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American control Conference (ACC)

作者： Gabriel F. Machado Morgan Jones Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Modern societies have an abundance of data yet good system models are rare. Unfortunately, many of the current system identification and machine learning techniques fail to generalize outside of the training set, producing models that violate basic physical laws. This work proposes a novel method for the Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI). SINDy-SI is an iterative method that uses Sum-of-Squares (SOS) programming to learn optimally fitted models while guaranteeing that the learned model satisfies side information, such as symmetries and physical laws. Guided by the principle of Occam's razor, that the simplest or most regularized best fitted model is typically the superior choice, during each iteration SINDy-SI prunes the basis functions associated with small coefficients, yielding a sparse dynamical model upon termination. Through several numerical experiments we will show how the combination of side information constraints and sparse polynomial representation cultivates dynamical models that obey known physical laws while displaying impressive generalized performance beyond the training set.

关键词： Training Programming Numerical simulation Data models Polynomials Numerical models System identification

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MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第9期8卷 4223-4236页

作者： Westny, Theodor Oskarsson, Joel Olofsson, Bjorn Frisk, Erik Linköping University Department of Computer and Information Science Linköping581 83 Sweden Lund University Department of Automatic Control Lund221 00 Sweden Linköping University Department of Electrical Engineering Linköping581 83 Sweden

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics. © 2016 IEEE.

关键词： Trajectories

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Optimal Flight Trajectories of Hybrid Electric Aircraft with In-Flight Charging

Optimal Flight Trajectories of Hybrid Electric Aircraft with...

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European control Conference (ECC)

作者： Yuanbo Nie Paing Ko Ross Drummond Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

The transition towards electric aircraft is particularly challenging due to the relatively low specific energy densities of electrical energy storage systems. If electric aircraft are to be realised, flight paths must be optimised to take advantage of the unique features of electric propulsion systems. In this paper, the problem of determining the optimal flight trajectories for aircraft powered by a combination of a fuel cell stack and lithium-ion battery pack is considered. Particular emphasis is given to the role of the battery pack's temperature and in-flight charging requirements on the results. Solutions that minimise the fuel consumption and the flight time are first considered. For both cases, the optimal solution was observed to discharge the battery during the climb with only minimal in-flight recharging of the battery by the fuel cell. A scenario that requires a fast climb and descent and the battery to be charged upon arrival was identified and shown to lead to an oscillatory profile for optimal in-flight charging. These results demonstrate the potential of solving optimal control problems to generate tailored electric aircraft trajectories.

关键词： Lithium-ion batteries Shafts Fuel cells Optimal control Motors Trajectory Aircraft

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Experimental Research of Modes of Operation of Local Autonomous Sources of Energy Supply with Induction Generator 4

Experimental Research of Modes of Operation of Local Autonom...

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4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Zachepa, Iurii Zachepa, Nataliia Khrebtova, Oksana Kobiliansky, Boris Turaev, Ilya Kremenchuk Mykhailo Ostrohradskyi National University Department of Systems of Automatic Control and Electric Drive Kremenchuk Ukraine Ukrainian Engineering Pedagogics Academy Department of Electromechanical and Computer Systems Kharkov Ukraine

ISBN: (纸本)9798350346831

The paper presents the results of experimental studies of autonomous power supply sources formed according to the 'internal combustion engine-induction generator' scheme. Autonomous local sources of energy supply according to this scheme are formed from existing electromechanical and electrotechnical equipment of other functional purposes only for the period of electricity demand. Such sources of electricity are universal for welding and other technological equipment of military and civil, including agricultural, machinery. According to the results, the viability of the proposed concept of creating autonomous power supply sources based on motor vehicles and electrical equipment has been proven. The limits of guaranteed excitation and subsequent trouble-free operation of autonomous local sources of power supply with power to consumers of various categories have been established. © 2022 IEEE.

关键词： Electric loads

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Creating Whole Story Virtual Laboratories for a First Course in control

Creating Whole Story Virtual Laboratories for a First Course...

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UKACC International Conference on control (control)

作者： J. A. Rossiter Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

There has a been a growing interest in virtual laboratories as a supplement to hardware laboratories in supporting student learning and experience. This paper focuses specifically on virtual laboratories built using the MATLAB environment and highlights some recent developments. Specifically, these virtual laboratories aim to give users an overview of the core content of an entire 1st course in control in a single virtual laboratory interface. This paper highlights two such laboratories and shows how they can be used to supplement other learning resources and activities.

关键词： Laboratories Hardware Matlab

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A non-iterative approach to Linear Quadratic Static Output Feedback

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IFAC-PapersOnLine 2023年第2期56卷 9540-9545页

作者： Hadriano Morales Escamilla Paul Trodden Visakan Kadirkamanathan Department of Automatic Control and Systems Engineering ACSE The University of Sheffield Sheffield UK

This paper considers the problem of static output feedback (SOF) synthesis for linear time-invariant (LTI) systems. Static output feedback, and more generally structured controller synthesis, is of special interest to any industrial application where a reduced-order controller is desired, e.g., high-order systems, or a specific structure is to be imposed, i.e., distributed/decentralised control. A simple two-step process is proposed, solving one Riccati equation and one optimisation problem with linear matrix inequality (LMI) constraints, enabled via a dilation using the distance to the full-state feedback optimum gain. Numerical analysis shows considerable computational savings, with negligible differences in the performance, when compared to current iterative methods.

关键词： Output feedback control controller constraints and structure Linear multivariable systems Linear matrix inequalities Optimal control Static controller synthesis S-procedure

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Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning 32

Uncertainty-Informed Model Selection Method for Nonlinear Sy...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Gu, Yuanlin Wei, Hua-Liang University of Stirling Division of Computer Science and Maths StirlingFK9 4LA United Kingdom University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom

ISBN: (纸本)9798350395440

Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks. © 2024 IEEE.

关键词： Machine learning

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Stress Matrix-Based Formation control of Multi-Agent systems with Discrete-Time Communication 12

Stress Matrix-Based Formation Control of Multi-Agent Systems...

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2024 12th International Conference on systems and control, ICSC 2024

作者： Onuoha, Okechi Ubochi, Brendan Chijioke Kurawa, Suleiman Tang, Zezhi Wu, Changshun Dong, Yi University of Hertfordshire United Kingdom Federal University of Technology The Department of Electrical and Electronic Engineering Akure Nigeria University of Manchester The Department of Electrical Engineering United Kingdom University of Sheffield The Department of Automatic Control and Systems Engineering United Kingdom University Grenoble Alpes Grenoble38000 France The School of Electronics & Computer Science University of Southampton United Kingdom

ISBN: (纸本)9798331531812

This study addresses the affine formation maneuver control of cooperative multi-agent systems (MAS) having periodic inter-agent communication for both static and dynamic leader cases. Here, we focus on the leader-follower MASs. The primary aim of the control system is to steer the entire collection of agents to produce required patterns (geometric) along with any required maneuver through the direct control of only few a selected agents referred to as leaders. Most of the existing works are constrained to either the individual agents communicate with each other in continuous-time or the sample-data scenario where the leaders are stationary or have constant acceleration or velocities. Here, we consider the scenarios where the velocities of the leaders can be time-varying or constant. Here, different cases are addressed and some control laws are proposed. Conditions are established to help guarantee the overall stability of the systems. A simulation study is employed for the illustration of our proposed laws. © 2024 IEEE.

关键词： Continuous time systems

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Optimized Spatial Filter Selection for Transfer Learning in Brain-computer Interface

Optimized Spatial Filter Selection for Transfer Learning in ...

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Metrology for Extended Reality, Artificial Intelligence and Neural engineering (MetroXRAINE), IEEE International Conference on

作者： Mohamed A A Mohamed Joshua Giles Mahnaz Arvaneh Automatic control and Systems Engineering Department University of Sheffield Sheffield UK

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

The Common Spatial Patterns (CSP) algorithm has shown great efficacy in extracting features for Brain-computer Interfaces (BCIs), particularly in motor imagery BCIs. However, CSP performs poorly when dealing with limited labeled data, which leads to long calibration time at the beginning of each session. To overcome this challenge, we leverage source data, i.e., labeled data from other subjects, to transfer knowledge across subjects. This work proposes a novel approach termed Scaled and Warped CSP-based Transfer Learning (SW-CSP-TL). This method aligns source data with the temporal and amplitude structure of target data before computing CSP filters, ensuring optimization for capturing discriminative features relevant to the target domain. We evaluate the proposed SW-CSP-TL algorithm using the publicly available dataset 2a from BCI Competition IV and compare its performance with that of the classical CSP algorithm and the transfer learning CSP algorithm (CSP-TL) without amplitude scaling and temporal warping. Our algorithm exhibits superior performance particularly when the training data size is relatively small. Results demonstrate that for 5 and 15 training trials per class from the target session, SW-CSP-TL outperformed the classical CSP by an average of 10%, and 8%, respectively.

关键词： Training Transfer learning Training data Neural engineering Feature extraction Spatial filters Motors Brain-computer interfaces Standards Optimization

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