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Adaptive PI-Fuzzy Like control of a Stack Pneumatic Actuators Testbed for Multi-Configuration Small Scale Soft Robotics

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Adaptive PI-Fuzzy Like Control of a Stack Pneumatic Actuator...

International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)

作者： Ammar K. Al Mhdawi Nick Wright Amjad J. Humaidi Ahmad Taher Azar Department of Electrical and Electronic Engineering Newcastle University Newcastle UK Edge Hill University UK ASSCL Lab Prince Sultan University KSA Control and Systems Engineering Department University of Technology Baghdad Iraq College of Computer & Information Sciences Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Egypt

It has become evident that pneumatic muscle-based robots are the most compliant of all soft and semi-soft robotic testbeds under investigation, despite the fact that the artificial muscle-based robot is the most challenging due to its hyperelastic behavior resulting from the constitutive elastic material. The purpose of this paper is to examine the development of a physical pneumatic actuation testbed and the application of control strategies for precision small scale soft robotics control. Moreover, an adaptive PI-Fuzzy like control is proposed for reference signal tracking. It is imperative to note that in this application, the controller's performance is determined by its ability to track a reference signal as well as its ability to minimize the absolute tracking error. The Fuzzy Logic controller (FLC) controller, including the nature of the membership functions and the rule base, is described in detail. Simulations and hardware testing have revealed that the adaptive PI-Fuzzy like controller outperforms the traditional FLC controller in terms of experimental results. Additionally, the actuation speed is controlled under different loading conditions. It is considered that the proposed testbed is capable of controlling and managing multi-configurations of medium to small scale soft-based robots efficiently and effectively.

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Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Mathematics Southeast University Nanjing210096 China The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms require that each agent broadcasts its decision to its neighbors at each iteration. To better utilize the limited communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in UnderRepresented Populations

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arXiv 2024年

作者： Kulyabin, Mikhail Zhdanov, Aleksei Maier, Andreas Loh, Lynne Estevez, Jose J. Constable, Paul A. Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Engineering School of Information Technologies Telecommunications and Control Systems Ural Federal University Yekaterinburg Russia Flinders University College of Nursing and Health Sciences Caring Futures Institute AdelaideSA Australia

Visual electrophysiology is often used clinically to determine the functional changes associated with retinal or neurological conditions. Te full-feld fash electroretinogram (ERG) assesses the global contribution of the outer and inner retinal layers initiated by the rods and cone pathways depending on the state of retinal adaptation. Within clinical centers, reference normative data are used to compare clinical cases that may be rare or underpowered within a specifc demographic. To bolster either the reference dataset or the case dataset, the application of synthetic ERG waveforms may ofer benefts to disease classifcation and case-control studies. In this study and as a proof of concept, artifcial intelligence (AI) to generate synthetic signals using generative adversarial networks is deployed to upscale male participants within an ISCEV reference dataset containing 68 participants, with waveforms from the right and left eye. Random forest classifers further improved classifcation for sex within the group from a balanced accuracy of 0.72–0.83 with the added synthetic male waveforms. Tis is the frst study to demonstrate the generation of synthetic ERG waveforms to improve machine learning classifcation modelling with electroretinogram waveforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Ophthalmology

Anti-disturbance filter design for a class of stochastic systems with fading channels

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Science China(Information Sciences) 2020年第11期63卷 327-329页

作者： Yang LIU Zidong WANG Hongli DONG Hongjian LIU College of Electrical Engineering and Automation Shandong University of Science and Technology Department of Computer Science Brunel University London Institute of Complex Systems and Advanced Control Northeast Petroleum University School of Mathematics and Physics Anhui Polytechnic University

Dear editor,As a significant topic in control and signal processing communities, the filtering problem has been attracting persistent research attention in the past few decades [1]. In practical engineering, the parameter perturbations are unavoidable due mainly to exogenous disturbances, environmental changes and some other phenomena.

关键词： Stochastic systems fault detection model uncertainty anti-disturbance fading channel

Active Wildfires Detection and Dynamic Escape Routes Planning for Humans Through Information Fusion between Drones and Satellites

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Active Wildfires Detection and Dynamic Escape Routes Plannin...

International Conference on Intelligent Transportation

作者： Chang Liu Tamas Sziranyi Department of Networked Systems and Services Budapest University of Technology and Economics Müegyetem rkp. 3 H-1111 Budapest Hungary and Machine Perception Research Laboratory of Institute for Computer Science and Control (SZTAKI) Budapest Hungary Faculty of Transportation Engineering and Vehicle Engineering Machine Perception Research Laboratory of Institute for Computer Science and Control (SZTAKI) Budapest University of Technology and Economics (BME-KJK) Müegyetem rkp. 3 H-1111 Budapest Hungary Budapest Hungary

UAVs are playing an increasingly important role in the field of wilderness rescue by virtue of their flexibility. This paper proposes a fusion of UAV vision technology and satellite image analysis technology for active wildfires detection and road networks extraction of wildfire areas and real-time dynamic escape route planning for people in distress. Firstly, the fire source location and the segmentation of smoke and flames are targeted based on Sentinel 2 satellite imagery. Secondly, the road segmentation and the road condition assessment are performed by D-linkNet and NDVI values in the central area of the fire source by UAV. Finally, the dynamic optimal route planning for humans in real time is performed by the weighted A* algorithm in the road network with the dynamic fire spread model. Taking the Chongqing wildfire on August 24, 2022, as a case study, the results demonstrate that the dynamic escape route planning algorithm can provide an optimal real-time navigation path for humans in the presence of fire through the information fusion of UAVs and satellites.

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Data-Driven Modelling and Model Predictive control Design of a Nonlinear Auger-Based Pyrolysis Reactor

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SSRN

SSRN 2024年

作者： Adiprabowo, Arya Bhaskara Muharto, Bambang Prabowo, Wargiantoro Saputro, Frendy Rian Anggoro, Trisno Masfuri, Imron Sugeng, Dhani Avianto Rosyadi, Erlan Lang, Zi-Qiang KST BJ Habibie Energy Building 620-625 Indonesia Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom Department of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7522 NB Netherlands Vehicle System Engineering Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia Department of Chemical and Environmental Engineering Malaysia Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia

Pyrolysis is a prominent technique to harvest energy while addressing the global waste problem. Product yield relies heavily on precise reactor temperature control in a pyrolysis process;thus, a representative mathematical model is necessary to design and evaluate the control system. Hence, this research aims to create a data-driven model of a 1 kg/h Auger-based pyrolysis (APR) plant and design a robust controller accordingly. The system is excited by using a pseudo-random binary signal (PRBS) to trigger process response and is recorded for 2 hours. Various models with different orders, such as Auto Regressive Exogeneous (ARX), Auto Regressive Moving Average Exogeneous (ARMAX), Box-Jenkins (BJ), and Output Error (OE) were created from the obtained data. The models were examined using best-fit percentage, final prediction error (FPE) and mean square error (MSE) criteria and validated using cross-correlation and auto-correlation functions. Two widely used and purpose-fit controllers, Proportional-Integral-Derivate (PID) and Model Predictive control (MPC), with output constraints, were designed and tuned for this application. Several operational scenarios were investigated, such as setpoint tracking and disturbance rejection. The best-fit models were selected and validated for TIC-3, TIC-4 and TIC-5, with 96.22%, 97.4% and 84.83% fitness criteria, respectively. Both controllers successfully satisfied all the tests with less than 2% steady-state error. However, MPC surpassed PID controllers in all aspects, such as 5-7 times faster settling time, negligible overshoot, and reduced integral absolute error (IAE) and integral square error (ISE) up to 44.75% and 5.45%, respectively. Nevertheless, MPC with input and output constraints performance pales compared to other controllers despite some adjustments with massive ISE and settling time above 3000 seconds. As a result, a recommendation to change the input actuator will be beneficial to unleash the benefit of MPC fully. © 2

关键词： Model predictive control

Aluminum Nitride Ring Resonator-Coupled Spin Defects in Hexagonal Boron Nitride for Integrated Quantum Photonics

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Aluminum Nitride Ring Resonator-Coupled Spin Defects in Hexa...

Conference on Lasers and Electro-Optics (CLEO)

作者： Henry C. Roberts Aadil Waseem Scott A. Wicker Hamza Abudayyeh Dong Seob Kim Zhida Liu Steven Randolph Xiaoqin Li Xiuling Li Department of Electrical and Computer Engineering Microelectronics Research Center The University of Texas at Austin Austin TX USA Department of Physics and Center for Complex Quantum Systems Center for Dynamics and Control of Materials and Texas Materials Institute The University of Texas at Austin Austin TX USA Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN USA

We report a new approach to generate waveguide-coupled emission from deterministically implanted boron vacancy spin defects in hBN using single-crystal AlN-on-sapphire ring resonators. This facilitates the eventual de... 详细信息

ISBN: (纸本)9798350369311

关键词： Boron Waveguide lasers Ring lasers Optical ring resonators Electro-optical waveguides Photonics Aluminum nitride

Minimum jointly structural input and output selection for strongly connected networks

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arXiv 2021年

作者： Ramos, Guilherme Pedro Aguiar, A. Pequito, Sérgio Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Portugal Delft University of Technology Delft Center for Systems and Control Netherlands

In this paper, given a linear time-invariant strongly connected network, we study the problem of determining the minimum number of state variables that need to be simultaneously actuated and measured to ensure structural controllability and observability, respectively. This problem is fundamental in the design of multi-agent systems, where there are economic constraints in the decision of which agents to equip with a more costly on-board system that will allow the agent to have both actuation and sensing capabilities. Despite the combinatorial nature of this problem, we present a solution that couples the design of both structural controllability and structural observability counterparts to address it with polynomial-time complexity. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

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arXiv 2025年

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C.L. Philip The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Department of New Network Pengcheng Laboratory Shenzhen518000 China The China Tobacco Guangdong Industrial Corporation Guangzhou510385 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenMix, a data augmentation technique specifically designed for semi-supervised semantic segmentation with Vision Transformers. TokenMix aligns well with the global attention mechanism by mixing images at the token level, enhancing learning capability for contextual information among image patches. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations. Copyright © 2025, The Authors. All rights reserved.

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