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Research on Motion Path Optimisation Based on Geometric Model and Optimisation Algorithm

Research on Motion Path Optimisation Based on Geometric Mode...

引用

Electronics, Energy systems and Power Engineering (EESPE), IEEE international conference on

作者： Junqi Li Yaoqin Wu Zihui Liu Jiangxi Agriculture University Nanchang China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

In this paper, a series of solutions based on geometric models and optimisation algorithms are proposed for the problem of motion optimisation of complex motion systems in space. Firstly, a mathematical model based on the helix equation is constructed, the position of each motion node is calculated using the polar coordinate system and recurrence relation, and the velocity of the node is calculated by the differential method [1]–[2]. In order to determine the time for the system to move along the helix to the termination point, a collision detection model is introduced in this paper, which combines the space division technique with an iterative algorithm to accurately locate the collision moment [3]–[4]. Subsequently, a pitch optimisation model is established with collision avoidance and boundary conditions as constraints, and the optimal pitch is solved by an iterative method [5]. In addition, a geometric relationship-based optimisation model for the turnaround curve was designed, and the curve length was optimised using the Powell optimisation algorithm [6]. Finally, this paper constructs a speed relation model under the maximum speed constraint to optimise the maximum travel speed of the system using the dichotomy method [7]–[8]. The accuracy of the model is verified by numerical differentiation, and the proposed method effectively solves the path optimisation and speed control problems of the motion system.

关键词： Accuracy Optimization models Heuristic algorithms Geometric modeling Velocity control Numerical models Trajectory Iterative methods Collision avoidance Optimization

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Evaluation of the Quality of Fuzzy Clustering of complex-Valued Data

Evaluation of the Quality of Fuzzy Clustering of Complex-Val...

引用

international conference on control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Irina Alexandrovna Sedykh Kirill Nikolaevich Makarov Department of Automated Control Systems Lipetsk State Technical University Lipetsk Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The article discusses fuzzy clustering of complexvalued data and methods for evaluating the quality of fuzzy clustering to determine the optimal number of clusters: Xie-Beni index, fuzzy simplified silhouette index, Kwon index, Tang-Sun-Sun index. Based on the evaluation, the optimal number of clusters was determined for the C-Shell and Gustafson-Kessel methods. The results of the study are presented in the form of tables and graphs. The conclusion is made about the work done/

关键词： Analytical models Automation Clustering algorithms control systems Mathematical models Energy efficiency Data models Indexes

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Transient Stability Analysis of Power System Considering Multiple Inverter Aggregation Model

Transient Stability Analysis of Power System Considering Mul...

引用

Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Zhennan Yang Haoyi Liu Zhanpeng Xu Qin Xu Electric Power Dispatching and Control Center of Guangdong Power Grid Guangzhou China China Energy Engineering Group Guangdong Electric Power Design Institute Co. Ltd. Guangzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

With the continuous progress of power system and power electronics technology, multiple power electronic devices play an increasingly important role in power systems. Due to the variety and large number of source-side power electronic devices with different operating principles and control strategies, direct modeling and analysis have become extremely complex. Therefore, this paper addressed the problem of simplifying the model by aggregating multiple power electronic devices on the source side and proposes an aggregated model construction technique based on the multi-inverter single-machine equivalence method, which aims to improve the modeling efficiency of the power system, reduce the difficulty of analysis, and optimize the system performance. Through theoretical analysis and experimental verification, it has been proven that the method can effectively simplify the model of source-side power electronic devices under certain conditions and improve the modeling accuracy and analysis efficiency to a certain extent.

关键词： Analytical models Computational modeling System performance Power system stability Power electronics Stability analysis Reduced order systems Hybrid power systems Wind turbines Transient analysis

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PWA-CTM: An Extended Cell-Transmission Model based on Piecewise Affine Approximation of the Fundamental Diagram 30

PWA-CTM: An Extended Cell-Transmission Model based on Piecew...

引用

30th Mediterranean conference on control and Automation (MED)

作者： Alimardani, Fatemeh Baras, John S. Univ Maryland Elect & Comp Engn Dept College Pk MD 20742 USA Univ Maryland Inst Syst Res College Pk MD 20742 USA

ISBN: (纸本)9781665406734

Throughout the past decades, many different versions of the widely used first-order Cell-Transmission Model (CTM) have been proposed for optimal traffic control. Highway traffic management techniques such as Ramp Metering (RM) are typically designed based on an optimization problem with nonlinear constraints originating in the flow-density relation of the Fundamental Diagram (FD). Most of the extended CTM versions are based on the trapezoidal approximation of the flow-density relation of the Fundamental Diagram (FD) in an attempt to simplify the optimization problem. However, this relation is naturally nonlinear, and crude approximations can greatly impact the efficiency of the optimization solution. In this study, we propose a class of extended CTMs that are based on piecewise affine approximations of the flow-density relation such that (a) the integrated squared error with respect to the true relation is greatly reduced in comparison to the trapezoidal approximation, and (b) the optimization problem remains tractable for real-time application of ramp metering optimal controllers. A two-step identification method is used to approximate the FD with piecewise affine functions resulting in what we refer to as PWA-CTMs. The proposed models are evaluated by the performance of the optimal ramp metering controllers, e.g. using the widely used PI-ALINEA approach, in complex highway traffic networks. Simulation results show that the optimization problems based on the PWA-CTMs require less computation time compared to other CTM extensions while achieving higher accuracy of the flow and density evolution. Hence, the proposed PWA-CTMs constitute one of the best approximation approaches for first-order traffic flow models that can be used in more general and challenging modeling and control applications.

关键词： Road transportation Automation Computational modeling Simulation Traffic control Real-time systems Optimization

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Challenges for Lane Sensing and Tracking Algorithms of the Automated Driving Support System 2

Challenges for Lane Sensing and Tracking Algorithms of the A...

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2nd IEEE international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Prasannablessy, M. Senthilprabha, R. India Department of Information Technology Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Lane Recognition is a vital technology for the safe and effective navigations, and it plays a very important role in the development of an self-driving cars. Conventional lane recognition methods have always depended on a mix of manually designed features and lengthy post-processing procedures. Traditional lane detecting systems are made by hand and are therefore specific to particular situations, which increases the likelihood of them malfunctioning on a variety of unpredictable road conditions. The development of Deep Learning (DL) in recent years has completely changed several facets of computer vision, including lane detection. One Kind of deep learning models called convolutional neural networks (CNNs) has shown impressive results in pixel-level tasks like lane segmentation. Without the requirement for human feature extraction, CNNs may automatically extract hierarchical features from the input data. Because of this, they may adjust to a variety of visual patterns and circumstances, possibly providing more robustness than conventional techniques. Some of the common problems are roads without visible lane lines, surfaces damaged by debris or fractures, and hazy or dim lane markers. Under such circumstances, CNN-based models may perform less well, which could result in errors and even safety risks. Moreover, a lot of modern CNN models require a lot of processing resources because to their complex designs. They are not feasible for deployment in embedded systems, which are usually limited by power, size, and processing capabilities, because to this requirement for computational resources. CNN-based lane detection in autonomous vehicles is not widely used due to its need on expensive hardware, especially for applications that need to be integrated into small and energy-efficient platforms. More compact and effective models that can achieve great performance without using a lot of processing power are therefore desperately needed © 2024 IEEE.

关键词： Adaptive cruise control

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Research on prediction method of Atmospheric carbon monoxide Concentration based on Deep Learning 5

Research on prediction method of Atmospheric carbon monoxide...

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5th international Academic Exchange conference on Science and Technology Innovation, IAECST 2023

作者： Zhang, Yan Zhou, Qi Ma, Xiaozhen Li, Shuxian Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350357738

In order to solve the problems existing in the existing atmospheric environment prediction models in accurately predicting the change trend of carbon monoxide (CO) concentration in the atmosphere, Sparrow optimization algorithm (SSA) is proposed to optimize the CO concentration prediction model of convolutional neural network (CNN) and bidirectional long Short-Term memory neural network (BILSTM). The SSA algorithm was used to optimize the hyperparameters in the CNN-BILSTM model to confirm the best model. Then, the feature extraction of CO concentration data is carried out by CNN, and the time series modeling is carried out by BILSTM, and finally the CO concentration data prediction results in the next period of time are obtained. The results show that compared with the CNN-BILSTM model, the MAE value of the SSA-CNN-BILSTM model is reduced by 0.0083, the MAPE value is reduced by 2.142%, and the RMSE value is reduced by 0.01. SSA-CNN-BILSTM can predict the change trend of CO concentration more accurately, and can better adapt to complex atmospheric environmental conditions. © 2023 IEEE.

关键词： Long short-term memory

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Optimal Design of Instrumentation and control Components for Power Distribution systems

Optimal Design of Instrumentation and Control Components for...

引用

international conference on control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Yu.A. Klimenko Y. E. Lvovich A.P. Preobrazhensky Voronezh Institute of High Technologies Voronezh Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The paper examines the problems that arise in the course of organizing the formation of distribution energy systems. The development of a subsystem for managing and optimizing components that are part of energy systems is carried out. Proposals related to the formation of an optimization module are given. The features of a multi-level optimization model of the optimization module are discussed. A mathematical model is used on its basis, with the help of which it is possible to manage the quality of components of distribution energy systems. An illustration of a basic diagram of control over the production of energy components is given. A structural diagram of the distribution of the data flow in the optimization module is shown.

关键词： Automation Optimization models Instruments Power distribution Production control systems Energy efficiency Proposals Optimization

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Moving Horizon path planning for intelligent vehicle oriented to dynamic obstacle avoidance 6

Moving Horizon path planning for intelligent vehicle oriente...

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6th CAA international conference on Vehicular control and Intelligence, CVCI 2022

作者： Han, Jiaqi Cui, Maoyuan Lv, Ying Liu, Kewang Dai, Qikun Guo, Hongyan Intelligent Connected Vehicle Development Institute China Faw Group Co. Ltd Changchun China College of Communication Engineering Jilin University The State Key Laboratory of Automotive Simulation and Control Changchun China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

In urban conditions, the road environment is very complex and the vehicle itself has strong nonlinearity, which makes the decision and control of obstacle avoidance path planning very challenging. To solve the problem of difficult vehicle path planning, this paper proposes a method for path planning that combines artificial potential fields with model predictive control. Drawing on the concept of virtual force field, the virtual force field and model prediction are organically combined by abstracting the force field function as the optimization objective, which can realize the modeling of dynamic environment through virtual force field and consider the multi-task and multi-objective problems in path planning through model prediction. At the same time, the state changes of both the obstacle vehicle and the main vehicle are predicted, and a good path planning effect can be achieved in the low-speed dynamic environment. The simulation results indicate that the method can complete the path planning for static and dynamic obstacles and ensure the safety and stability of the vehicle. © 2022 IEEE.

关键词： Motion planning

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Online Optimization with Feedback Delay and Nonlinear Switching Cost 22

Online Optimization with Feedback Delay and Nonlinear Switch...

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2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint international conference on Measurement and modeling of Computer systems, SIGMETRICS/PERFORMANCE 2022

作者： Pan, Weici Shi, Guanya Lin, Yiheng Wierman, Adam Stony Brook University Stony BrookNY United States California Institute of Technology PasadenaCA United States

ISBN: (纸本)9781450391412

We study a variant of online optimization in which the learner receives k-round delayed feedback about hitting cost and there is a multi-step nonlinear switching cost, i.e., costs depend on multiple previous actions in a nonlinear manner. Our main result shows that a novel Iterative Regularized Online Balanced Descent (iROBD) algorithm has a constant, dimension-free competitive ratio that is O(L2k), where L is the Lipschitz constant of the nonlinear switching cost. Additionally, we provide lower bounds that illustrate the Lipschitz condition is required and the dependencies on k and L are tight. Finally, via reductions, we show that this setting is closely related to online control problems with delay, nonlinear dynamics, and adversarial disturbances, where iROBD directly offers constant-competitive online policies. This extended abstract is an abridged version of [2]. © 2022 Owner/Author.

关键词： online optimization online control online learning

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Learning to Solve Pod Retrieval as Sequential Decision Making Problem 17

Learning to Solve Pod Retrieval as Sequential Decision Makin...

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IEEE 17th international conference on control and Automation (ICCA)

作者： Fan, Yunfeng Deng, Fang Shi, Xiang Yang, Jing Beijing Inst Technol Sch Automat Control Sci & Engn Beijing 100081 Peoples R China Technol Chongqing Innovat Ctr Beijing Inst Chongqing 401120 Peoples R China Zhejiang Cainiao Supply Chain Management Co Ltd Hangzhou 310000 Peoples R China

ISBN: (纸本)9781665495721

The problem of pod retrieval in Robotic Mobile Fulfilment System (RMFS) is a key problem to improve the order picking efficiency. In such system, each robot needs to complete a set of retrieval requests, including bringing each pod from a retrieval location to a picking station and return the pod to a storage location. The objective is to minimize the total cost for each robot with all retrieval requests completed. In the previous literature, the problem was viewed as a static combinatorial optimization problem, which was commonly solved by heuristic methods. This kind of approachs often face with computational efficiency problems and are hard to satisfy the real-time requirement in complex real scenes. In this paper, we formulate the problem as a Markov Decision Process, a kind of Sequential Decision Making Problem, and then using Transformer with reinforcement learning to learn an efficient retrieval policy. The effectiveness of the method is verified by experiments.

关键词： Decision making Reinforcement learning Markov processes Transformers Real-time systems Decoding Resource management

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