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42nd Chinese control Conference, CCC 2023

作者： Li, Chengxi Gong, Wei Li, Li Tongji University The Department of Control Science and Engineering Shanghai China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China

ISBN: (纸本)9789887581543

High-quality sparse maps (e.g., radio maps, noise maps, and hydrographs) are essential in environmental monitoring, decision-making, path navigation, etc. However, constructing high-quality maps requires intensive data sampling, which is a repetitive and tedious task. To address this issue, we propose a new sparse map construction method based on Bayesian optimization in this paper. First, by exploiting the idea of spatial clustering, a novel acquisition method is proposed, which considers potential correlations between different points. This method further guides intelligent agents to collect samples at the most informative points. Second, by designing inference models, high-precision and stable maps can be gradually constructed as the number of sampling points increases. Next, a customized dataset, which contains Wi-Fi signal strength in a complex indoor environment, is collected to support the performance validation of our proposed method. Finally, we test the performance of our methods on this customized dataset. Experimental results show that our proposed methods outperform other existing methods including random search, grid search, and maximum predictive error sampling. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Decision making

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Distributed data-driven consensus control of multi-agent systems under switched uncertainties

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control Theory and Technology 2023年第3期21卷 478-487页

作者： Wenjie Liu Yifei Li Gang Wang Jian Sun Jie Chen National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China Department of Control Science and Engineering Tongji UniversityShanghai 201804China

Cooperative control of multi-agent systems(MASs),particularly consensus control,has gained significant attention in the last two decades,thanks to the rapid and sustained development of distributed and networked *** this paper,we present some new results focused on consensus control of a set of unknown linear MASs(whose system matrices are unknown)under unknown switched uncertainties,with an emphasis on distributed data-driven *** proposed controller is end-to-end,designed by solving two data-based semi-definite programs(SDPs),which adjust to the changes of the uncertainty *** approach achieves asymptotic consensus of the MAS provided that the switching is slow enough and the uncertainty is *** illustrate the effectiveness of our proposed method through a numerical example.

关键词： Data-driven control Multi-agent systems Consensus-Switched uncertainties

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation 26

Out-of-distribution Object Detection through Bayesian Uncert...

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26th International Conference on Information Fusion, FUSION 2023

作者： Zhang, Tianhao Wang, Shenglin Bouaynaya, Nidhal Calinescu, Radu Mihaylova, Lyudmila The University of Sheffield Automatic Control and Systems Engineering United Kingdom Rowan University Department of Electrical and Computer Engineering United States University of York Department of Computer Science United Kingdom

ISBN: (纸本)9798890344854

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset. © 2023 International Society of Information Fusion.

关键词： Object detection

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Robust Iris Centre Localisation for Assistive Eye-Gaze Tracking

arXiv

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arXiv 2024年

作者： Sandamal, Nipun Cristina, Stefania Camilleri, Kenneth P. Department of Systems and Control Engineering Faculty of Engineering University of Malta Msida Malta

In this research work, we address the problem of robust iris centre localization in unconstrained conditions as a core component of our eye-gaze tracking platform. We investigate the application of U-Net variants for segmentation-based and regression-based approaches to improve our iris centre localisation, which was previously based on Bayes' classification. The achieved results are comparable to or better than the state-of-the-art, offering a drastic improvement over those achieved by the Bayes' classifier, and without sacrificing the real-time performance of our eye-gaze tracking platform. © 2024, CC BY-NC-SA.

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Trajectory Planning of Autonomous Mobile Robot using Model Predictive control in Human-Robot Shared Workspace 3

Trajectory Planning of Autonomous Mobile Robot using Model P...

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3rd IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Chen, Jieming Chen, Xiang Liu, Steven The Hong Kong Polytechnic University Department of Electrical Engineering Hong Kong Hong Kong University of Kaiserslautern Institute of Control Systems Kaiserslautern Germany

ISBN: (纸本)9798350398410

This paper proposes an integrated framework of trajectory planning and control for autonomous mobile robots (AMRs) in an intra-logistic scenario, where humans and robots share the same indoor workspace. The proposed framework consists of perception and trajectory planning. To perceive human motion, information from the RGB-D camera is used to detect human positions. Then a model-based tracking approach is employed to track multiple people's motion and estimate their speed over a short time horizon. For trajectory planning, a Model Predictive control (MPC) based scheme is adopted to generate motor commands while taking into account energy efficiency, safety, and addressing human-aware proximity constraints. To convexify the nonlinear collision avoidance constraints, Sequential Convexification Programming is employed. The simulation and experimental results demonstrate that the proposed method can be implemented in real-time and efficiently avoid people in advance. © 2023 IEEE.

关键词： Model predictive control

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Analyszing Signal Integrity in a Structure with Double Modal Redundancy Before and After Failures

Analyszing Signal Integrity in a Structure with Double Modal...

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Informatics, Electronics, and Radio engineering (PIERE), International Conference Problems of

作者： Ksenia N. Abramova Andrey V. Chub Yevgeniy S. Zhechev Department of Television and Control Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Department of Microwave and Quantum Radio Engineering Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

This paper presents the results of a signal integrity analysis for a structure with double modal redundancy (MR) before and after failures. Two failure cases are considered: open circuit and short circuit. The study investigates key signal integrity parameters such as S-parameters, signal phase, group delay time, and eye diagrams. The results show that after the first failure, the bandwidth of the structure increases, while it decreases after the second failure. The change in bandwidth affects the data rate that can be transmitted to the device input without significant signal distortion. Eye diagrams are analyzed for each case considering the data rate based on the bandwidth. The analysis of eye diagrams reveals that despite some deviations from the ideal eye shape in certain cases, the eye remains open, indicating a low probability of bit errors. A comparison of the results before and after failure, with the same data rate, shows that the failures in the redundant circuit have a negligible impact on the signal integrity within the bandwidth.

关键词： Shape Redundancy Low-pass filters Bandwidth Reliability engineering Scattering parameters Delays Passband Microstrip Signal integrity

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SS-DBSCAN: Epsilon Estimation with Stratified Sampling for Density-Based Spatial Clustering of Applications with Noise

SS-DBSCAN: Epsilon Estimation with Stratified Sampling for D...

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2023 International Conference on Automation, control and Electronics engineering, CACEE 2023

作者： Monko, Gloriana Joseph Kimura, Masaomi Shibaura Institute of Technology Graduate School Department of Functional Control Systems Tokyo Japan Shibaura Institute of Technology Department of Computer Science and Engineering Tokyo Japan

ISBN: (纸本)9798350309201

Clustering algorithms are crucial in uncovering hidden patterns and structures within datasets. Among the density-based clustering algorithms, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has gained considerable attention for its effectiveness in various applications. However, determining appropriate parameter values for this algorithm remains a challenging task. This paper presents a novel methodology for eps parameter estimation for an improved DBSCAN, namely SS-DBSCAN. The experimental results across nine datasets demonstrate the efficacy of our proposed method in accurately determining clusters with eps value from SS-DBSCAN algorithm. The clusters identified using estimated eps values by SS-DBSCAN align well with the inherent structure of the datasets, yielding better cluster results than the manually set parameters and other methods used for automatic estimations of the eps for DBSCAN. Our approach adapted well to the peculiarities of each dataset, whether dealing with different scales, dimensions, or densities;it proved the versatility and robustness across various datasets, thereby emphasizing its generalizability and potential for broader applications. © 2023 IEEE.

关键词： Parameter estimation

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A Resilient Data-Driven controller Against DoS Attacks 41

A Resilient Data-Driven Controller Against DoS Attacks

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第41届中国控制会议

作者： Wenjie Liu Jian Sun Gang Wang Jie Chen Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Department of Control Science and Engineering Tongji University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper is concerned with the stabilization problem of linear time-invariant systems under Denial-of-Service(DoS)***,system matrices are unknown,and only some input-output trajectories collected from off-line experiments are ***,in the model-based case,it has been proven that systems equipped with a predictor-based state feedback controller achieve resilience against DoS ***,this is difficult or even impossible to be implemented in the absence of a system model,thus leading to resilient control challenging in the data-based *** maintain resilience against DoS attacks in the data-based case,a data-driven model predictive control(MPC) scheme is proposed such that future system input-output trajectories can be obtained by solving a data-dependent optimal *** this scheme,a data-driven resilient control strategy is developed such that the system achieves the same level of resilience of the DoS attacks as the model-based ***,a numerical example is given to validate the effectiveness of the proposed method.

关键词： Denial-of-service attack data-driven control model predictive control input-to-state stability

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Adaptive-Optimal control for Reconfigurable Robots

Adaptive-Optimal Control for Reconfigurable Robots

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2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Maheshwari, Adya Rautela, Aaditya Rayguru, Madan Mohan Valluru, Sudarshan K. Delhi Technological University Department of Electrical Engineering New Delhi India Delhi Technological University Control of Dynamical Systems and Computation Laboratory Department of Electrical Engineering New Delhi India

ISBN: (纸本)9781665474924

Reconfigurable robots refer to the robotic systems which have the ability to transform their morphological structure to adapt to changes in their surroundings. A specialized control system based upon the dynamic characteristics is required to be modelled so that a vast range of operations can be performed by the reconfigurable robots. Reinforcement learning based technique is therefore considered as a suitable mechanism for carrying out adaptive optimal control for the robotic systems. In this paper, an attempt has been made to model the adaptive optimal control mechanism for a single-link manipulator: a reconfigurable robotic system. The system has been modelled for a dynamic environment. The loss and reward plots are obtained from the simulation of the robotic system by deploying reinforcement learning based techniques with the given controlled input © 2023 IEEE.

关键词： Reinforcement learning

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ReACT: Reinforcement Learning for controller Parametrization Using B-Spline Geometries

ReACT: Reinforcement Learning for Controller Parametrization...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Rudolf, Thomas Flögel, Daniel Schürmann, Tobias Süß, Simon Schwab, Stefan Hohmann, Sören FZI Research Center for Information Technology Embedded Systems and Sensors Engineering Department Karlsruhe76131 Germany Institute of Control Systems The Karlsruhe Institute of Technology Karlsruhe76131 Germany

ISBN: (纸本)9798350337020

Robust and performant controllers are essential for industrial applications. However, deriving controller parameters for complex and nonlinear systems is challenging and time-consuming. To facilitate automatic controller parametrization, this work presents a novel approach using deep reinforcement learning (DRL) with N-dimensional B-spline geometries (BSGs). We focus on the control of parameter-variant systems, a class of systems with complex behavior which depends on the operating conditions. For this system class, gain-scheduling control structures are widely used in applications across industries due to well-known design principles. Facilitating the expensive controller parametrization task regarding these control structures, we deploy an DRL agent. Based on control system observations, the agent autonomously decides how to adapt the controller parameters. We make the adaptation process more efficient by introducing BSGs to map the controller parameters which may depend on numerous operating conditions. To preprocess time-series data and extract a fixed-length feature vector, we use a long short-term memory (LSTM) neural networks. Furthermore, this work contributes actor regularizations that are relevant to real-world environments which differ from training. Accordingly, we apply dropout layer normalization to the actor and critic networks of the truncated quantile critic (TQC) algorithm. To show our approach's working principle and effectiveness, we train and evaluate the DRL agent on the parametrization task of an industrial control structure with parameter lookup tables. © 2023 IEEE.

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