonstrate the performance of our proposed approach in experimental studies using a 7-degree-of-freedom Franka Research 3 robotic manipulator. © 2023, CC BY.

关键词： Model predictive control

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Parameter Estimation of Synchronous Generator Using Neural controlled Differential Equations

Parameter Estimation of Synchronous Generator Using Neural C...

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International Conference on control and Automation (ICCA)

作者： Zhun Yin Hong Wang Zhong-Ping Jiang Department of Electrical and Computer Engineering Control and Networks Lab Tandon School of Engineering New York University Brooklyn NY USA Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper introduces a synchronous generator modeling method based on neural controlled differential equations (neural CDEs) using online sampled data. This method begins with a fifth-order generator model, where every trainable parameter is regarded as one parameter of the fifth-order generator model. The objective is to use the real-time data to learn these parameters. A training algorithm has been formulated, and it has been shown that the combination of the proposed neural CDEs and the fifth-order model can produce desired online parameter estimations for the synchronous generator. The simulation results show that the proposed method can generate a very accurate estimation and model predictions with the mean absolute percentage error of 0.04638%.

关键词： Training Parameter estimation Accuracy Voltage Differential equations Predictive models Mathematical models

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Equilibrium-Independent control of Continuous-Time Nonlinear Systems via the LPV Framework - Extended Version

arXiv

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arXiv 2023年

作者： Koelewijn, Patrick J.W. Weiland, Siep Tóth, Roland The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, we consider the analysis and control of continuous-time nonlinear systems to ensure universal shifted stability and performance, i.e., stability and performance w.r.t. each forced equilibrium point of the system. This "equilibrium-free" concept is especially beneficial for control problems that require the tracking of setpoints and rejection of persistent disturbances, such as input loads. In this paper, we show how the velocity form, i.e., the time-differentiated dynamics of the system, plays a crucial role in characterizing these properties and how the analysis of it can be solved by the application of Linear Parameter-Varying (LPV) methods in a computationally efficient manner. Furthermore, by leveraging the properties of the velocity form and the LPV framework, a novel controller synthesis method is presented which ensures closed-loop universal shifted stability and performance. The proposed controller design is verified in a simulation study and also experimentally on a real system. Additionally, we compare the proposed method to a standard LPV control design, demonstrating the improved stability and performance guarantees of the new approach. Copyright © 2023, The Authors. All rights reserved.

关键词： controllers

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Eco-Friendly 0G Networks: Unlocking the Power of Backscatter Communications for a Greener Future

arXiv

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arXiv 2024年

作者： Javaid, Shumaila Fahim, Hamza He, Bin Saeed, Nasir Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201210 China Department of Electrical and Communication Engineering College of Engineering UAE University Al-Ain15551 United Arab Emirates

Backscatter Communication (BackCom) technology has emerged as a promising paradigm for the Green Internet of Things (IoT) ecosystem, offering advantages such as low power consumption, cost-effectiveness, and ease of deployment. While traditional BackCom systems, such as RFID technology, have found widespread applications, the advent of ambient backscatter presents new opportunities for expanding applications and enhancing capabilities. Moreover, ongoing standardization efforts are actively focusing on BackCom technologies, positioning them as a potential solution to meet the near-zero power consumption and massive connectivity requirements of next-generation wireless systems. 0G networks have the potential to provide advanced solutions by leveraging BackCom technology to deliver ultra-low-power, ubiquitous connectivity for the expanding IoT ecosystem, supporting billions of devices with minimal energy consumption. This paper investigates the integration of BackCom and 0G networks to enhance the capabilities of traditional BackCom systems and enable Green IoT. We conduct an in-depth analysis of BackCom-enabled 0G networks, exploring their architecture and operational objectives, and also explore the Waste Factor (WF) metric for evaluating energy efficiency and minimizing energy waste within integrated systems. By examining both structural and operational aspects, we demonstrate how this synergy enhances the performance, scalability, and sustainability of next-generation wireless networks. Moreover, we highlight possible applications, open challenges, and future directions, offering valuable insights for guiding future research and practical implementations aimed at achieving large-scale, sustainable IoT deployments. © 2024, CC BY-SA.

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A Comparative Evaluation of GA and PSO Tuned PID controllers for the Quadruple Tank System

A Comparative Evaluation of GA and PSO Tuned PID Controllers...

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Information and Communication Technology for Development for Africa (ICT4DA), International Conference on

作者： Gashaye L. Hailu Tefera T. Yetayew Electrical and Computer Engineering Faculty Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Power and Control Engineering Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798331527792

ISBN: (纸本)9798331527808

In this research, nature inspired metaheuristic optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Techniques are formulated to tune optimal combinations of PID controller parameters for a quadruple tank level control application. The degree of relation of the best paring for both interacted control loops are justified by using Relative Gain Array (RGA) calculation. The overall system has been modeled and implemented in MATLAB/Simulink and performance evaluations has been done for time domain performance specifications of settling time, rise time, percentage overshoot and steady state error as comparison criterions of controllers. Accordingly, for the minimum phase case, GA tuned PID has settling time of 0.36sec and 0.31second for the tank 1 and tank 2 respectively. And PSO tuned PID controller has settling time of 0.96sec and 0.35sec for tank 1and tank 2 respectively. The quantitative and qualitative analysis of results reveals that there is fast dynamic response using GA PID relative to PSO PID even though the difference is not significant that may be due to stochastic feature of the metahueristic optimization algorithms. In summary, the optimal combination of PID controller algorithms’ parameters can give robust performance compared to PID controller designed using the classical approaches or manual tuning techniques.

关键词： Performance evaluation Heuristic algorithms Metaheuristics Stochastic processes Mathematical models Steady-state Time-domain analysis MATLAB Tuning Genetic algorithms

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An Extended Stochastic Region of Attraction Estimation Method for Power Systems Under Stochastic Sustained Disturbances

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IEEE Transactions on Power Systems 2025年

作者： Yang, Yuerong Lin, Shunjiang Liu, Mingbo Deng, Leyi Xie, Huifan Li, Qifeng South China University of Technology School of Electric Power Engineering Guangzhou510640 China Power Dispatching and Control Center CSG Guangzhou510663 China University of Central Florida Department of Electrical and Computer Engineering OrlandoFL32816 United States

Stochastic sustained disturbances (SSDs) in renewable energy output impact power system transient stability. This study addresses the dynamic performance of power systems under SSDs using Lévy-driven stochastic differential algebraic equations (LDSDAEs). The stochastic region of attraction for LDSDAEs is divided into a stable region and a probabilistically stable region, collectively termed the extended stochastic region of attraction (ESRA). Traditional methods for estimating the deterministic region of attraction (RA) are inadequate. To address this issue, this study proposes an estimation method for the ESRA of LDSDAEs based on escape probability. We first derive an integro-differential equation for escape probabilities of initial system states near the attractor of LDSDAEs. We then explore the connection between escape probability and ESRA. Finally, we propose a wavelet basis functions-based extended projection method to solve the integro-differential equation, providing an analytical expression for escape probabilities and thus determining the ESRA. Case studies on the modified IEEE 39- and 118-bus systems demonstrate the proposed method's high computational accuracy and efficiency for power systems under Lévy disturbances. © 1969-2012 IEEE.

关键词： Integrodifferential equations

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Sine-Cosine Zebra Optimization Algorithm with Mixture of Experts based Long Short-Term Memory for the Detection and Classification of Cyber Treats

Sine-Cosine Zebra Optimization Algorithm with Mixture of Exp...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： N. Tejasri R. Archana Reddy Mohammed Hussein Fallah Savita Muchakhandi Gudapalli Karuna Malla Reddy Engineering College for Women (UGC-Autonomous) Hyderabad India Department of Computer Science and Engineering SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical & Electronics Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The use of internet application is rising rapidly where the number of users is increasing day by day creates traffic in the network which led path for the growth of cybercrimes. To avoid the cyber treats various advance technologies have been introduced for the detection of unauthorised users in the network. The existing methods tried to improve the accuracy of cyber threat section model using various algorithms but did not reach the target due to the issues such as imbalanced data, variable constraints and overfitting. The paper proposed Sine-Cosine Zebra Optimization Algorithm with Mixture of Experts-Long Short-Term Memory (SCZOA-MoELSTM) for the detection and classification of cyber treats. The min-max normalization rescaled the ranges of the data. The Sine-Cosine Zebra Optimization Algorithm (SCZOA) was used for the selection of optimal features. Mixture of Experts-Long Short-Term Memory (MoELSTM) captured the long-range values of the features that enhanced the performance of cyber threat detection and classification. The proposed SCZOA-MoELSTM model obtained accuracy of 99.04% and 96.85%, precision of 98.22% and 95.73%, Detection Rate (Recall) of 98.98% and 95.67%, F1-Score of 98.59% and 95.69% respectively for CICIDS2017 and UNSW-NB15 datasets compared to the existing Long Short-Term Memory (LSTM).

关键词： Training Measurement Accuracy Feature extraction Threat assessment Classification algorithms Pattern recognition Optimization Long short term memory Overfitting

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Graph Convolutional Network with Neural Inductive Matrix Completion for Predicting Disease-Related LncRNA Genes

Graph Convolutional Network with Neural Inductive Matrix Com...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Qiang Zhang, Shuxin Dong, Suyu Xu, Long Dong, Weihe Li, Xiaokun Sun, Pengzhong Jiang, Feng Zhang, Xianyu Luo, Gongning Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology School of Medicine and Health Harbin China Heilongjiang University School of Computer Science and Technology Harbin China Harbin Institute of Technology School of Computer Science and Technology Harbin China Harbin Medical University Cancer Hospital Department of Breast Surgery Harbin China King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences & Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9798350337488

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level. © 2023 IEEE.

关键词： gene identification graph convolutional network inductive matrix completion lncRNA-disease association

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MULSAM: Multidimensional Attention with Hardware Acceleration for Efficient Intrusion Detection on Vehicular CAN Bus

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Xu, He Shi, Xiaokang Liu, Hansheng Wang, Yanwen Lu, Jiwu Zeng, Haibo Li, Renfa Wu, Di Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology Hunan Changsha410082 China Hunan University College of Electrical and Information Engineering Hunan Changsha410082 China Hunan University Shenzhen Research Institute Shenzhen518000 China Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA24061 United States Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China

controller Area Network (CAN) protocol is an efficient standard enabling communication among Electronic control Units (ECUs). However, the CAN bus is vulnerable to malicious attacks because of a lack of defense features. In this article, a novel vehicle Intrusion Detection System (IDS) is developed. The challenge is that existing techniques of Intrusion Detection Systems (IDS) rarely consider attacks with small-batch, which are characterized by their small attack scale and concealed attack patterns, posing a significant threat to driving safety. To solve this problem, we developed an algorithm model that merges Multi-dimensional Long Short-Term Memory (MD-LSTM) and Self-Attention Mechanism (SAM), shortly named MULSAM. The MULSAM model was compared with other baseline models, including Stacked LSTM, MD-LSTM, etc. Experiments show that our approach has the best detection accuracy (98.98%) and training stability. Further, to speed up the inference of MULSAM on edge, the hardware accelerator is implemented on FPGA devices using technologies such as parallelization, modular, pipeline, and fixed-point quantization. Experiments show that our FPGA-based acceleration scheme has a better energy efficiency than the CPU platform. Even with a certain degree of quantification, the acceleration model for MULSAM still displays a high detection accuracy of 98.81% and a low latency of 1.88 ms. © 1982-2012 IEEE.

关键词： Intrusion detection

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