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22th Annual Conference Towards Autonomous Robotic systems, TAROS 2021

作者： Dhesi, Arjan Groß, Roderich Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9783030891763

This work proposes a novel solution to the problem of covering a bounded grid world using a swarm of robotic agents. The controller requires no run-time memory and only few, discrete sensory inputs. Two variants of the solution to the problem are studied, one effectively modulating the sensing range based on the agent’s context. It is found that during the dispersion, the controller with sensing range modulation outperforms the default controller in terms of speed and evenness of the dispersion. Due to its simplicity, the solution could be realised on swarms of agents with ultra-low power and computational requirements, making it potentially relevant for large-scale swarm applications. © 2021, Springer Nature Switzerland AG.

关键词： Multipurpose robots

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Adaptive Iterative Learning control for Robotic Manipulators

Adaptive Iterative Learning Control for Robotic Manipulators

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UKACC International Conference on control (control)

作者： Yu Dou Emmanuel Prempain Lanlan Su School of Engineering University of Leicester Leicester United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

A refined control scheme is presented to optimise the trajectory-tracking performance of robotic manipulators. This strategy integrates a linear feedback controller and a feedforward learning controller. The former mitigates unknown disturbances and desensitises the system to unidentified parameters, while the latter enhances tracking efficiency by incorporating past tracking errors. Traditionally, a fixed learning gain is used in the learning law to update the control input. However, we modify the learning law in this study by applying an adaptive learning gain. Our simulations on a robotic manipulator demonstrate that the adaptive ILC algorithm surpasses the classical ILC algorithm concerning convergence speed. These findings highlight the advantages of our approach, showcasing its extensive applicability in trajectory tracking.

关键词： Adaptive learning Adaptation models Trajectory tracking Manipulators Feedforward systems Adaptive control Convergence

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A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings

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International Journal of Automation and computing 2021年第4期18卷 581-593页

作者： Zhao-Hua Liu Xu-Dong Meng Hua-Liang Wei Liang Chen Bi-Liang Lu Zhen-Heng Wang Lei Chen School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China Department of Automatic Control and Systems Engineering University of SheffieldSheffield SI 3JDUK

Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life(RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network(LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM *** this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.

关键词： Deep learning fault diagnosis fault prognosis long and short time memory network(LSTM) rolling bearing rotating machinery regularization remaining useful life prediction(RUL) recurrent neural network(RNN)

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An Interacting Multiple Model Approach Based on Maximum Correntropy Student's T Filter

An Interacting Multiple Model Approach Based on Maximum Corr...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Fethi Candan Aykut Beke Lyudmila Mihaylova Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Department of Control Systems Design ASELSAN Ankara Turkiye

This paper presents a novel approach called the Interacting Multiple Model (IMM)-based Maximum Correntropy Student's T Filter (MCStF), which addresses the challenges posed by non-Gaussian measurement noises. The MCStF demonstrates superior performance compared to the IMM algorithm based on Kalman Filters (KFs) in both simulation environments and real-time systems. The Crazyflie 2.0 nano Unmanned Air Vehicle (UAV) model is used in the simulation validation, and results from 3000 independent Monte Carlo runs are shown. After getting the simulation results under monotonously changed non-Gaussian distribution, their performance results have been compared to each other. The same scenario has been applied in the real-time system using Crazyflie 2.0. Next, results from real-time tests are presented in which the position of Crazyflie 2.0 is estimated online.

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Incremental Stability Analysis of Lurie systems

Incremental Stability Analysis of Lurie Systems

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European control Conference (ECC)

作者： Lanlan Su Ross Drummond Sei Zhen Khong Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK Department of Electrical Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

The incremental stability analysis of Lurie systems consisting of the feedback interconnection between a linear time-invariant (LTI) system and a slope-bounded nonlinearity is considered. We first show that the incremental input-output mappings generated by the set of slope-bounded nonlinearities satisfy a set of biased integral quadratic constraints (IQCs) defined by Popov multipliers. Then, a frequency-domain inequality (FDI) condition on the LTI system is proposed for establishing incremental closed-loop stability via an incremental form of IQC theory. Application of the KYP lemma yields an equivalent linear matrix inequality (LMI) condition.

关键词： Linear systems Frequency-domain analysis Europe Aerospace electronics Stability analysis Linear matrix inequalities

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Deep Reinforcement Learning Method for control of Mixed Autonomy Traffic systems

Deep Reinforcement Learning Method for Control of Mixed Auto...

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International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Xingyu Liu Esa Apriaskar Lyudmila Mihaylova Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Department of Electrical Engineering Faculty of Engineering Universitas Negeri Semarang Semarang Indonesia

ISBN: (数字)9798350368031

ISBN: (纸本)9798350368048

The introduction of autonomous vehicles (AVs) presents a novel approach to regulating and optimising traffic flow through the automated control of AVs. In this context, the AV is defined as the actuator and an optimal control policy is desired to make control decisions. Deep Reinforcement Learning (DRL) is a novel method which aims to maximize the cumulative rewards given by the predefined reward function by making sequential decisions in a stochastic environment. In light of the above, we propose a DRL-based vehicular control method to train an optimal policy for the control of AV in a model-free fashion, and consequently improve the traffic efficiency with the obtained control policy. A single-lane circular road environment with both AV and human-driven vehicles is selected to serve as the mixed autonomy traffic system in the Simulation of Urban MObility (SUMO) [1] traffic simulator, and the Proximal Policy Optimization (PPO) algorithm is applied for the policy improvement. Simulation results demonstrate that our strategy is effective in mitigating the unstable stop-and-go waves, increasing 67.7% of the average driving speed and reducing 19.3% of the average energy consumption in a closed-ring road environment.

关键词： Roads Simulation Heuristic algorithms Stochastic processes Optimal control Deep reinforcement learning Fuels Intelligent systems Traffic congestion Optimization

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On the Gaussian Filtering for Nonlinear Dynamic systems Using Variational Inference 27

On the Gaussian Filtering for Nonlinear Dynamic Systems Usin...

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27th International Conference on Information Fusion, FUSION 2024

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Li, Ji University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9781737749769

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements. © 2024 ISIF.

关键词： Wiener filtering

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CDQKL: Consensus-Based Distributed Quantum Kernel Learning

CDQKL: Consensus-Based Distributed Quantum Kernel Learning

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Wenxuan Ma Mengxiang Liu Ruilong Deng State Key Laboratory of Industrial Control Technology and the College of Control Science and Engineering Zhejiang University Hangzhou China Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

ISBN: (数字)9798350366778

ISBN: (纸本)9798350366785

The field of quantum computing has developed rapidly in recent years due to its promising trend of surpassing traditional machine learning in terms of speed and effectiveness. Quantum kernel learning is one of the paradigms of quantum machine learning, but the training of quantum kernel is time consuming. Therefore, this work makes the first attempt to introduce a consensus-based distributed approach to quantum kernel learning - named CDQKL - that only requires to exchange model parameter information between adjacent nodes while avoiding the need of sharing local training data. Through comparative experimental studies, the advantages of CDQKL in classification accuracy and convergence speed are verified. Considering the popularization of quantum computing cloud service and miniaturization of quantum terminals, the CDQKL adapting to this trend is able to play a vital role in data security, which implies the far-reaching significance of this work. Our code is available at https://***/Leisurivan/CDOKL.

关键词： Training Adaptation models Quantum computing Accuracy Training data Machine learning Market research

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Acoustic Echo Sensing for Robot Localization in Buried Pipe Networks

TechRxiv

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TechRxiv 2023年

作者： Worley, Rob Yu, Yicheng Horoshenkov, Kirill V. Anderson, Sean R. Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Department of Mechanical Engineering University of Sheffield Sheffield United Kingdom

Robot localization in buried pipes presents a number of challenges, including the unavailability of methods such as a global positioning system, and the limited perspective of sensors such as vision. This paper addresses these challenges by using acoustic sensing, where signals sent by robots propagate long distances and around corners in the pipe environment, and can be used to estimate the distance to reflective features. It is shown that the reverberant environment causes a number of echoes which contain useful information. A novel algorithm for using this information in pose-graph optimization is proposed, and is shown to be necessary in comparison to a naive approach. The algorithm is demonstrated to be accurate and robust by measuring the error rate, which is the proportion of estimate error that was greater than a target threshold of 0.5 metres. The median error rate was 0 for measurement uncertainty 2.5 times larger than that found experimentally, and for motion uncertainty 4 times that which could be tolerated by an idealised loop-closure approach used for comparison. This work is the foundation for new data fusion methods for robot localization in buried pipes. © 2023, CC BY.

关键词： Mapping

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

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

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Mediterranean Conference on control and Automation (MED)

作者： Yuanlin Gu Hua-Liang Wei Division of Computer Science and Maths University of Stirling Stirling United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

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.

关键词： Data-driven modeling Adaptation models Uncertainty Recurrent neural networks Training data Machine learning Data models

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