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control of maneuvering fixed wing UAVs

Control of maneuvering fixed wing UAVs

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Zsófia Bodó Béla Lantos Department of Control Engineering and Information Technology Budapest University of Technology and Economics Hungary

ISBN: (数字)9781728173771

ISBN: (纸本)9781728173788

The paper deals with the attitude and altitude control of maneuvering fixed wing UAVs. A velocity damping component was separated in the aircraft dynamic model supporting the stability. Beside Euler angles, an alternative attitude control method was elaborated using quaternion based backstepping control (BSC) in the body frame. A reference signal design method was developed for UAVs that can smoothly connect straight line, circle arc and spiral motion sections for maneuvers. The fusion of BSC and dynamic inversion based attitude and altitude control was elaborated for which the controller parameters can be easily determined. The simulation system for testing the algorithms shows acceptable precision.

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Optimal HAP Deployment and Power control for Space-Air-Ground IoRT Networks

Optimal HAP Deployment and Power Control for Space-Air-Groun...

引用

ieee Conference on Wireless Communications and Networking

作者： Chaoyi Zhu Yitao Li Manqing Zhang Qi Wang Wuyang Zhou CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Hefei Anhui China Artificial Intelligence Key Laboratory of Sichuan Province School of Automation and Information Engineering Sichuan University of Science and Engineering Yibin China

In recent years, Internet of Things (IoT) has become one of the most important technologies in academia and industry. However, in many application scenarios, smart devices are distributed over a remote area without terrestrial communication systems. Considering the power of smart devices is limited, it is infeasible for terrestrial access networks to effectively receive the data of smart devices. Therefore, as a supplement to the terrestrial networks, Space-Air-Ground networks are critical to the Internet of Remote Things (IoRT). In this paper, we use high-altitude platforms (HAPs) to assist data transmission from smart devices to low earth orbit (LEO) satellites. Aiming at minimizing system power consumption, we propose an algorithm that jointly optimizes the resource allocation scheme and the HAP relay deployment. Since the problem is a mix-integer non-convex programming which is prohibitive to solve, our proposed algorithm divides it into two sub-problems to find a near-optimal solution with low computational complexity. Simulation results show that compared with average resource allocation scheme, our proposed algorithm can significantly reduce the system power consumption while satisfying the rate requirements of smart devices.

关键词： Power demand Satellites Simulation Low earth orbit satellites Taylor series Resource management Internet of Things

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Nonlinear Model Predictive control of Industrial Grinding Circuits using Machine Learning

Nonlinear Model Predictive Control of Industrial Grinding Ci...

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Ravi kiran Inapakurthi Srinivas Soumitri Miriyala Suryanarayana Kolluri Kishalay Mitra Indian Institute of Technology Hyderabad Hyderabad India University of Texas at Austin Austin United States of America

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

control problems of engineering interest such as industrial grinding circuit (IGC) are essential for minimizing the energy consumption, maximizing throughput or maintaining product quality to make these processes energy sustainable in future. Detailed physics based models, although provide more insight into the process and yield accurate results, often cannot be used for control purposes owing to the high computational time involved in solving the complicated mass, momentum, energy balance equations used to describe the process. In the current study, the aim is to use optimally designed Recurrent Neural Networks, a type of data-based modeling technique popularly used in the machine learning domain, for modeling transients involved in the IGC and test its effectiveness in set point (SP) tracking under the nonlinear model predictive control (NMPC) framework. SP tracking of throughput (related to the amount of raw materials processed to give products) and recirculation load (related to the energy consumption) is performed. We observe that the developed machine learning based models could effectively perform the SP tracking for nonlinear industrial process like grinding.

关键词： computational modeling Integrated circuit modeling Recurrent neural networks Optimization Training Predictive models Predictive control

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TransView: Inside, Outside, and Across the Cropping View Boundaries

TransView: Inside, Outside, and Across the Cropping View Bou...

引用

International Conference on Computer Vision (ICCV)

作者： Zhiyu Pan Zhiguo Cao Kewei Wang Hao Lu Weicai Zhong Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Huawei CBG

ISBN: (纸本)9781665428132

We show that relation modeling between visual elements matters in cropping view recommendation. Cropping view recommendation addresses the problem of image recomposition conditioned on the composition quality and the ranking of views (cropped sub-regions). This task is challenging because the visual difference is subtle when a visual element is reserved or removed. Existing methods represent visual elements by extracting region-based convolutional features inside and outside the cropping view boundaries, without probing a fundamental question: why some visual elements are of interest or of discard? In this work, we observe that the relation between different visual elements significantly affects their relative positions to the desired cropping view, and such relation can be characterized by the attraction inside/outside the cropping view boundaries and the repulsion across the boundaries. By instantiating a transformer-based solution that represents visual elements as visual words and that models the dependencies between visual words, we report not only state-of-the-art performance on public benchmarks, but also interesting visualizations that depict the attraction and repulsion between visual elements, which may shed light on what makes for effective cropping view recommendation.

关键词： Visualization Computer vision computational modeling Benchmark testing Feature extraction Transformers Decoding

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The evolution and challenges of modern Distributed control Systems

The evolution and challenges of modern Distributed Control S...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Dănuț Adrian Poștovei Constantin Bulac Ion Triștiu Balduino Camachi Faculty of Power Engineering University Politehnica of Bucharest Bucharest Romania Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania

ISBN: (数字)9781728173771

ISBN: (纸本)9781728173788

This paper presents the evolution and development trends of the distributed control systems (DCS) implemented in the Electrical Power Systems, highlighting the impact of the modern versus traditional DCS systems. The principles of the modern DCS systems presented in this paper can be found implemented in Romania in the Transmission and Distribution energy sector, implemented by the Romanian Transmission and System Operator CNTEE Transelectrica SA and, as well as the electricity distribution operators like Electrica, EoN and CEZ.

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A memory of motion for visual predictive control tasks

A memory of motion for visual predictive control tasks

引用

ieee International Conference on Robotics and automation (ICRA)

作者： Antonio Paolillo Teguh Santoso Lembono Sylvain Calinon Idiap Research Institute Martigny Switzerland USI-SUPSI Dalle Molle Institute for Artificial Intelligence (IDSIA) Lugano Switzerland

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

This paper addresses the problem of efficiently achieving visual predictive control tasks. To this end, a memory of motion, containing a set of trajectories built off-line, is used for leveraging precomputation and dealing with difficult visual tasks. Standard regression techniques, such as k-nearest neighbors and Gaussian process regression, are used to query the memory and provide on-line a warm-start and a way point to the control optimization process. The proposed technique allows the control scheme to achieve high performance and, at the same time, keep the computational time limited. Simulation and experimental results, carried out with a 7-axis manipulator, show the effectiveness of the approach.

关键词： Visualization Microsoft Windows Trajectory Optimization Task analysis computational modeling Cameras

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Faster R-CNN Based Indoor Flame Detection for Firefighting Robot

Faster R-CNN Based Indoor Flame Detection for Firefighting R...

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ieee symposium Series on computational intelligence (SSCI)

作者： Guo, Jia Hou, Zengguang Xie, Xiaoliang Yao, Shuncai Wang, Qiaoli Jin, Xuechen North Univ China Sch Elect & Control Engn Taiyuan 030051 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China

ISBN: (纸本)9781728124858

Firefighters are primarily tasked to handle fire incidents, but they are often exposed to high risks when extinguishing fire. Firefighting robots are actively being researched to reduce fire fighters injuries and deaths as well as increase their effectiveness on performing tasks. However, the major concern is how to make the flame detection methods to satisfy the high precision requirement of firefighting robot. Therefore, the flame detection of firefighting robot has become a hot topic in this area. In this paper, a Faster R-CNN model is proposed to detect flame in noisy images of fire ground. Firstly, the region generation network is used to extract the candidate flame regions. Secondly, the candidate flame regions are convoluted and pooled to extract the flame characteristics. Thirdly, the output features of Region Proposal Network (RPN) are fed into two fully connected layers: a box-regression layer which recognizes the locations of objects and a box-classification layer which classifies the objects. The dataset used in the experiment was obtained by video capture. The network is pre-trained based on Google platform Tensorflow, and the obtained precision and frame rate of the proposed method are up to 99.8% and 1.4 FPS, respectively. The experimental results demonstrate that the method equipped merits such as automatically extract the flame characteristics, effectively improve the precision of flame detection, and has excellent generalization ability and robustness.

关键词： firefighting robot faster R-CNN flame detection image processing deep learning

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Mobile Robot Self-LocalizationUsing Visual Odometry Based on Ceiling Vision

Mobile Robot Self-LocalizationUsing Visual Odometry Based on...

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Lin, Qing Liu, Xiaofeng Zhang, Zhihao Hohai Univ Coll IoT Engn Changzhou Key Lab Robot & Intelligent Changzhou 213022 Peoples R China Harbin Inst Technol Coll Astronaut Inst Intelligent Control & Syst Harbin 150001 Peoples R China

ISBN: (纸本)9781728124858

Self-localization is an important topic for mobile robot. Although some ceiling-based visual odometry have been proposed, the localization process is still cumbersome. In this paper, we propose a novel monocular visual odometry based on ceiling vision that is fast and easy to implement. By pointing the camera to the ceiling, visual odometry reduces the computation and eliminates interference from dynamic environments. We apply Oriented FAST and Rotated BRIEF (ORB) to match the features between two frames of the ceiling view and use sliding window filter to remove incorrect matches, and then get the orientation and position of the robot through coordinate transformation. Compared to traditional visual odometry, our algorithm performs better in speed, and only needs one correct match to get the displacement and rotation angle of robot.

关键词： visual odometry monocular ceiling vision self-localization ORB

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Multi-Agent Reinforcement Learning Based on Clustering in Two-Player Games

Multi-Agent Reinforcement Learning Based on Clustering in Tw...

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ieee symposium Series on computational intelligence (SSCI)

作者： Li, Weifan Zhuand, Yuanheng Zhao, Dongbin Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9781728124858

Non-stationary environment is general in real environment, including adversarial environment and multi-agent problem. Multi-agent environment is a typical non-stationary environment. Each agent of the shared environment must learn a efficient interaction for maximizing the expected reward. Independent reinforcement learning (InRL) is the simplest form in which each agent treats other agents as part of environment. In this paper, we present Max-Mean-Learning-Win-or-Learn-Fast (MML-WoLF), which is an independent on-policy learning algorithm based on reinforcement clustering. A variational auto-encoder method based on reinforcement learning is proposed to extract features for unsupervised clustering. Based on clustering results, MML-WoLF uses statistics and the dominated factor to calculate the values of the states that belong to a certain category. The agent policy is iteratively updated by the value. We apply our algorithm to multi-agent problems including matrix-game, grid world, and continuous world game. The clustering results are able to show the strategies distribution under the agent's current policy. The experiment results suggest that our method significantly improves average performance over other independent learning algorithms in multi-agent problems.

关键词： reinforcement learning unsupervised clustering matrix game multi-agent

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Computer-assisted Drug Delivery for General Anesthesia: Completing the Puzzle 17

Computer-assisted Drug Delivery for General Anesthesia: Comp...

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17th ieee World symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Ionescu, Clara M. Univ Ghent Dept Elect Energy Met Mech Construct & Syst Res Grp Dynam Syst & Control Campus ArdoyenTechnol Pk B-9052 Ghent Belgium

ISBN: (纸本)9781728102504

In this presentation, a complete overview of the current forms of computer-assisted drug delivery methodologies for general anesthesia will be given, along with the latest challenges. The choice of the instrumentation for gathering information from the patient in the closed loop used in the optimization scheme of the controller seems to be a crucial step in obtaining best possible results with respect to patient well-being and fast recovery. Our clinical studies indicate a possible improvement of current device for measuring pain non-invasively from skin impedance, what earlier though to be a limited approach. as perspectives, the completion of the puzzle with the hemodynamic system and nociceptor stimulation model is presented, along with an opensource benchmark simulator in Matlab (R) with mimicked patient population models.

关键词： Anesthesia Drugs Mathematical model computational modeling Drug delivery Brain modeling Pain

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