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Design of an aircraft autonomous traction taxiing system based on hydraulic secondary control

Chinese Journal of Aeronautics 2024年第6期37卷 348-359页

作者： Xiaochao LIU Zhongyi QIU Hao ZHANG Pengyuan QI Zhenyu WANG Yaoxing SHANG Zongxia JIAO Research Institute for Frontier Science Beihang UniversityBeijing 100083China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China Ningbo Institute of Technology Beihang UniversityNingbo 315800China Tianmushan Laboratory Xixi Octagon CityYuhang DistrictHangzhou 310023China Science and Technology on Aircraft Control Laboratory Beihang UniversityBeijing 100083China Beijing Tianma Intelligent Control Technology Co. Ltd.Beijing 101399China China Coal Research Institute Beijing 100013China

At present,aircraft taxiing at ground airports needs to be provided with a thrust by the main *** taxiing process is inefficient,has high fuel consumption and serious pollution,and is prone to safety *** this paper,a new configuration of aircraft autonomous traction taxiing system is proposed based on the principle of hydraulic secondary control,in which a hydraulic motor drive device is installed at the front wheels of the aircraft to drive the wheels to rotate forward or *** on this,autonomous taxiing can be realized without relying on the main engines,thus greatly improving airport operation ***,this paper analyzes the influencing factors of the autonomous traction taxiing process,and investigates the parameter matching design of the new configuration ***,this paper develops the ground principle prototype,designs the aircraft longitudinal bonding force observer and the aircraft wheel disturbance moment observer,and proposes the speed control method of the aircraft front wheel autonomous traction taxiing by considering the ground bonding force saturation ***,the ground taxiing test is conducted,and the results show that the new configuration proposed in this paper presents a new solution for aircraft autonomous traction taxiing.

关键词： Autonomous traction taxi-ing Hydraulic secondary control Hydraulic motors Saturation characteristics Disturbance observation Speed control

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Learning-based tracking control of AUV:Mixed policy improvement and game-based disturbance rejection

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CAAI Transactions on Intelligence technology 2025年第2期10卷 510-528页

作者： Jun Ye Hongbo Gao Manjiang Hu Yougang Bian Qingjia Cui Xiaohui Qin Rongjun Ding State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle EngineeringHunan UniversityChangshaChina School of Information Science and Technology and Institute of Advanced TechnologyUniversity of Science and Technology of ChinaHefeiChina Nanyang Technological University SingaporeSingapore Wuxi Intelligent Control Research Institute of Hunan University Hunan UniversityWuxiChina

A mixed adaptive dynamic programming(ADP)scheme based on zero-sum game theory is developed to address optimal control problems of autonomous underwater vehicle(AUV)systems subject to disturbances and safe *** combining prior dynamic knowledge and actual sampled data,the proposed approach effectively mitigates the defect caused by the inaccurate dynamic model and significantly improves the training speed of the ADP ***,the dataset is enriched with sufficient reference data collected based on a nominal model without considering modelling ***,the control object interacts with the real environment and continuously gathers adequate sampled data in the *** comprehensively leverage the advantages of model-based and model-free methods during training,an adaptive tuning factor is introduced based on the dataset that possesses model-referenced information and conforms to the distribution of the real-world environment,which balances the influence of model-based control law and data-driven policy gradient on the direction of policy *** a result,the proposed approach accelerates the learning speed compared to data-driven methods,concurrently also enhancing the tracking performance in comparison to model-based control ***,the optimal control problem under disturbances is formulated as a zero-sum game,and the actor-critic-disturbance framework is introduced to approximate the optimal control input,cost function,and disturbance policy,***,the convergence property of the proposed algorithm based on the value iteration method is ***,an example of AUV path following based on the improved line-of-sight guidance is presented to demonstrate the effectiveness of the proposed method.

关键词： adaptive dynamic programming autonomous underwater vehicle game theory optimal control reinforcement learning

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Multi-View Joint Target Selection for Relativistic-Perturbed Starlight Navigation

Multi-View Joint Target Selection for Relativistic-Perturbed...

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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Zhao, Yaru Ge, Dantong Xiong, Kai School of Aerospace Engineering Beijing Institute of Technology Beijing China Key Laboratory of Autonomous Navigation Control for Deep Space Exploration Beijing Institute of Technology Beijing China National Key Laboratory of Space Intelligent Control Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9798350384185

Autonomous navigation is one of the key technologies for spacecraft to realize long-term space operation and carry out science missions. In order to meet the requirement of high precision autonomous navigation, the effects of the gravitational deflection of light, stellar aberration and measurement bias of sensor are considered to establish a relativistic-perturbed starlight navigation scheme. To obtain high quality measurements, a multi-probe star sensor composed of multiple field of view probes is adopted for observation. A polygonal star pair selection method is proposed to conduct multi-view joint target selection. By constructing irregular polygons to select high quality star pair observations, the accuracy of navigation estimation is expected to be improved. The performance of relativistic-perturbed starlight navigation using the multi-view joint target selection method is then verified through a numerical simulation. © 2024 IEEE.

关键词： Probes

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Research on Adaptive Fault-Tolerant control for Manned Lunar Rover

Research on Adaptive Fault-Tolerant Control for Manned Lunar...

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第43届中国控制会议

作者： Zhouyuan Qian Tao Cao Tao Hu Hao Cheng Bo Zheng Shanghai Aerospace Control Technology Institute Shanghai Key Laboratory of Aerospace Intelligent Control Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Since the lunar terrain conditions are very hummocky,there is a risk of simultaneous failure of multiple drive motors and steering motors for manned lunar rover throughout the *** adaptive fault-tolerant control method based on direct yaw-moment control is proposed to ensure that the rover can still drive stably and accomplish the detection mission despite multi-motor *** method adopts a hierarchical control architecture,where the upper layer solves the vehicle's yaw moment demand based on the astronaut's driving intention and vehicle condition,and the lower layer carries out torque allocation by introducing failure factor to represent the motor failure ***,the objective function is designed by combining longitudinal drive control,yaw moment control and tire load rate control,and is adjusted by slip rate to restrain tire *** the joint simulation experiments,the effectiveness of the proposed method is *** results show that the driving stability and vehicle maneuverability are guaranteed even in the event of multi-motor failures.

关键词： Manned lunar rover fault-tolerant control direct yaw-moment control stability control

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Self-Adaptive Faulted Spacecraft Attitude Adjustment Event-Triggered control Via Reinforcement Learning Critic Mechanism

Self-Adaptive Faulted Spacecraft Attitude Adjustment Event-T...

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第43届中国控制会议

作者： Jing Huang Yizhen Meng Ming Cheng Xiaoxuan Yan Shanghai Aerospace Control Technology Institute Shanghai key laboratory of aerospace intelligent control technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Addressing the intricate challenge of achieving optimal ignition and attitude control during orbital deviation in a malfunctioning spacecraft with constrained inputs, this study integrates the Actor-Critic control framework derived from reinforcement learning into the interconnected constraints of spacecraft orbit decay and attitude control. Initially, we acquire an evaluative metric(Critic neural network) to assess the effectiveness of ignition and orbital deviation attitude control. Subsequently, this metric is amalgamated with spacecraft attitude control errors to formulate a self-adjusting, eventtriggered control strategy for spacecraft ignition and orbital deviation utilizing adaptive dynamic programming. Given that both performance metrics entail future states and lack direct computability, the robust approximation capabilities of neural networks are harnessed to address the evaluation network and the input-limited optimal performance metric of adaptive dynamic programming. This methodological approach aims to pinpoint the optimal attitude control for orbital deviation in malfunctioning spacecraft. Ultimately, the results from numerical simulations vividly illustrate the efficacy of the algorithm developed in this investigation.

关键词： Spacecraft deorbit self-driven reinforcement learning event-triggered adaptive dynamic programming adaptive neural network

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Simulation and Transfer of Physical Image Features Based on Recurrent Generative Adversarial Networks

Simulation and Transfer of Physical Image Features Based on ...

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2024 China Automation Congress, CAC 2024

作者： Dong, Yongxiang Qiu, Chaochao Pan, Qinghui Wu, Feiyue Jiang, Juncheng Lian, Jie Wang, Dong Wang, Wei Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9798350368604

Training machine learning models in virtual environments often fails to generalize effectively to real-world applications. The discrepancy between simulated and real environments results in suboptimal performance of the learned models. Various approaches have been proposed to address this"simulation-to-reality gap," including refining training images within simulations or randomizing virtual environments to narrow the disparity. A CycleGAN model is used to convert simulated images into real ones for correction, but this method remains task-agnostic and does not preserve task-specific information, which can negatively affect task performance. Critical features, particularly those directly related to the task, may be lost during translation, which can compromise task success. To address this issue, conditional information is introduced during the image transformation process to ensure that task-relevant features, such as location data, are retained. This method makes the transformation task-aware rather than task-agnostic. Additionally, skip connections are implemented to mitigate blurring in the generated images. The proposed method has been trained on a custom dataset from a laboratory environment and validated on a robotic arm. This approach significantly improves the accuracy of image conversion tasks and enhances the efficiency of sim-to-real image translation. © 2024 IEEE.

关键词： Generative adversarial networks

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Fuzzy-Model-Based Finite Frequency Fault Detection Filtering Design for Two-Dimensional Nonlinear Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2099-2110页

作者： Meng Wang Huaicheng Yan Jianbin Qiu Wenqiang Ji Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationSchool of Information Science and EngineeringEast China University of Science and TechnologyShanghai 200237China Research Institute of Intelligent Control and Systems School of AstronauticsHarbin Institute of TechnologyHarbin 150080China School of Artificial Intelligence Hebei University of TechnologyTianjin 300401China IEEE

This article studies the fault detection filtering design problem for Roesser type two-dimensional(2-D)nonlinear systems described by uncertain 2-D Takagi-Sugeno(T-S)fuzzy ***,fuzzy Lyapunov functions are constructed and the 2-D Fourier transform is exploited,based on which a finite frequency fault detection filtering design method is proposed such that a residual signal is generated with robustness to external disturbances and sensitivity to *** has been shown that the utilization of available frequency spectrum information of faults and disturbances makes the proposed filtering design method more general and less conservative compared with a conventional nonfrequency based filtering design ***,with the proposed evaluation function and its threshold,a novel mixed finite frequency H_(∞)/H_(-)fault detection algorithm is developed,based on which the fault can be immediately detected once the evaluation function exceeds the ***,it is verified with simulation studies that the proposed method is effective and less conservative than conventional non-frequency and/or common Lyapunov function based filtering design methods.

关键词： Fault diagnosis finite frequency specifications mixed H_(∞)/H_(-)performance two-dimensional nonlinear systems

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Adaptive Iterative Learning control for Nonlinear Multiagent Systems With Initial Error Compensation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4192-4201页

作者： Li, Zhiqiang Zhou, Qi Liu, Yang Ren, Hongru Li, Hongyi Guangdong University of Technology School of Automation and Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China

In this article, the consensus tracking problem of neural network (NN)-based adaptive iterative learning control (AILC) scheme is investigated for the nonlinear multiagent systems (MASs) with repetitive control tasks. To achieve the better tracking performance, the NN is developed to iteratively learn the uncertain nonlinearity. Meanwhile, the gradient descent algorithm is utilized to train NN weights, which is different from the traditional method based on \sigma-modification. For initial error problems of AILC, an initial error compensation method is developed to deal with the initial consensus tracking error. During the AILC design using the dynamic surface control technique, an auxiliary control signal is introduced to handle the residual term. Then, the boundedness of all signals and the iterative convergence of tracking errors are analyzed by the Lyapunov theory. Final, a simulation of the multimanipulators is provided to validate the effectiveness of the proposed control algorithm. © 2020 IEEE.

关键词： Multi agent systems

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Data-Driven control of Distributed Event-Triggered Network Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 351-364页

作者： Xin Wang Jian Sun Gang Wang Frank Allgower Jie Chen the National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the Institute for Systems Theory and Automatic Control University of StuttgartStuttgart 70550Germany the Department of Control Science and Engineering Tongji UniversityShanghai 201804 the National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(*** systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional *** the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is ***,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input ***,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.

关键词： Data-driven control distributed event-triggered network system(ETS) linear matrix inequalitie(LMI) looped-functional stability

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A Multi-Task Learning Framework for Load Identification and Signal De-Noising

A Multi-Task Learning Framework for Load Identification and ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhang, Chenming Jiang, Jiahao Li, Xiang Wang, Zhelong Qiu, Sen Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of the Ministry of Education School of Control Science and Engineering Dalian116024 China

ISBN: (纸本)9781665481090

Non-intrusive Load Monitoring (NILM) represents a cost-efficient technological advancement in meticulously monitoring electricity consumption patterns. However, external noise interference in practical load identification poses challenges for accurate monitoring. To address this, a multi-task learning framework is proposed, concurrently tackling load identification and signal de-noising. To extract effective information from load signals, a novel feature representation called variational mode decomposition-instantaneous amplitude (VMD-IA) was introduced. To ensure optimal performance across the different tasks, a multi-gate mixture of expert architecture (MMOE) module is integrated into the proposed model. The proposed approach was evaluated on a self-collected load dataset, as well as public NILM datasets. The experimental results indicate that the proposed model outperforms state-of-the-art methods in all tasks. © 2024 IEEE.

关键词： Signal denoising

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