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Simulation and application of magnesium alloy gap controller based on hybrid prediction model 3

Simulation and application of magnesium alloy gap controller...

引用

3rd World Robot Conference (WRC) / Symposium on Advanced Robotics and Automation (SARA)

作者： Wang, Haixia Zhang, Bing Cheng, Xiao Qiu, Jinyang Soochow Univ Appl Technol Coll Suzhou Jiangsu Peoples R China Jiangsu Univ Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9781665418232

The gap control system of magnesium alloy strip is characterized by nonlinear structure, time-varying parameters and large hysteresis. It is difficult to obtain satisfactory control effect by conventional control methods. This paper puts forward a kind of hybrid prediction optimization algorithm based on the grey prediction model GM(1, 1), combined with the advantages of the rolling optimization strategy. It overcomes the grey forecasting model dimension and effect of prediction step, gets more accurate dynamic rolling forecast data, makes up for the weakness of the thickness actuator and the lag of the detection mechanism. Furthermore, it uses Smith prediction to replace in the steady-state rolling, so as to avoid the inaccuracy of the prediction value obtained by Gray prediction Model entering after the steady state. This optimized algorithm not only makes up for the defects of the Gray prediction Model, but also improves the accuracy of the prediction data. It can effectively improve the dynamic thickness and accuracy control through the field rolling.

关键词： Strips Heuristic algorithms Predictive models prediction algorithms Data models Steady-state Magnesium

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Machine Learning Enabled Traffic prediction for Speed Optimization of Connected and Autonomous Electric Vehicles

Machine Learning Enabled Traffic Prediction for Speed Optimi...

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American Control Conference (ACC)

作者： Shao, Yunli Zheng, Yuan Sun, Zongxuan Oak Ridge Natl Lab Oak Ridge TN 37932 USA Univ Minnesota Twin Cities Dept Mech Engn Minneapolis MN 55455 USA Seagate Technol Shakopee MN 55379 USA Univ Minnesota Dept Mech Engn St Paul MN 55455 USA

ISBN: (纸本)9781665441971

Connected and autonomous vehicles (CAVs) can bring in energy, mobility, and safety benefits to transportation. The optimal control strategies of CAVs are usually determined for a look-ahead horizon using previewed traffic information. This requires the development of an effective future traffic prediction algorithm and its integration to the CAV control framework. However, it is challenging for short-term traffic prediction using information from connectivity, especially for mixed traffic scenarios. In this work, a novel machine learning enabled traffic prediction method is developed and integrated with a speed optimization algorithm for connected and autonomous electric vehicles. The traffic prediction is based on a hybrid macroscopic traffic flow model, in which the most challenging nonlinear terms are modeled with neural networks (NNs). The traffic prediction method can be readily applied to various mixed traffic scenarios. Information from connected vehicles is used as partial measurement of the traffic states and the rest unknown traffic states are estimated using a state observer. Then, the preceding vehicle's future trajectory is obtained to formulate the car-following distance constraint of the energy optimization problem. In a simulated scenario of 70% penetration rate of connectivity, the NN-based traffic prediction algorithm can reduce the root-mean-square errors of the prediction of preceding vehicle speed by near 50%, compared to the conventional traffic flow model. The energy benefit is 12.5%, which is satisfactory compared to 16.5% of the scenario with perfect prediction.

关键词： Machine learning algorithms Transportation Optimal control Machine learning Artificial neural networks Predictive models prediction algorithms

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Decision-making and Planning Framework with prediction-Guided Strategy Tree Search Algorithm for Uncontrolled Intersections 25

Decision-making and Planning Framework with Prediction-Guide...

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25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022

作者： Zhang, Ting Fu, Mengyin Song, Wenjie Yang, Yi Wang, Meiling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 China Nanjing University of Science and Technology Nanjing 210014 China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468817

Uncontrolled intersections are important and challenging traffic scenarios for autonomous vehicles. Vehicles not only need to avoid collisions with dynamic vehicles instantaneously but also predict their behavior then make long-term decisions in reaction. To solve this problem, we propose a cooperative framework composed of a Primary Driver (PD) for motion planning and a Subordinate Driver (SD) for decision-making. SD is essentially the combination of a prediction module and a high-level behavior planner, which develops a prediction-guided strategy tree to determine the optimal action sequence. Especially, under the guidance of the prediction results, the tree branches are evaluated in security metrics, then get trimmed in action and observation space to reduce the dimensional complexity. With the assistance of SD, PD works as a collision checker and a low-level motion planner to generate a safe and smooth trajectory. We use the INTERACTION dataset to validate our method and achieve more than 90% success rate with efficiency improvement in various situations. © 2022 IEEE.

关键词： Uncertainty Decision making prediction algorithms Planning Trajectory Complexity theory Behavioral sciences

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An anti-trackslip path tracking algorithm for steel box girder inspection robot based on model prediction control 3

An anti-trackslip path tracking algorithm for steel box gird...

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3rd World Robot Conference (WRC) / Symposium on Advanced Robotics and Automation (SARA)

作者： Yang, Zhenbo Xue, Lei Wang, Peng Wei, Shaobin Gao, He Wen, Yufang Tao, Yong Aero Engine Corp China Beijing Inst Aeronaut Mat Beijing 100095 Peoples R China Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Beihang Univ Res Inst Aeroengine Beijing 102206 Peoples R China

ISBN: (纸本)9781665418232

An anti-trackslip path tracking algorithm for steel box girder inspection robot based on model prediction (MPC) is proposed in this paper. This algorithm is oriented to the actual needs of the automatic inspection of steel box girder of super large bridges. It solves the slippage problem of the inspection robot because of the wear of driving wheels and slippery contact. The kinematic models of longitudinal and lateral slippage were established and corrected after analyzing the robot sliding control and slippage parameter estimation methods. The robot system error dynamic model and the error model based on state extension were proposed by overall considering control constraints. The robot's optimized objective function was constructed to convert MPC problem into quadratic programming. Finally, path tracking simulations were performed on the inspection robot under pure rolling and sliding conditions, respectively, using the improved MPC (IMPC) algorithm and the front-wheel feedback algorithm. The comparison showed that the IMPC algorithm exceeded the front-wheel feedback algorithm based on geometrical tracking in tracking precision, proving the effectiveness of the proposed IMPC in anti-trackslip path tracking.

关键词： Parameter estimation Wheels Inspection Predictive models prediction algorithms Linear programming Mobile robots

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Motion-prediction-based Wireless Scheduling for Multi-User Panoramic Video Streaming 40

Motion-Prediction-based Wireless Scheduling for Multi-User P...

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40th IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Chen, Jiangong Qin, Xudong Zhu, Guangyu Ji, Bo Li, Bin Univ Rhode Isl Dept ECBE Kingston RI 02881 USA Univ Rhode Isl Dept CSS Kingston RI 02881 USA Virginia Tech Dept CS Blacksburg VA USA

ISBN: (纸本)9780738112817

Multi-user panoramic video streaming demands 4 similar to 6x bandwidth of a regular video with the same resolution, which poses a significant challenge on the wireless scheduling design to achieve desired performance. On the other hand, recent studies reveal that one can effectively predict the user's Field-of-View (FoV) and thus simply deliver the corresponding portion instead of the entire scenes. Motivated by this important fact, we aim to employ autoregressive process for motion prediction and analytically characterize the user's successful viewing probability as a function of the delivered portion. Then, we consider the problem of wireless scheduling design with the goal of maximizing application-level throughput (i.e., average rate for successfully viewing the desired content) and service regularity performance (i.e., how often each user gets successful views) subject to the minimum required service rate and wireless interference constraints. As such, we incorporate users' successful viewing probabilities into our scheduling design and develop a scheduling algorithm that not only asymptotically achieves the optimal application-level throughput but also provides service regularity guarantees. Finally, we perform simulations to demonstrate the efficiency of our proposed algorithm using a real dataset of users' head motion.

关键词： Wireless communication Scheduling algorithms Conferences Streaming media Throughput prediction algorithms Scheduling

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Online Solar Energy prediction for Energy-Harvesting Internet of Things Devices 21

Online Solar Energy Prediction for Energy-Harvesting Interne...

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IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)

作者： Yamin, Nuzhat Bhat, Ganapati Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99163 USA

ISBN: (纸本)9781665439220

Low-power internet of things devices have the potential to transform multiple fields including healthcare, environmental monitoring, and digital agriculture. However, the operating life of these devices is severely constrained by their small batteries that require frequent recharging. Harvesting energy from ambient sources has emerged as an effective approach to prolong the lifetime of these devices. The harvested energy must be carefully managed to ensure that sufficient energy is available when ambient energy is scarce. prediction of the energy available in the future can aid energy management algorithms in making better decisions about the allocation of the available energy. This paper proposes a novel hierarchical machine learning model that considers recent history and daily variations to make accurate predictions of future energy availability. We also propose using online learning to adapt the model to seasonal and spatial variations in the harvested energy. Using real-world solar energy data from the National Renewable Energy Laboratory we show that the proposed approach has mean absolute errors of 1.5 J and 1.1 J in predictions under same and different locations, respectively. We also demonstrate that using the proposed approach in an energy management algorithm leads to 54% higher utility and 95% lower battery violations than the baseline.

关键词： Adaptation models Renewable energy sources Solar energy Transforms Predictive models prediction algorithms Batteries

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DyGLIP: A Dynamic Graph Model with Link prediction for Accurate Multi-Camera Multiple Object Tracking

<i>DyGLIP</i>: A Dynamic Graph Model with Link Prediction fo...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Quach, Kha Gia Pha Nguyen Le, Huu Truong, Thanh-Dat Duong, Chi Nhan Minh-Triet Tran Luu, Khoa Concordia Univ Montreal PQ Canada VinAI Res Hanoi Vietnam Chalmers Univ Technol Gothenburg Sweden Univ Arkansas Fayetteville AR 72701 USA VNU HCM Univ Sci Ho Chi Minh City Vietnam

ISBN: (纸本)9781665445092

Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number of existing works, solving the data association problem in any MC-MOT pipeline is arguably one of the most challenging tasks. Developing a robust MC-MOT system, however, is still highly challenging due to many practical issues such as inconsistent lighting conditions, varying object movement patterns, or the trajectory occlusions of the objects between the cameras. To address these problems, this work, therefore, proposes a new Dynamic Graph Model with Link prediction (DyGLIP) approach 1 to solve the data association task. Compared to existing methods, our new model offers several advantages, including better feature representations and the ability to recover from lost tracks during camera transitions. Moreover, our model works gracefully regardless of the overlapping ratios between the cameras. Experimental results show that we outperform existing MC-MOT algorithms by a large margin on several practical datasets. Notably, our model works favorably on online settings but can be extended to an incremental approach for large-scale datasets.

关键词： Computer vision Pipelines Predictive models Cameras prediction algorithms Robustness Trajectory

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prediction of Acoustic Communication Performance in Marine Robots Using Model-Based Kriging

Prediction of Acoustic Communication Performance in Marine R...

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American Control Conference (ACC)

作者： Kontoudis, George P. Stilwell, Daniel J. Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA

ISBN: (纸本)9781665441971

In this paper, we present a data-driven iterative algorithm for accurate prediction of underwater acoustic communication performance at unvisited sites. The prediction algorithm consists of two steps: i) estimation of the covariance matrix;and ii) prediction of the communication performance. The importance of the covariance estimation is highlighted with a multi-stage, model-based iterative methodology that produces unbiased and robust results. The efficiency of the framework has been validated with synthetic data.

关键词： Uncertainty Estimation Predictive models prediction algorithms Control systems Iterative methods Multi-robot systems

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prediction and Optimal Feedback Steering of Probability Density Functions for Safe Automated Driving

Prediction and Optimal Feedback Steering of Probability Dens...

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American Control Conference (ACC)

作者： Haddad, Shadi Caluya, Kenneth F. Halder, Abhishek Singh, Baljeet Univ Calif Santa Cruz Dept Appl Math Santa Cruz CA 95064 USA Ford Greenfield Labs Palo Alto CA 94304 USA

ISBN: (纸本)9781665441971

We propose a stochastic prediction-control framework to promote safety in automated driving by directly controlling the joint state probability density functions (PDFs) subject to the vehicle dynamics via trajectory-level state feedback. To illustrate the main ideas, we focus on a multi-lane highway driving scenario although the proposed framework can be adapted to other contexts. The computational pipeline consists of a PDF prediction layer, followed by a PDF control layer. The prediction layer performs moving horizon nonparametric forecasts for the ego and the non-ego vehicles' stochastic states, and thereby derives safe target PDF for the ego. The latter is based on the forecasted collision probabilities, and promotes the probabilistic safety for the ego. The PDF control layer designs a feedback that optimally steers the joint state PDF subject to the controlled ego dynamics while satisfying the endpoint PDF constraints. Our computation for the PDF prediction layer leverages the structure of the controlled Liouville PDE to evolve the joint PDF values, as opposed to empirically approximating the PDFs. Our computation for the PDF control layer leverages the differential flatness structure in vehicle dynamics. We harness recent theoretical and algorithmic advances in optimal mass transport, and the Schrodinger bridge. The numerical simulations illustrate the efficacy of the proposed framework.

关键词： Road transportation State feedback Handheld computers Pipelines Probability density function Probabilistic logic prediction algorithms

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Generation of Emergency Trajectories Based on Aircraft Trajectory prediction 40

Generation of Emergency Trajectories Based on Aircraft Traje...

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IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

作者： Saez, Raul Khaledian, Homeyra Prats, Xavier Tech Univ Catalonia UPC Dept Phys Aerosp Div Castelldefels 08860 Barcelona Spain

ISBN: (纸本)9781665434201

We present an automated emergency trajectory generator to compute the best emergency trajectory for a given landing site. A given emergency 2-dimensional (2D) path (i.e., route) is used as input for a trajectory predictor (TP), which computes a trajectory by taking into account the current aircraft performance and weather. The set of vertical profiles considered in the TP has been designed in order to cover the widest possible range of emergency situations. Moreover, the aircraft intents considered in these profiles are chosen by taking into account the operational requirements of the air traffic operation system and the input of the flight crew. A set of representative profiles has been tested during the study, to verify the result of the proposed algorithm and its computing time. Furthermore, the effect of the distance to go on the resulting trajectories has been also analyzed. This concept is expected to be part of an advanced flight management system on-board function to help the pilot take efficient and effective decisions in emergency situations and adverse conditions.(1)

关键词： Frequency modulation Tools Aerospace electronics prediction algorithms Generators Trajectory Air traffic control

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