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2nd ieee-Industrial-Electronics-Society Annual On-Line conference (ONCON)

作者： Shafieeroudbari, Elham Iyer, Lakshmi Varaha Kar, Narayan C. Univ Windsor Dept Elect & Comp Engn Windsor ON Canada Magna Int Inc Corp R&D & Engn Troy MI USA

ISBN: (纸本)9798350357974

Model predictive control (MPC) has become a cornerstone in controlling permanent magnet synchronous motor (PMSM) drives powered through voltage-source inverters. However, MPC models rely on an accurate mathematical model of the PMSM and are generally built based upon accurate parameters of the motor. This in turn poses significant challenges in realtime control of PMSMs because of dynamic variations of motor parameters. To tackle this shortcoming of conventional MPC approaches, this paper is devoted to the design of a novel and efficient model-free data-driven control strategy incorporating an evolving fuzzy system (EFS). In particular, the proposed method initially makes use of historical data collected from the MPC solution to the control problem. The structure and parameter learning of EFSs is then realized through the collected data. The constructed data-driven EFS model is then employed in place of the MPC module to deal with the control problem, enabling the real-time solution. The resulting predictive EFS model, equipped with a rule base, achieves superior steady-state and dynamic performance compared with the MPC solution without relying on the accurate mathematical modeling of the PMSM. The attained results validate the robustness and effectiveness

关键词： data-driven modelling evolving fuzzy systems permanent magnet synchronous motors predictive current control

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Deep Reinforcement learning-Based Hierarchical Motion Planning Strategy for Multirotors

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ieee TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第6期21卷 4324-4333页

作者： Hua, Hean Wang, Yaonan Zhong, Hang Zhang, Hui Fang, Yongchun Hunan Univ Sch Robot Changsha 410082 Peoples R China Natl Engn Res Ctr Robot Visual Percept & Control T Changsha 410082 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Nankai Univ Inst Robot & Automat Informat Syst Coll Artificial Intelligence Tianjin 300353 Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot Tianjin 300353 Peoples R China

This article proposes a novel hierarchical motion planning strategy for multirotors, where the virtual goal (VG) oriented deep reinforcement learning (RL) and motion optimization are designed cooperatively to achieve efficient, flexible and smooth navigation in unknown environments. Specifically, the intelligent hierarchical motion planning is achieved in a three-step design. First, the dynamic VG generation algorithm is proposed considering the perception range of onboard sensors and current velocity, which transforms the global navigation into a real-time point-to-VG planning, thereby guaranteeing efficient computation even in resource-limited multirotors. Second, instead of generating motion actions, the upper-layer deep RL is designed to make spatial-temporal decisions of VG online, which outputs time allocation and spatial distribution commands according to current observation. Third, based on upper-layer's decisions, local optimization and control are implemented accordingly. Different from existing solutions, high-performance planning is guaranteed by the online VG oriented intelligent decision making, where the data-driven learning and model-driven optimization are integrated to navigate the multirotors. Comparative experiments are carried out in both physical simulation and indoor environments, which demonstrate the satisfactory performance of the proposed motion planning strategy in terms of feasibility, efficiency, navigation smoothness, and flexibility.

关键词： Planning Navigation Optimization Real-time systems Trajectory Training Heuristic algorithms Dynamics Robots Proposals Deep reinforcement learning (RL) hierarchical multirotor planning local trajectory generation real-time navigation real-world experiments

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Iterative Fault-Tolerant control Strategy for Urban Traffic Signals under Signal Light Failure 13

Iterative Fault-Tolerant Control Strategy for Urban Traffic ...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Liang, Shuo Yan, Fei College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan030024 China College of Automation and Information Engineering Xi'an University of Technology Xi'an710048 China

ISBN: (纸本)9798350361674

Most existing signal control methods in urban transportation areas have not taken into account the impact of signal light failures in actual road networks. By introducing a signal fault model on the basis of a macroscopic traffic flow model, a traffic signal control strategy based on iterative learning fault-tolerant control is proposed to address the overall signal control problem of urban road networks in the presence of traffic signal faults. When a signal light in the road network malfunctions and the fault is restored, by controlling the traffic signal, the queue length of vehicles in each lane at the intersection of the road network tends to be balanced, thereby improving road traffic efficiency. At the same time, state feedback is introduced into the control law to reduce the number of vehicles entering the intersection with faulty signal lights, reduce the impact on traffic flow when the signal light fails, and suppress the impact caused by periodic disturbances within the region. Finally, the convergence of the method was proved through mathematical derivation, and a linear matrix inequality was given to determine the controller gain. The simulation experiment results show that the proposed method can effectively reduce the impact of signal light failures on urban traffic flow. © 2024 ieee.

关键词： State feedback

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An Efficient Matching Game Approach to Association Formation in UAV-Enabled Hierarchical Distributed learning

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ieee TRANSACTIONS ON CYBERNETICS 2024年第10期54卷 5696-5707页

作者： Huo, Xin Zhang, Hao Wang, Zhuping Yan, Huaicheng Liu, Chun Tongji Univ Dept Control Sci & Engn Natl Key Lab Autonomous Intelligent Unmanned Syst Shanghai 200092 Peoples R China Tongji Univ Frontiers Sci Ctr Intelligent Autonomous Syst Minist Educ Shanghai 200092 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China

Distributed machine learning has emerged as a promising data processing technology for next-generation communication systems. It leverages the computational capabilities of local nodes to efficiently handle large datasets, creating highly accurate data-driven models for analysis and prediction purposes. However, the performance of distributed machine learning can be significantly hampered by communication bottlenecks and node dropouts. In this article, a novel unmanned aerial vehicle (UAV)-enabled hierarchical distributed learning architecture is proposed to support machine learning applications, e.g., regional monitoring. Multiple UAV receivers (URs) are introduced as wireless relays to improve the communication between the UAV transmitters (UTs) and the cloud server. Our objective is to identify the optimal UT-UR association to maximize the social welfare of the network, which is distinctly different from the existing works that focus on the unilateral profit-maximizing problem. We formulate a two-side many-to-one matching game to model the UT-UR association problem, and a two-phase many-to-one matching algorithm is designed to identify the stable matching. The validity of our proposed scheme is verified through in-depth numerical simulations.

关键词： Game theory heterogeneous unmanned aerial vehicles (UAVs) hierarchical distributed learning matching theory

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Hybrid Attack Optimization Supported Enhanced Deep learning to Facilitate Power System Event Detection using PMU data

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ieee LATIN AMERICA TRANSACTIONS 2025年第3期23卷 223-231页

作者： Shaikh, Saba Kausar M. Kallamadi, Manjunath Ahmedabad & Inst Informat Technol Pune Inst Infrastructure Technol Res & Management Pune India Inst Infrastructure Technol Res & Management Ahmadabad Gujarat India

Accurate event detection is crucial for initiating control and protection measures in power systems to ensure enhanced and reliable operation. Phasor measurement units (PMUs) play a vital role in various functional aspects of power systems, including state estimation and intelligent protection algorithms. However, the authenticity of real-time data from PMUs must be verified before feeding it into applicable real-time algorithms to prevent undesirable or erroneous operations. This paper aims to present an efficient preprocessing methodology for identifying unwanted, incorrect, missing, or noisy PMU data to facilitate robust event detection algorithms. The proposed methodology leverages real-time data-driven deep learning techniques for authenticating incoming data. Given the high sampling rate of PMUs, the presence of extraneous data can lead to false event detection, necessitating reliable data preprocessing. Challenges identified in existing literature, such as the limitations of Steady State (SS)-Local Outlier Factor (LOF) in event detection and classification, issues with detecting line tripping and inter-area oscillations, computational and bandwidth requirements for micro-PMU installations, and false alarms resulting from inaccuracies in frequency ramp rate determination, are addressed. To overcome these challenges, this research proposes a deep learning approach that utilizes modified Deep Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) classifiers to classify and extract features from PMU data, enabling highly efficient detection of disturbances in transmission lines. Additionally, a hybrid attack optimization (HAO) technique is employed to enhance convergence rates, accuracy, and efficiency. Performance evaluation of the proposed system is conducted by calculating and assessing disturbances generated by power lines using metrics such as accuracy, precision, recall, System Average Interruption Duration Index (SAIDI), and System Average I

关键词： Phasor measurement units Event detection Feature extraction Optimization Long short term memory Accuracy Deep learning Real-time systems data models Logic gates Deep Convolution Neural Network (DCNN), event detection, Hybrid Attack Optimization (HAO), Long Short-Term Memory (LSTM), Phasor Measurement Unit (PMU).

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A Semantic-Consistent Conditional Generative Adversarial Model for Zero-Shot learning in Bearing Fault Diagnosis 13

A Semantic-Consistent Conditional Generative Adversarial Mod...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Chen, Jizheng Yang, Rui School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China School of Electrical Engineering Electronics and Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9798350361674

The scarcity of labeled data remains one of the most significant challenges in industrial fault diagnosis, where the industrial processes lack readily sampled in many fault types. This issue is often called the zero-shot learning problem in industrial fault diagnosis. To address this challenge, this paper proposes a novel fault diagnosis model for rotating machinery, leveraging Generative Adversarial Networks (GANs) and semantic consistent loss. Our approach aims to generate a rich set of samples for fault types that are difficult to collect, thereby enabling the training of an accurate classification model. The proposed model introduces a one-dimensional convolutional neural network to enhance the quality of samples and the accuracy of the model. A semantic consistent discriminator is proposed to ensure that the distribution of generated samples closely aligns with the distribution of actual samples. The performance of the model is validated using the CWRU bearing fault dataset, and the results demonstrate its efficacy in addressing the zero-shot fault diagnosis challenge. © 2024 ieee.

关键词： Generative adversarial networks

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Rational Basis Functions in Iterative learning control for Multivariable systems 62

Rational Basis Functions in Iterative Learning Control for M...

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62nd ieee conference on Decision and control (CDC)

作者： Poot, Maurice Portegies, Jim Kostic, Dragan Oomen, Tom Eindhoven Univ Technol Dept Mech Engn Eindhoven Netherlands Eindhoven Univ Technol Dept Math & Comp Sci Eindhoven Netherlands ASMPT Ctr Competency Beuningen Netherlands Delft Univ Technol Delft Ctr Syst & Control Delft Netherlands

ISBN: (纸本)9798350301243

Feedforward control with task flexibility for MIMO systems is essential to meet ever-increasing demands on throughput and accuracy. The aim of this paper is to develop a framework for data-driven tuning of rational feedforward controllers in iterative learning control (ILC) for noncommutative MIMO systems. A convex optimization problem in ILC is achieved by rewriting the nonlinear terms in the control scheme as a function of the previous feedforward parameters. A simulation study on an multivariable industrial printer shows that the developed framework converges and achieves significant better performance than direct application of the RBF algorithm using SK-iterations for SISO systems.

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Enhancing Task Performance of Learned Simplified Models via Reinforcement learning

Enhancing Task Performance of Learned Simplified Models via ...

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ieee International conference on Robotics and Automation (ICRA)

作者： Bui, Hien Posa, Michael Univ Penn GRASP Lab Philadelphia PA 19104 USA

ISBN: (纸本)9798350384581;9798350384574

In contact-rich tasks, the hybrid, multi-modal nature of contact dynamics poses great challenges in model representation, planning, and control. Recent efforts have attempted to address these challenges via data-driven methods, learning dynamical models in combination with model predictive control. Those methods, while effective, rely solely on minimizing forward prediction errors to hope for better task performance with MPC controllers. This weak correlation can result in data inefficiency as well as limitations to overall performance. In response, we propose a novel strategy: using a policy gradient algorithm to find a simplified dynamics model that explicitly maximizes task performance. Specifically, we parameterize the stochastic policy as the perturbed output of the MPC controller, thus, the learned model representation can directly associate with the policy or task performance. We apply the proposed method to contact-rich tasks where a three-fingered robotic hand manipulates previously unknown objects. Our method significantly enhances task success rate by up to 15% in manipulating diverse objects compared to the existing method while sustaining data efficiency. Our method can solve some tasks with success rates of 70% or higher using under 30 minutes of data. All videos and codes are available at https://***/view/lcs-rl.

关键词： Stochastic systems

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Framework for learning a Hand Intent Recognition Model from sEMG for FES-based control 10

Framework for Learning a Hand Intent Recognition Model from ...

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10th ieee RAS/EMBS International conference for Biomedical Robotics and Biomechatronics (BioRob)

作者： Das, Neha Endo, Satoshi Kavianirad, Hossein Hirche, Sandra Tech Univ Munich Sch Computat Informat & Technol Chair Informat Oriented Control D-80992 Munich Germany

ISBN: (纸本)9798350386523;9798350386530

Stroke survivors and individuals with neuromuscular disorders often experience motor function impairments, particularly during hand movements crucial for activities of daily living (ADL). Functional Electrical Stimulation (FES) has emerged as a potential assistive and rehabilitative technique to address these limitations. However, accurately determining user intent during FES poses a significant challenge. This work proposes a framework for rapidly learning a model of the user's hand intent from surface electromyography (sEMG) signals, specifically for continuous FES-based control of the ipsilateral hand. The framework systematically collects data from expected volitional and FES-evoked hand motions, followed by training a logistic regression model for intent classification. The study demonstrates that the proposed model can learn from limited data and compares favorably to deep neural nets trained on the same dataset. This model is able to recognize user intent with high accuracy even during concurrent FES stimulation.

关键词： Logistic regression

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Modeling and control of Robotic Manipulators Based on Symbolic Regression

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2023年第5期34卷 2440-2450页

作者： Zhang, Zhixin Chen, Zhiyong Cent South Univ Sch Automat Changsha 410083 Peoples R China Univ Newcastle Sch Engn Callaghan NSW 2308 Australia

Model-based design is an important method of addressing problems associated with designing complex control systems. For complex dynamic systems in the presence of uncertainties, the modeling process from the first principles becomes extremely tedious and simplification in mechanism and parameter measurement may result in model inaccuracy. On the contrary, machine learning has the characteristic of fitting complicated equations, which makes it widely used in the research of model identification. However, it only brings a black-box model where the design schemes based on an analytical model cannot be applied. In this article, a simple and novel scheme for modeling and control of robotic manipulators is proposed;without prior knowledge, a dynamic model in an analytical form is obtained from artificially excited training data using the symbolic regression technique, and then, a controller is designed based on the dynamic model. Due to the ingenious experimental design, on one hand, the amount of training data is far less than the system identification method by machine learning. On the other hand, a decoupling feature is used in the model that greatly simplifies controller design. The experimental results on two-degree of freedom (DOF) and 6-DOF robotic manipulator simulators verify that the scheme is feasible and effective.

关键词： Manipulator dynamics Mathematical model data models Analytical models Robots Dynamics Torque data-driven modeling dynamic model identification feedback linearization robotic manipulator symbolic regression

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