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7th International Conference on Robotics, control and Automation engineering, RCAE 2024

作者： Mu, Junqi Zong, Xiaofeng Wei, Yiyang Hou, Xuping China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan430074 China

ISBN: (纸本)9798350355642

The actuator, serving as the fundamental power source of a soft robot, functions as its central component. The field of soft robotics has garnered increasing research attention since its inception. Pneumatic crawling robots, being an essential branch within the realm of soft robotics, possess notable characteristics such as rapid response time, cost-effectiveness, and human-computer interaction safety. Consequently, they hold significant research value. Among various types of crawling robots that have garnered considerable interest from researchers is the inchworm crawling robot. This paper aims to address the design and development aspects pertaining to inchworm actuators. By utilizing ABAQUS simulation software and taking displacement as the reference parameter, an analysis was conducted to investigate the impact of various geometric parameters on actuator displacement. Additionally, optimization of these geometric parameters was performed. The values of aspect ratio, length-to-width ratio, vertex angle and gap depth suitable for inchworm-like actuator are obtained. These findings are presented for the first time in the field of crawling soft robots and provide a theoretical basis for the development of pneumatic inchworm-like actuators. © 2024 IEEE.

关键词： Aspect ratio

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Multi-UAV Coverage Path Planning Method Based on Improved Particle Swarm Optimization

Multi-UAV Coverage Path Planning Method Based on Improved Pa...

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2024 China Automation Congress, CAC 2024

作者： Zhou, Yan Xiong, Yonghua School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350368604

The widespread application of unmanned aerial vehicle (UAV) has made the coverage path planning problem for multiple UAV systems a research hotspot. In this paper, we propose a multi-UAV solution to achieve complete coverage of multi-region. Based on this solution, we present an improved particle swarm optimization (PSO) algorithm that combines PSO algorithm, greedy strategy, and back-and-forth (BAF) algorithm. Its purpose is to plan the optimal paths for multi-UAV while achieving the shortest total distance and the highest UAV cooperation efficiency with the minimum number of UAVs, thereby enhancing the multi-UAV coverage cooperation and path optimization capabilities. Simulation results demonstrate the feasibility and superiority of this algorithm. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Adaptive Cooperative Estimation and Guidance Against Unknown Maneuvering Target

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Liu, Tianrun Chen, Yang-Yang Li, Shihua Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Advanced Ocean Institute of Southeast University Nantong226000 China

A critical challenge in missile guidance is the frequent inaccessibility of essential target information by on-board seekers. This paper studies the three-dimensional cooperative guidance issue of multiple missiles simultaneously attacking an unknown maneuvering target. Different from most existing research, the pitch and yaw lead angles of a maneuvering target are entirely unknown, while the position and velocity information is only accessible to neighboring missiles. An adaptive distributed estimator is designed to estimate the target information over a directed communication network, generating a transmitted target with similar maneuverability to the actual target for each missile. Boundedness of the estimation errors are guaranteed within a prescribed time. Subsequently, a modified guidance law that integrates proportional navigation (PN) and coordination components is developed for simultaneously tracking the trajectories of the transmitted targets. The superiority of the developed adaptive cooperative estimation and guidance approach is confirmed through simulations. © 1965-2011 IEEE.

关键词： adaptive estimate Cooperative guidance maneuvering target prescribed-time three-dimensional

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Robust Speed Regulation of PMSM under Disturbances and Noises using Composite Model Predictive control

Robust Speed Regulation of PMSM under Disturbances and Noise...

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第42届中国控制会议

作者： Linxuan Mao Jinya Su Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

This paper presents a single-loop Model Predictive control strategy that incorporates a reduced-order Generalized Proportional Integral Observe and a Kalman filter to enhance the speed regulation of Permanent Magnet Synchronous Motor systems in the presence of complex disturbances and measurement noises. The proposed controller design seamlessly integrates the predictive control, disturbance observer, and state filter components, and it was evaluated through simulation comparisons. The performance of the proposed method is evaluated using various metrics, including maximum velocity drop, recovery time, and variance of steady-state error, which demonstrate its superior response performance and anti-disturbance ability when compared to other existing methods without state filtering.

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Fall-LSTM: A Fall Detection Network Based on Spatio-Temporal Location Module

Fall-LSTM: A Fall Detection Network Based on Spatio-Temporal...

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Chinese control Conference (CCC)

作者： Dapeng Xu Xiaobo Lu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

Fall events have unique dynamic features, which are not fully utilized by existing fall detection methods. Based on video understanding, we propose Fall-LSTM to learn such features pertinently without additional inputs. Fall-LSTM is composed of CNN-LSTM framework and two excitation modules i.e., Spatial Attention Module (SAM) and Temporal Location Module (TLM). SAM provides spatial constraints on motion for feature layers through foreground extraction and spatial pooling. TLM emphasizes frames with high probability of fall events to LSTM by inferring the rate and trend of motion in clips. Experimental results show that our proposed modules significantly improve the performance of LSTM model, outperforming the state-of-the-art methods on two public Fall Detection Datasets i.e., Le2i and UR.

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PMSM System Identification by Knowledge-informed Machine Learning

PMSM System Identification by Knowledge-informed Machine Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Yue Yu Jiageng Tong Jinhui Xia Jinya Su Shihua Li School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Because of its excellent efficiency, compact dimen-sions, and accurate control features, Permanent Magnet Syn-chronous Motor (PMSM) are experiencing widespread applications across various industries. By accurately characterizing the dynamic behavior of PMSM systems through system identi-fication, engineers can ensure that PMSM motors reach their maximum potentials and meet the stringent requirements of modern industrial and technical systems while reducing energy consumption and maintenance costs. The traditional recursive least squares method is sensitive to noises, and unable to accu-rately identify parameters in complex environments. Pure data-driven models lack interpretability and require complex model architecture and computational costs. To this end, this work draws knowledge-informed neural ordinary differential equations (NODEs) for system identification, which embeds system prior knowledge into the NODEs for more efficient and accurate model learning. Comparative simulations show that this method not only obtains a higher-precision system model, but also significantly reduces the amount of training data and computation costs.

关键词： Accuracy Costs Uncertainty Computational modeling Noise Ordinary differential equations Motors Mathematical models Data models System identification

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Bézier-UKF Enhanced Incremental MPC Planner for Quadrotor Based Target Following

Bézier-UKF Enhanced Incremental MPC Planner for Quadrotor B...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Haodi Zhang Jinya Su Shihua Li School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

This paper presents a vision-based tracking control for a quadrotor to follow a moving target without assuming any quadrotor-target communication. Constant turn rate & acceleration model (CTRA) is first drawn to describe the target's motion, allowing to infer unmeasurable target motion variables while mitigating the adverse effects of vision sensor noises through an unscented Kalman filter (UKF). Then the target's future trajectory is firstly predicted by using a Bézier curve, which is further treated as virtual observation of the CTRA model within the prediction horizon so that a better target trajectory prediction is derived by using the proposed Bézier- UKF fusion predictor method. The outputs of Bézier- UKF predictor are input into an incremental model predictive control (IMPC) planner as the target reference, enabling the derivation of the desired position, velocity and acceleration signal for the quadrotor. These signals are implemented by the low-level SE(3) geometric controller. Finally, we conduct a comparative study of the proposed method against baseline planners such as MPC and IMPC planners in high-fidelity AirSim simulation environment, demonstrating better target following performance.

关键词： Target tracking Noise Kinematics Predictive models Vision sensors Inspection Trajectory

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Adaptive Multi-Dimensional Taylor Network Tracking control for Stochastic Nonlinear systems with Full-State Constraints and Input Saturation

Adaptive Multi-Dimensional Taylor Network Tracking Control f...

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International Conference on Robotics and Automation engineering (ICRAE)

作者： Xiao-Yi Zheng Hong-Sen Yan Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education and School of Automation Southeast University Nanjing China

In this study, an adaptive tracking controller using multi-dimensional Taylor network (MTN) is presented for state-constrained nonlinear stochastic systems with saturated input, in which MTN is implemented to model the unknown nonlinear functions. Firstly, the barrier Lyapunov function (BLF) and backstepping technique are combined under a unified framework to eliminate the impact of full-state constraints. Then, the effect raised by saturated input is solved by introducing an appropriate auxiliary system. Furthermore, by employing the Lyapunov stability theorem, the designed adaptive controller could ensure that all closed-loop signals are bounded in probability, the output signal can track the desired signal successfully, the tracking error is bounded by the expected bound, and the system state constraints are never violated. Finally, the efficiency of the suggested control methodology is confirmed by providing an example.

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An Adaptive Event-Triggered Secondary Regulation Strategy for Microgrids with Loss of Effectiveness Actuator Faults

An Adaptive Event-Triggered Secondary Regulation Strategy fo...

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Annual Conference of Industrial Electronics Society

作者： Xuechao Qiu Xiangyu Wang Dan Niu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

In this paper, an adaptive event-triggered secondary regulation strategy is investigated for microgrids with loss of effectiveness actuator faults. In order to deal with unknown loss of effectiveness actuator faults, a distributed secondary regulation strategy is proposed, which achieves voltage and frequency regulations, as well as power sharing. Meanwhile, to save system resources and relieve the communication burden, an adaptive event-triggered mechanism is designed. Finally, some simulation results are given to validate the proposed strategy, which indicates that the proposed strategy reduces the controller updates and increases the reliability of system.

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Enhancing Human-AI Trust by Describing AI Decision-Making Behavior

Enhancing Human-AI Trust by Describing AI Decision-Making Be...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Zhouwei Lou Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

Air combat game is a highly complex and dynamic decision-making problem that is crucial for ensuring national security and improving combat efficiency. In recent years, artificial intelligence (AI) technologies such as deep reinforcement learning have made significant progress in the air combat game field, surpassing human experts' capabilities. However, the decision-making process of AI algorithms often lacks transparency and interpretability, resulting in low trust in them, which limits their promotion and application in practical scenarios. To enhance human-AI trust, this paper proposes a decision explanation method based on natural language generation. As the most direct means of information transmission, natural language can help people quickly understand the behavior and intent of AI algorithms. Taking a one-on-one air combat game as an experimental scenario, this paper constructs a combat dataset mapping temporal states to behavioral explanations and designs an attention-based Encoder-decoder architecture (AED) capable of generating natural language descriptions of current AI decision-making behavior based on a period of combat data. Experimental results show that AED can accurately describe the decision-making behavior of AI algorithms and help improve the level of human-AI trust.

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