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Research on longitudinal control technology in the transition section of tilt-rotor aircraft based on linear active disturbance rejection control 2

Research on longitudinal control technology in the transitio...

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2023 2nd International Conference on Aerospace and control Engineering, ICoACE 2023

作者： Xu, Shaofeng Chang, Shaoping Shi, Pengfei Wang, Lin AVIC Xi'An Flight Automatic Control Research Institute Xi'an710065 China National Key Laboratory of Science and Technology on Aircraft Control Xi'an710065 China

To solve the problem of strong coupling and control redundancy in the transition section of the tilt-rotor, this paper presents a longitudinal control method based on linear active disturbance rejection control (LADRC), verified by simulation. Firstly, the nonlinear dynamics model of tilt-rotor aircraft based on the mechanism method is established, and the model's accuracy is verified by comparing the trim result with the generic tilt-rotor aircraft simulation (GTRS) model verified by flight test in XV-15. Then, the control and redundant rudder surface assignment strategies for tilt-rotor aircraft are proposed. Then, the vertical and vertical control law of tilt-rotor aircraft is designed, the linear expansion state observer (LESO) model is analyzed and derived, and the linear active disturbance rejection controller suitable for attitude loop control of tilt-rotor is designed. The altitude and velocity loops are controlled. Finally, the proposed linear active disturbance rejection control (LADRC) controller is verified by simulation, and the whole process of tilting transition is simulated. The results show that the proposed control strategy and the controller designed in this paper have strong anti-disturbance ability, fast response speed, and high control precision and can be used in the transition section of tilt-rotor aircraft. The vertical direction shall be well controlled. © Published under licence by IOP Publishing Ltd.

关键词： controllers

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A proposal on centralised and distributed optimisation via proportional–integral–derivative controllers(PID)control perspective

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IET Cyber-systems and Robotics 2023年第4期5卷 40-48页

作者： Jiaxu Liu Song Chen Shengze Cai Chao Xu School of Mathematical Sciences Zhejiang UniversityHangzhouChina The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhouChina

Motivated by the excellent performance of proportional–integral–derivative controllers(PIDs)in the field of control,the authors injected the philosophy of PID into optimi-sation and introduced two types of novel PID optimisers from a continuous-time view,which benefit from the idea that discrete-time optimisation algorithm can be modelled as a continuous dynamical system/controlled *** centralised optimisation,the au-thors discuss the idea of the first-order PID optimiser and the second-order accelerated PID ***,this framework is extended into distributed optimisation settings,and a distributed PID optimiser is ***,some numerical examples are given to verify our ideas.

关键词： control deep learning deep neural network machine learning

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Stochastic Data-Driven Predictive control: Regularization, Estimation, and Constraint Tightening

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IFAC-PapersOnLine 2024年第15期58卷 79-84页

作者： Mingzhou Yin Andrea Iannelli Roy S. Smith Automatic Control Laboratory ETH Zürich Switzerland Institute for Systems Theory and Automatic Control University of Stuttgart Germany

Data-driven predictive control methods based on the Willems’ fundamental lemma have shown great success in recent years. These approaches use receding horizon predictive control with nonparametric data-driven predictors instead of model-based predictors. This study addresses three problems of applying such algorithms under unbounded stochastic uncertainties: 1) tuning-free regularizer design, 2) initial condition estimation, and 3) reliable constraint satisfaction, by using stochastic prediction error quantification. The regularizer is designed by leveraging the expected output cost. An initial condition estimator is proposed by filtering the measurements with the one-step-ahead stochastic data-driven prediction. A novel constraint-tightening method, using second-order cone constraints, is presented to ensure high-probability chance constraint satisfaction. Numerical results demonstrate that the proposed methods lead to satisfactory control performance in terms of both control cost and constraint satisfaction, with significantly improved initial condition estimation.

关键词： data-driven control model predictive control estimation filtering stochastic optimal control stochastic system identification

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Tracking and Mapping Strategy for Indoor UAV Based on Entropy Theory:An ORB-SLAM3 Extension 41

Tracking and Mapping Strategy for Indoor UAV Based on Entrop...

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第41届中国控制会议

作者： Jian Xu Haoyu Qi Min Xu Yunge Zang Zhen Li Xi Zhang Xiangdong Liu School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing Aerospace Automatic Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligence Control

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

For indoor application,which is an entirely GPS-denied environment,visual simultaneous localization and mapping(SLAM) facilitates the real autonomy of unmanned aerial vehicle(UAV) but raises the challenging requirement on accurate localization with low computational *** address this difficulty,a stereo-camera based SLAM system is proposed by applying Entropy theory to *** an extension to ORB-SLAM3,the additional entropy decision module and map processor are specifically *** decision module can improve computing efficiency by deciding whether keyframes or extra optimization should be ***,the map processor is targeted at loading and maintaining the prior map whenever *** results in indoor laboratory environment show that the developed system can achieve the superior localization accuracy in more efficient computation manner with smaller size of mapping compared with ***,the map can be effectively expanded and corrected even when prior information is invalid,greatly increasing the robustness of SLAM system.

关键词： UAV stereo-camera ORB-SLAM3 entropy theory prior map

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Filtered iterative learning control of the time-delay flexible manipulator system with slowly varying tracking trajectory

Filtered iterative learning control of the time-delay flexib...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Jianxiang Yin, Liangjun Institute of Intelligent Systems and Control Guangxi University of Science and Technology Liuzhou China

ISBN: (纸本)9798350368604

In this paper, the filtered iterative learning control (FT-ILC) problem of a time-delay flexible manipulator system with variable tracking trajectory is studied. In practice, the system may be affected by random noise, which can reduce the control accuracy. In order to reduce the influence of noise on the system, this paper designs a filtered iterative learning law. At the same time, considering that the desired trajectory of the system is not fixed, but slowly changing, the learning law is extended to this situation. Through the compression mapping principle, the convergence of the time-delay flexible manipulator system is demonstrated. Finally, the numerical simulation results verify the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Delay control systems

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Study on Sensor Data Processing method based on Generative Neural Network 6

Study on Sensor Data Processing method based on Generative N...

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6th International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Li, Wenlong Ma, Hongchao Shi, Huichao North Automatic Control Technology Institute Taiyuan China School of Information Science and Technology Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9781837242672

The method of using experimental data as a training dataset to train deep neural networks for sensor measurement error compensation has aroused widespread research interest among researchers. Due to the difficulty and high cost of experiments, the sensor measurement data obtained through experiments is limited. Therefore, it is difficult to achieve the compensating method for sensor measurement errors by training deep neural networks with sufficient experimental data. In the case of limited sensor experimental data, a generative deep neural network is used in this paper to simulate the obtained limited amount of experimental data and generate a sufficient amount of experimental data. A mixed dataset is constructed using real experimental data and generated experimental data to complete the training of the error compensation network and achieve the correction of sensor measurement results. By comparing the sensor results corrected by measurement error correction model trained by original experimental data and the mixed data, it can be proven that the mixed data trained measurement error correction model has a better compensation effect on measurement errors. © The Institution of Engineering & technology 2024.

关键词： Data assimilation

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Dynamic Data Collection for UAV-Assisted Green Industrial IoT

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IEEE Internet of Things Journal 2025年第13期12卷 22786-22799页

作者： Lu, Jiarong Wang, Ying Zhao, Junwei Wu, Wen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China North Automatic Control Technology Institute China Pengcheng Laboratory Frontier Research Center Shenzhen518055 China

Unmanned aerial vehicles (UAVs) can collect data from industrial Internet of Things (IoT) devices that experience poor channel conditions caused by the obstruction of large industrial equipment. However, due to the mobility of UAVs and stochastic industrial data generation, extreme events with significantly high latency may occur during data collection, resulting in unreliable communication. Besides, UAV speed variation brings challenges to achieving green communication and reliable data collection. In this paper, we propose a dynamic UAV-assisted resource allocation scheme to collect data reliably for green industrial IoT. Specifically, the queue tail distribution is adopted to characterize the occurrence probability of extreme events, which indicates the reliability of the queue length. Then, given the impact of UAV speed on energy consumption and queue reliability, we aim to minimize energy consumption constrained by tail distribution and optimize UAV speed to ensure reliable data collection. Furthermore, the device access, bandwidth allocation, power control, and UAV speed are jointly optimized for minimizing the long-term energy consumption of UAVs and industrial IoT devices, constrained by the tail distribution of the queue length. The formulated problem is intractable due to intricately coupled variables and stochastic characteristics. To resolve it, we propose a novel algorithm, namely JDBPS, which can achieve reliable data collection and green communication. Simulation results demonstrate that the proposed JDBPS algorithm can constrain tail distribution while reducing transmit power of industrial IoT devices by 15.7% compared with the fixed UAV speed scheme. © 2014 IEEE.

关键词： Energy utilization

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RESEARCH ON FLOW RIPPLE REDUCTION OF PISTON PUMP WITH PRE-COMPRESSION VOLUME

RESEARCH ON FLOW RIPPLE REDUCTION OF PISTON PUMP WITH PRE-CO...

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2024 CSAA/IET International Conference on Aircraft Utility systems, AUS 2024

作者： Huang, Jian Lou, Fangli Nie, Songlin Wang, Jun Huo, Xijian Beijing Institute of Automatic Control Equipment Beijing100074 China Beijing University of Technology Beijing100124 China

The piston pump is a core power component of aviation hydraulic servo control systems, and its performance directly affects the overall efficacy of the hydraulic system. A significant challenge affecting the stability and control of hydraulic servo systems is the flow pulsation generated by the axial piston pump. This flow pulsation primarily arises from pressure fluctuations during the piston flow distribution process. To address this issue, a pre-compression chamber structure has been designed to achieve pre-boost and pre-depressurization of the piston, thereby enhancing the performance of the piston pump. Computational fluid dynamics (CFD) methods are employed to analyze the flow characteristics of the piston pump. Simulation results indicate that the pre-compression volume effectively pre-pressurizes the piston chamber, reducing the pressure differential between the piston chamber and the high-pressure outlet. This modification results in a reduction of approximately 12.3% in outlet flow pulsation for the bidirectional piston pump. This study provides a foundational approach for achieving stable flow output in piston pumps. © The Institution of Engineering & technology 2024.

关键词： Reciprocating pumps

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Predictive Linear Online Tracking for Unknown Targets 41

Predictive Linear Online Tracking for Unknown Targets

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41st International Conference on Machine Learning, ICML 2024

作者： Tsiamis, Anastasios Karapetyan, Aren Li, Yueshan Balta, Efe C. Lygeros, John Automatic Control Laboratory ETH Zürich Zürich Switzerland Institute for Dynamic Systems and Control ETH Zürich Zürich Switzerland Control and Automation Group Inspire AG Zürich Switzerland

In this paper, we study the problem of online tracking in linear control systems, where the objective is to follow a moving target. Unlike classical tracking control, the target is unknown, non-stationary, and its state is revealed sequentially, thus, fitting the framework of online non-stochastic control. We consider the case of quadratic costs and propose a new algorithm, called predictive linear online tracking (PLOT). The algorithm uses recursive least squares with exponential forgetting to learn a time-varying dynamic model of the target. The learned model is used in the optimal policy under the framework of receding horizon control. We show the dynamic regret of PLOT scales with O(√TVT), where VT is the total variation of the target dynamics and T is the time horizon. Unlike prior work, our theoretical results hold for non-stationary targets. We implement PLOT on a real quadrotor and provide open-source software, thus, showcasing one of the first successful applications of online control methods on real hardware. Copyright 2024 by the author(s)

关键词： Target tracking

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Real-time drone detection framework based on advanced texture feature extraction and pattern recognition model using GUI

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Neural Computing and Applications 2025年第5期37卷 3435-3454页

作者： Hussen, Noha Salem, Mofreh Eldesouky, Ali I. Sakr, Noha Elghamrawy, Sally Computers and Control Systems Engineering Department Mansoura University Mansoura Egypt Computer Engineering Department Misr Higher Institute for Engineering and Technology Mansoura Egypt

The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the development of robust and efficient drone detection systems has become paramount. Traditional detection methods such as radar and acoustic sensors have limitations in detecting small drones and can be costly to implement. This can lead to lower detection rates and increased security breaches and safety concerns. However, prevailing methodologies often falter in achieving real-time detection with requisite precision and efficacy, particularly within complex operational environments. Motivated by the imperative to surmount these challenges, our research endeavors to introduce a pioneering real-time drone detection framework. Our study encapsulates several seminal advancements. Firstly, we proffer a groundbreaking framework that synergizes advanced integration technique based on texture feature extraction and pattern recognition techniques for real-time drone detection to increase accuracy to detect drones in different conditions such as bad weather and low resolution. Secondly, we introduce an intuitive graphical user interface, enhancing the usability and accessibility of the system in real-time scenario. Lastly, through exhaustive evaluation and comparative analysis, we substantiate the superior performance of our framework in terms of accuracy, precision, and real-time detection capabilities compared to conventional DDS methodologies. Its stability and effectiveness render it a compelling solution for security-focused entities, notably those within air force and military systems. Our experimental results reveal a commendable accuracy rate of 97%, affirming the reliability and precision 98% and recall parameter 98% of our framework in accurately identifying and detecting drones, thus surpassing recent models in the field. © The Author(s), under exclusive

关键词： Graphical user interfaces

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