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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Barbin, Evgenij S. Nesterenko, Tamara G. Koleda, Aleksej N. Talovskaia, Alena A. Kulinich, Ivan V. Mokhovikov, Denis M. Laboratory of microsystems engineering Tomsk State University of Control Systems and Radioelectronics Laboratory of Radiophotonics V.E. Zuev Institute of Atmospheric Optics of SB RAS Tomsk Russia Laboratory of microsystems engineering Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Radiophotonics Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Radiophotonics Tomsk State University of Control Systems and Radioelectronics Laboratory of Intelligent Computer Systems V.E. Zuev Institute of Atmospheric Optics of SB RAS Tomsk Russia

ISBN: (纸本)9798350389234

The treatise studies a temperature effect on the microoptoelectromechanical (MOEMS) accelerometer with an optical measuring transducer based on evanescent wave coupling. The work investigates the temperature sensitivity of the mechanical part of the accelerometer in a temperature range from minus 65 °C to plus 125 °C. The temperature alteration induces stresses in the mechanical structure. Maximum mechanical stresses emerging in the structure at a temperature of minus 65 °C reach 556.61 MPa for the layer of aluminum that can be used for in-package wire bonding. However, these stresses are smaller than the admissible value for aluminum (690 MPa). The maximum stresses in residual layers of the mechanical structure are several times smaller than those in the aluminum layer. At different temperatures, the eigenfrequency of the accelerometer also changes, which alters its mechanical transmission coefficient. The temperature sensitivity of normalized mechanical transmission coefficient per 1 °C amounts to 0.00012 °C-1. To compensate the temperature effect on the mechanical transmission coefficient, various design schemes can be used and the output signal can be adjusted according to the temperature sensor readings. © 2024 IEEE.

关键词： Accelerometers

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A Visual Dynamic Odometry with Objects Tracking 41

A Visual Dynamic Odometry with Objects Tracking

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第41届中国控制会议

作者： Mingliang Gao Wei Gong Li Li The Department of Control Science and Engineering Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Tongji University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Visual SLAM approaches have made significant breakthroughs in recent *** has been widely used in mobile robots,autonomous driving,and AR/VR applications,which interact closely with the ***,the majority of SLAM algorithms are based on the static world assumption,which hinders the popularization of visual SLAM in a natural dynamic *** paper proposes a multi-object tracking visual odometry in a dynamic environment,segmented at the instance level,combined with the multi-object tracking function,and utilizes a new tracking *** new tracking strategy divides the image into the static and potential dynamic regions combined with instance *** the static region,it is tracked by minimizing the reprojection *** potential dynamic region is tracked by minimizing the photometric *** experimental results show that in the dynamic environment,the proposed algorithm can accurately estimate the camera pose and accurately estimate the motion of moving *** with the state-of-the-art algorithms,our proposed algorithm has much lower complexity while having similar accuracy performance.

关键词： Visual SLAM Dynamic Environment Object Tracking Motion Estimation

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Adaptive Iterative Learning control for Rigid-Flexible Coupled Manipulator System with Variable Tracking Trajectory

Adaptive Iterative Learning Control for Rigid-Flexible Coupl...

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2024 International Annual Conference on Complex systems and intelligent Science, CSIS-IAC 2024

作者： Yin, Liangjun Zhang, Jianxiang Zhang, Jiaoxia Institute of Intelligent Systems and Control Guangxi University of Science and Technology Liuzhou545000 China School of Applied Science and Civil Engineering Beijing Institute of Technology Zhuhai519088 China

ISBN: (纸本)9798331504755

In this paper, the adaptive iterative learning control problem of a rigid-flexible coupled manipulator system with variable tracking trajectories is investigated. The dynamics model of the rigid-flexible coupled manipulator is represented by a partial differential equation (PDE). An improved D-type adaptive iterative learning algorithm is proposed, and the Lyapunov function method is used to prove the convergence of the joint axis angle error and the flexible manipulator elasticity along the iterative axis, so as to achieve the complete tracking of the joint axis angle to the variable desired angle trajectory and the complete suppression of the flexible manipulator part of the elasticity of the flexible manipulator. Finally, the effectiveness of the proposed algorithm is validated by numerical simulations. © 2024 IEEE.

关键词： Lyapunov functions

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Bayesian Optimization with Clustering for Sparse Map Construction 42

Bayesian Optimization with Clustering for Sparse Map Constru...

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42nd Chinese control Conference, CCC 2023

作者： Li, Chengxi Gong, Wei Li, Li Tongji University The Department of Control Science and Engineering Shanghai China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China

ISBN: (纸本)9789887581543

High-quality sparse maps (e.g., radio maps, noise maps, and hydrographs) are essential in environmental monitoring, decision-making, path navigation, etc. However, constructing high-quality maps requires intensive data sampling, which is a repetitive and tedious task. To address this issue, we propose a new sparse map construction method based on Bayesian optimization in this paper. First, by exploiting the idea of spatial clustering, a novel acquisition method is proposed, which considers potential correlations between different points. This method further guides intelligent agents to collect samples at the most informative points. Second, by designing inference models, high-precision and stable maps can be gradually constructed as the number of sampling points increases. Next, a customized dataset, which contains Wi-Fi signal strength in a complex indoor environment, is collected to support the performance validation of our proposed method. Finally, we test the performance of our methods on this customized dataset. Experimental results show that our proposed methods outperform other existing methods including random search, grid search, and maximum predictive error sampling. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Decision making

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Performance Comparison of Lightweight CNN Models for Drone Collision Avoidance Dataset 20

Performance Comparison of Lightweight CNN Models for Drone C...

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20th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Zufar, Rifqi Nabila Banjerdpongchai, David Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering 254 Phayathai Road Bangkok Pathumwan10330 Thailand

ISBN: (纸本)9798350310467

Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain information, process information, and estimate whether the object has a risk of collision. Convolution Neural Network (CNN) is one of the methods that can be employed for collision prediction. However, CNN is a method that needs a large data in the training. Dario Pedro et al. provided a dataset called the CoLANet dataset that consists of VDOs of collision drones. Subsequently, they proposed a new algorithm called Neural Network Pipeline which has a Convolution Neural Network (CNN) part to extract the feature from a couple of images. CNN extracts images by using MobileNetV2 as a pre-trained model. They chose MobileNetV2 due to training performance from another dataset. This paper aims to assess the performance of lightweight CNN models using the CoLANet dataset. The models will be trained on the Keras library with parameters of fewer than ten million. The models will be validated by Confusion Matrix and Receiver Operating Characteristics. In conclusion, we examine which pre-trained CNN model has the best performance and suggest ongoing work. © 2023 IEEE.

关键词： Drones

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Unscented Kalman Filter for State of Charge Estimation of Lithium Titanate Battery 20

Unscented Kalman Filter for State of Charge Estimation of Li...

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20th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Dardchuntuk, Joshua Chun-Ken Banjerdpongchai, David Faculty of Engineering Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Pathumwan 254 Phayathai Road Bangkok10330 Thailand

ISBN: (纸本)9798350310467

An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO's electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems. © 2023 IEEE.

关键词： Battery management systems

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Surrogate submodel generation for an MEA-based post-combustion CO2 capture absorber 18

Surrogate submodel generation for an MEA-based post-combusti...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Wang, Yurun Huang, Yi Ye, Lingjian Huzhou University School of Engineering Huzhou China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou China

ISBN: (纸本)9798350312201

The absorber is the core operating unit of the MEA-based post-combustion CO2 capture process. Research on absorber modeling has been ongoing over the past few decades. However, the mechanistic model based on complex thermodynamics and reaction kinetics is computationally expensive and time-consuming. For example, the reaction balance typically needs iterative computations within the rigorous absorber simulator, for the computation of the true component concentrations distributed along the absorber. The objective of this paper is to develop a surrogate submodel for the reaction equilibrium, by establishing an explicit function to replace the equality relationships, the intensive iterations are avoided. The surrogate submodel is trained via the neural networks with data generated from the simulator using the Hammersley sequence. The results show that the surrogate submodel can substantially reduce the computation loads, while maintaining satisfactory accuracies compared with the rigorous simulator. © 2023 IEEE.

关键词： Carbon dioxide

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Distributed data-driven consensus control of multi-agent systems under switched uncertainties

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control Theory and Technology 2023年第3期21卷 478-487页

作者： Wenjie Liu Yifei Li Gang Wang Jian Sun Jie Chen National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China Department of Control Science and Engineering Tongji UniversityShanghai 201804China

Cooperative control of multi-agent systems(MASs),particularly consensus control,has gained significant attention in the last two decades,thanks to the rapid and sustained development of distributed and networked *** this paper,we present some new results focused on consensus control of a set of unknown linear MASs(whose system matrices are unknown)under unknown switched uncertainties,with an emphasis on distributed data-driven *** proposed controller is end-to-end,designed by solving two data-based semi-definite programs(SDPs),which adjust to the changes of the uncertainty *** approach achieves asymptotic consensus of the MAS provided that the switching is slow enough and the uncertainty is *** illustrate the effectiveness of our proposed method through a numerical example.

关键词： Data-driven control Multi-agent systems Consensus-Switched uncertainties

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Obstacle-circumventing adaptive control of a four-wheeled mobile robot subjected to motion uncertainties

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Frontiers of Mechanical engineering 2023年第3期18卷 123-143页

作者： Yiming YAN Shuting WANG Yuanlong XIE Hao WU Shiqi ZHENG Hu LI School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China

To achieve the collision-free trajectory tracking of the four-wheeled mobile robot(FMR),existing methods resolve the tracking control and obstacle avoidance *** the synergistic robustness and smooth navigation of mobile robots subjected to motion uncertainties in a dynamic environment using this non-cooperative processing method is *** address this challenge,this paper proposes an obstacle-circumventing adaptive control(OCAC)***,a novel anti-disturbance terminal slide mode control with adaptive gains is formulated,incorporating specified control laws for different *** formulation guarantees rapid convergence and simultaneous chattering *** introducing sub-target points,a new sub-target dynamic tracking regression obstacle avoidance strategy is presented to transfer the obstacle avoidance problem into a dynamic tracking one,thereby reducing the burden of local path searching while ensuring system stability during obstacle *** experiments demonstrate that the proposed OCAC method can strengthen the convergence and obstacle avoidance efficiency of the concerned FMR system.

关键词： four-wheeled mobile robot obstacle-circumventing adaptive control adaptive anti-disturbance terminal sliding mode control sub-target dynamic tracking regression obstacle avoidance

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Multi-Objective Optimization of AGV Real-Time Scheduling Based on Deep Reinforcement Learning 42

Multi-Objective Optimization of AGV Real-Time Scheduling Bas...

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42nd Chinese control Conference, CCC 2023

作者： Durst, Philip Jia, Xu Li, Li Tongji University Department of Control Science & Engineering Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China

ISBN: (纸本)9789887581543

Driven by the trend away from mass production towards more customization and individualism, manufacturing is under massive price pressure. Therefore, continuously improving production efficiency and reducing costs have always been the focus of manufacturing companies. With recent advances in Industry 4.0 and industrial Artificial Intelligence (AI), Automated Guided Vehicles (AGVs) have become a very promising technology to support this trend. Nowadays, they are widely used in job shop environments for material handling. However, this promising technology goes along with various new challenges, such as the high dynamics, complexity, and uncertainty of the job shop environment for AGV scheduling. To address these challenges, an adaptive Deep Reinforcement Learning (DRL) based AGV real-time scheduling approach is approached to optimize several efficiency parameters of the overall job shop system. Firstly, the DRL optimization problem is formulated as Markov Decision Process (MDP). The state and action representation, reward, and optimal policy function are described in detail. Then a novel DRL method is further developed to achieve the optimal mixed rule policy. For that, a novel RL algorithm, Proximal Policy Optimization (PPO), is applied to the Deep Neural Network (DNN) and implemented in TensorForce, a reinforcement learning package in Python based on TensorFlow. This algorithm is compared to conventional heuristic rules. Furthermore, SimPy, a relatively new discrete-event simulation package in Python, is used to implement the job shop environment. The job shop environment is based on a real-world scenario of a semiconductor factory, which is implemented in a simplified manner and applied to the DRL agent. The results are presented afterward, followed by a feasibility and effectiveness analysis of this approach. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Multiobjective optimization

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