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检索条件"任意字段=10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021"

共 359 条记录，以下是221-230 订阅

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Unknown System Dynamics Estimator based control for Bilateral Teleoperation systems 10

Unknown System Dynamics Estimator based Control for Bilatera...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Jing, Baorui Duan, Hao Na, Jing Wang, Shubo Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650500 China School of Automation Qingdao University and Shandong Key Laboratory of Industrial Control Technology Qingdao266071 China

ISBN: (纸本)9781665424233

In this paper, we propose a novel control scheme for bilateral teleoperation system subject to the unknown system dynamics. To realize this purpose, the low-pass filter operation is introduced to construct a simple yet robustness unknown system dynamics estimator (USDE). With the help of the proposed USDE, not only the effect of unknown system dynamics can be eliminated but also the acceleration signal can be avoided. Moreover, one salient feature of the proposed USDE is that it only has one tuning parameter, which is more straightforward compared with traditional estimators. the convergence of the estimation error and the stability of the closed-loop control system are rigorously proved via the Lyapunov theorem. Finally, simulation is carried out to demonstrate the superior performance concerning the movement synchronization and transparency. © 2021 ieee.

关键词： Low pass filters

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Energy Balance Based Attack Detection for Cyber Physical systems 11

Energy Balance Based Attack Detection for Cyber Physical Sys...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Li, Zhuyuan Yang, Ying Zhao, Zhengen Liu, Ruijie College of Engineering Peking University Beijing100871 China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this article, the deficiency of a widely-used observer-based attack detection scheme for cyber physical systems (CPS) is analyzed and then a novel dissipativity-based attack detector is proposed. Firstly, the definition of kernel attacks is introduced to capture the stealthy attacks which can not be detected through well-established observer-based techniques. Further, a data-driven design of kernel attacks based on subspace identification methods deploying input and output data is presented. To detect kernel attacks, a dissipativity-based attack detector is formulated in light of energy balance equation, providing a complementary strategy to guarantee reliable detection of attacks. Finally, the validity of the presented approach is demonstrated in a numerical example. © 2022 ieee.

关键词： Cyber Physical System

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Event-based Integral Reinforcement learning Algorithm for Non-zero-sum Games of Partially Unknown Nonlinear systems 10

Event-based Integral Reinforcement Learning Algorithm for No...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Su, Hanguang Zhang, Huaguang Luo, Yanhong Sun, Qiuye School of Information Science and Engineering Northeastern University Liaoning Shenyang China

ISBN: (纸本)9781665424233

In this work, a novel event-based integral reinforcement learning (IRL) adaptive control method is developed to solve the multiplayer non-zero-sum (NZS) games of the nonlinear systems with unknown drift dynamics. By virtue of the IRL algorithm, the system drift dynamics is no more needed in the controller design. Moreover, different from the existing iteration computation methods, this method is online implemented, on which condition the event-triggered control framework can be combined with the IRL algorithm in solving the NZS game problems. In this method, a state-dependent triggering condition is proposed, thus the computation and communication loads are reduced in the control process. Moreover, the uniform ultimate boundedness (UUB) stability of the controlled system and the convergence of the critic weights have also been proved. Finally, a numerical example is provided to demonstrate the effectiveness of our method. © 2021 ieee.

关键词： Reinforcement learning

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Practical Cascade control for Supercavitating Vehicle 10

Practical Cascade Control for Supercavitating Vehicle

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Zhou, Yu Sun, Mingwei Zhang, Jianhong Liu, Lehua Chen, Zengqiang College of Artificial Intelligence Nankai University Tianjin300350 China Beijing Mecha-electrical Engineering Institute Beijing100074 China

ISBN: (纸本)9781665424233

Supercavitating vehicles, compared with other underwater vehicles, have amazing high speeds. However, the supercavitating vehicles have the unique tail-slap phenomenon in motion due to the lack of buoyancy and the existence of the planing force. In addition, there are large and unpredictable uncertainties in the hydrodynamics of the supercavitating vehicle because of the approximate understanding of this kind of mechanics. All these together impose big challenge to the controller design. In this paper, the longitudinal model of the supercavitating vehicles is adopted to carry out control investigation. A cascade control strategy is designed to coordinate depth control and avoid the planing force. In the outer-loop, the depth is controlled by the classical proportional-integral-derivative (PID) method;in the inner-loop, the control of longitudinal velocity and pitch rate use the linear active disturbance reject control (LADRC). the feasibility of the method is verified by mathematical simulation. the simulation results demonstrate the effectiveness and the superiority of the proposed strategy. © 2021 ieee.

关键词： controllers

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Design and Implementation of A Novel Quadruped Robot 10

Design and Implementation of A Novel Quadruped Robot

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Lin, Wenqi Peng, Bo Jin, Long School of Information Science and Engineering Lanzhou University Lanzhou China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China

ISBN: (纸本)9781665424233

Legged robots have unparalleled advantages over the traditional four-wheel and crawler ones. Particularly, they possess higher maneuverability in complex environments and can play a more significant role in military and emergency missions. To adapt the legged robot to the above situations, the primary task is to make the robot move freely like a human or animal, but this is a complicated and expensive project. the paper is dedicated to designing a novel and cheap quadruped robot and developing a complete and adequate control system. the overall design architecture is proposed, focusing on ease of manufacture and low cost of manufacture. Specifically, the control system runs on stm32, and the movement of the quadruped robot is controlled by a direct current motor that can be driven towards different directions by manipulating the terminal control device. Among the quadruped robots of similar performances, the quadruped robot BlackDog is cheaper and more stable in walking, and its structure is simple and easy to be implemented. © 2021 ieee.

关键词： control systems

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Short-Term Traffic Flow Prediction Based on Ensemble Machine learning Strategies 10

Short-Term Traffic Flow Prediction Based on Ensemble Machine...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Zeng, Ximu Wang, Yixiong Deng, Xin Wang, Jin Key Laboratory of Data Engineering and Visual Computing College of Computer Science and Technology Chongqing University of Posts and Telecommunication Chongqing400065 China

ISBN: (纸本)9781665424233

In order to solve the problem of traffic congestion, many city governments have begun to develop intelligent transportation systems. As a research hotspot in the field of intelligent transportation, the short-term traffic flow prediction is of great significance to traffic diversion and route planning. In the recent big data era, the machine learning (ML) algorithms have been applied to mining deep information in the data. However, the performance of a single ML model is usually not good. the ensemble learning can improve accuracy in most cases. In this paper, we propose a new short-term traffic flow prediction model. this paper regards the traffic prediction problem as a regression prediction problem, rather than a time series forecasting problem. the proposed model combines the prediction results of the XGBoost, LightGBM and CatBoost models through the ensemble machine learning strategy. With the experiments on real traffic data in Xi'an city, this paper has obtained the comparison of predictive performance of the models. Compared with single models such as the ARIMA, LSTM, XGBoost, LightGBM and CatBoost models, the results show the proposed model is more accurate and more suitable for short-time traffic status prediction. Key Words: traffic prediction, ensemble learning, XGBoost, LightGBM, CatBoost © 2021 ieee.

关键词： Traffic congestion

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Object Pose Estimation Based on Improved YOLOX Algorithm 11

Object Pose Estimation Based on Improved YOLOX Algorithm

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Zhou, Yanhong Liu, Shan Polytechnic Institute Zhejiang University Hangzhou310015 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this paper proposes a deep learning method for object recognition and pose estimation only through a single RGB image. We extend the YOLOX algorithm, which has excellent performance in the field of 2D object detection, making it suitable for 6DoF pose estimation scenarios in a natural way. the model is designed to predict the position of key points in the image, which are used to establish the correspondence between the object model and the scene image. After that, the object pose can be obtained by PnP algorithm. In order to obtain labeled data for model training effectively, this paper designs a data augmentation method combining offline expansion and online augmentation, which can improve the generalization ability of the model. the results of experiments on the LINEMOD dataset demonstrate the effectiveness of our method, which is significantly competitive with other methods using similar implementation ideas. © 2022 ieee.

关键词： Object recognition

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Intelligent Fault Diagnosis of Chemical Process Based on twin convolution Deep Neural Network of Wavelet Transform 10

Intelligent Fault Diagnosis of Chemical Process Based on twi...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Yu, Ting Qi, Yongsheng College of Electric Power Inner Mongolia University of Technology Hohhot010080 China Key Laboratory of Mechanical and Electrical Control of Inner Mongolia Autonomous Region Hohhot010051 China

ISBN: (纸本)9781665424233

For traditional pattern recognition fault diagnosis methods, a large number of labeled data is a necessary condition for training effective models, but it is almost difficult to collect enough labeled data in industrial application scenarios. therefore, an intelligent fault diagnosis algorithm based on wavelet transform and twin convolution depth neural network is proposed and applied to the fault identification of water chillers. Firstly, the fault data is transformed by wavelet transform to extract the change information of amplitude and frequency, and then it is transformed into dynamic time-frequency map as the depth network input;secondly, the pooling layer and convolution layer of convolution neural network are used to construct a multi input twin network to map the contrast image data to a low dimensional space, and the similarity comparison is carried out based on Euclidean distance, so as to realize the water chiller detection Fault type intelligent self-learning. through the simulation of chiller data, it is verified that this method not only has good robustness to the same type of fault with different severity levels, but also can realize the autonomous learning of unknown fault and enhance the intelligence of diagnosis process under incomplete data modeling. © 2021 ieee.

关键词： Wavelet transforms

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Decision Making for Overtaking of Unmanned Vehicle Based on Deep Q-learning 10

Decision Making for Overtaking of Unmanned Vehicle Based on ...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Chen, Wenbin Xie, Guo Ji, Wenjiang Fei, Rong Hei, Xinhong Li, Siyu Ma, Jialin Xi'an University of Technology Xi'an710048 China

ISBN: (纸本)9781665424233

Overtaking decision under dynamic environment is one of the key research directions. Traditional methods are difficult to solve such a complex optimization decision-making problem. In recent years, reinforcement learning algorithm is developing continuously, which can be applied to solve the overtaking decision problem of unmanned vehicles. Reinforcement learning generates new data through the continuous interaction between agent and environment, and feeds back to the agent for corresponding learning behavior, so as to improve its own behavior strategy. In this paper, Deep Q-learning (DQN) is used to solve the overtaking decision-making problem on one-way through two lanes. A one-way through two lane traffic environment is built in Python to train and test the algorithm. © 2021 ieee.

关键词： Decision making

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Consensus of Nonlinear Multiagent systems with Transmission Delays and Deception Attacks via Sampled-data control 10

Consensus of Nonlinear Multiagent Systems with Transmission ...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Han, Sheng Zhong, Qishui Shi, Kaibo Yang, Jin School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Institute of Electronic and Information Engineering of Uestc Dongguan Guangdong523808 China School of Information Science and Engineering Chengdu University Chengdu610106 China

ISBN: (纸本)9781665424233

the consensus issue for the nonlinear multiagent systems (MASs) with transmission delays subjected to deception attacks is addressed based on sampled-data control. Due to the universality of cyber attacks, this paper considers a kind of deception attacks which occur randomly at sampling instant and can modify the communication information. By employing the two-sided looped-functional method, an augmented Lyapunov-Krasovskii functional (LKF) is constructed to fully utilize the information on the sampling interval, which makes some free matrices be not necessarily positive definite. then, the sufficient criteria are presented to ensure the consensus of MASs with deception attacks. Furthermore, the controller gain matrix and the maximal sampling interval are obtained based on the decoupled approach. Finally, the numerical example is exploited to verify the effectiveness of our results. © 2021 ieee.

关键词： Multi agent systems

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