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Gaussian Processes for Advanced Motion control*

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Gaussian Processes for Advanced Motion Control*

American control Conference (ACC)

作者： Maurice Poot Jim Portegies Noud Mooren Max van Haren Max van Meer Tom Oomen Department of Mechanical Engineering Control Systems Technology Section Eindhoven University of Technology Eindhoven The Netherlands Department of Mathematics and Computer Science CASA Eindhoven University of Technology Eindhoven The Netherlands Delft Center for Systems and Control Delft University of Technology Delft The Netherlands

ISBN: (数字)9781665451963

ISBN: (纸本)9781665494809

Machine learning techniques, including Gaussian processes (GPs), are expected to play a significant role in meeting speed, accuracy, and functionality requirements in future data-intensive mechatronic systems. This paper aims to reveal the potential of GPs for motion control applications. Successful applications of GPs for feedforward and learning control, including the identification and learning for noncausal feedforward, position-dependent snap feedforward, nonlinear feedforward, and GP-based spatial repetitive control, are outlined. Experimental results on various systems, including a desktop printer, wirebonder, and substrate carrier, confirmed that data-based learning using GPs can significantly improve the accuracy of mechatronic systems.

关键词： Mechatronics Gaussian processes Machine learning Printers Feedforward systems Motion control Substrates

Spiking neural networks: Towards bio-inspired multimodal perception in robotics

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear systems

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arXiv 2023年

作者： Koelewijn, Patrick J.W. Singh, Rajiv Seiler, Peter Tóth, Roland Sioux Technologies B.V. Eindhoven Netherlands Control Systems Group Eindhoven University of Technology Eindhoven Netherlands The MathWorks Inc. Natick United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data. This is achieved by a two-step procedure. In the first step, we learn a projection to a lower dimensional state-space. In step two, an LPV model is learned on the reduced-order state-space using a novel, efficient parameterization in terms of neural networks. The improved modeling accuracy of the method compared to an existing method is demonstrated by simulation examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Nonlinear dynamical systems

Consensus control of Leader-Following Multi-Agent systems in Directed Topology With Heterogeneous Disturbances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 423-431页

作者： Qinglai Wei Xin Wang Xiangnan Zhong Naiqi Wu the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China the Department of Computer and Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and TechnologyMacao 999078China

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown *** main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic *** traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent *** introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local *** the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is *** simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

关键词： Consensus control directed topology external disturbance multi-agent(MA)systems

On Approximate Opacity of Stochastic control systems

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arXiv 2024年

作者： Liu, Siyuan Yin, Xiang Dimarogonas, Dimos V. Zamani, Majid Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Key Lab of System Control & Information Processing Ministry of Education Shanghai China Computer Science Department University of Colorado BoulderCO80309 United States Computer Science Department Ludwig Maximilian University of Munich Germany

This paper investigates an important class of information-flow security property called opacity for stochastic control systems. Opacity captures whether a system’s secret behavior (a subset of the system’s behavior that is considered to be critical) can be kept from outside observers. Existing works on opacity for control systems only provide a binary characterization of the system’s security level by determining whether the system is opaque or not. In this work, we introduce a quantifiable measure of opacity that considers the likelihood of satisfying opacity for stochastic control systems modeled as general Markov decision processes (gMDPs). We also propose verification methods tailored to the new notions of opacity for finite gMDPs by using value iteration techniques. Then, a new notion called approximate opacity-preserving stochastic simulation relation is proposed, which captures the distance between two systems’ behaviors in terms of preserving opacity. Based on this new system relation, we show that one can verify opacity for stochastic control systems using their abstractions (modeled as finite gMDPs). We also discuss how to construct such abstractions for a class of gMDPs under certain stability conditions. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic control systems

Distributionally Robust control for Chance-Constrained Signal Temporal Logic Specifications

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Distributionally Robust Control for Chance-Constrained Signa...

IEEE Conference on Decision and control

作者： Arash Bahari Kordabad Eleftherios E. Vlahakis Lars Lindemann Dimos V. Dimarogonas Sadegh Soudjani Max Planck Institute for Software Systems Kaiserslautern Germany Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science Viterbi School of Engineering University of Southern California Los Angeles USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We consider distributionally robust optimal control of stochastic linear systems under signal temporal logic (STL) chance constraints when the disturbance distribution is unknown. By assuming that the underlying predicate functions are Lipschitz continuous and the noise realizations are drawn from a distribution having a concentration of measure property, we first formulate the underlying chance-constrained control problem as stochastic programming with constraints on expectations and propose a solution using a distributionally robust approach based on the Wasserstein metric. We show that by choosing a proper Wasserstein radius, the original chance-constrained optimization can be satisfied with a user-defined confidence level. A numerical example illustrates the efficacy of the method.

关键词： Linear systems Robust control Noise Optimal control Transforms Programming Probabilistic logic Trajectory Noise measurement Optimization

Pattern Recognition Method for Detecting Engineering Errors on Technical Drawings

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Pattern Recognition Method for Detecting Engineering Errors ...

AI IoT Congress (AIIoT), World

作者： Rimma Dzhusupova Richa Banotra Jan Bosch Helena Holmströ m Olsson Department of Electrical &#x0026 Instrumentation Control &#x0026 Safety Systems McDermott The Hague The Netherlands Department of Instrumentation Control &#x0026 Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Media Technology Malm&#x00F6 University Malm&#x00F6 Sweden

Many organizations are looking for how to automate repetitive tasks to reduce manual work and free up resources for innovation. Machine Learning, especially Deep Learning, increases the chance of achieving this goal while working with technical documentation. Highly costly engineering hours can be saved, for example, by empowering the manual check with AI, which helps to reduce the total time for technical documents review. This paper proposes a way to substantially reduce the hours spent by process engineers reviewing P&IDs (Piping & Instrumentation Diagrams). The developed solution is based on a deep learning model for analyzing complex real-life engineering diagrams to find design errors - patterns that are combinations of high-level objects. Through the research on an extensive collection of P&ID files provided by McDermott, we prove that our model recognizes patterns representing engineering mistakes with high accuracy. We also describe our experience dealing with class-imbalance problems, labelling, and model architecture selection. The developed model is domain agnostic and can be re-trained on various schematic diagrams within engineering fields and, as well, could be used as an idea for other researchers to see whether similar solutions could be built for different industries.

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Dual-beam photothermal material trace detection method based on self-mixing interference

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Applied Optics 2025年第16期64卷 4549-4558页

作者： Zhao, Yan Zhi, Yunlong Zhao, Hui Dong, Daming Tong, Jigang Tu, Chunling School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin300384 China National Engineering Research Center for Intelligent Agricultural Equipment Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria0188 South Africa

In this paper, a dual-beam photothermal self-mixing substance trace detection system is proposed. The crystal violet (CV) solution of the sample undergoes a thermal lens effect under the action of pump photoperiod excitation, and the refractive index changes cause the deviation of the probe beam, and the self-mixing interference (SMI) effect occurs when the modulated light returns to the cavity. By combining theoretical and experimental results, the power fluctuation of the SMI signal caused by the thermo-optic effect is linearly related to CV concentration and other parameters. At an excitation power of 24 mW, the detection limit can reach 8.0 × 10−8 mol/L. This method does not require labeling or complex CV pretreatment and has high sensitivity and flexibility, providing a guide for CV characterization in biological, environmental, and pharmaceutical research. © 2025 Optica Publishing Group.

关键词： Gaussian beams Laser beams Optical fields Phase shift Refractive index Spatial resolution

Dissipative Avoidance Feedback for Reactive Navigation Under Second-Order Dynamics

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arXiv 2024年

作者： Smaili, Lyes Tang, Zhiqi Berkane, Soulaimane Hamel, Tarek Department of Computer Science and Engineering University of Quebec in Outaouais GatineauQC Canada Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-10044 Sweden Department of Electrical Engineering Lakehead University ON Canada France

This paper addresses the problem of autonomous robot navigation in unknown, obstacle-filled environments with second-order dynamics by proposing a Dissipative Avoidance Feedback (DAF). Compared to the Artificial Potential Field (APF), which primarily uses repulsive forces based on position, DAF employs a dissipative feedback mechanism that accounts for both position and velocity, contributing to smoother and more natural obstacle avoidance. The proposed continuously differentiable controller solves the motion-to-goal problem while guaranteeing collision-free navigation by using the robot’s state and local obstacle distance information. We show that the controller guarantees safe navigation in generic n-dimensional environments and that all undesired ω-limit points are unstable under certain controlled curvature conditions. Designed for real-time implementation, DAF requires only locally measured data from limited-range sensors (e.g., LiDAR, depth cameras), making it particularly effective for robots navigating unknown workspaces. Simulations in 2D and 3D environments are conducted to validate the theoretical results and to showcase the effectiveness of our approach. © 2024, CC BY.

关键词： Feedback