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A MATLAB virtual laboratory to support learning of auto-tuning PID approaches

IFAC-PapersOnLine 2024年第7期58卷 73-78页

作者： J.A. Rossiter A. Visioli S. Dormido R. Bars Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 3JD UK Dipartimento di Ingegneria Meccanica e Industiale University of Brescia Italy Universidad Nacional de Educacion a Distancia Madrid Spain Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary

This paper presents a small number of MATLAB APPs and livescript files designed to help students both understand and implement PID tuning. The paper presents the thinking behind the use of MATLAB and the topic itself before then describing the proposed resources in detail. The resources split into files with detailed mathematical and coding background students can use for self-study and assignments, and a virtual laboratory which is more intuitive and interactive and useful for familiarisation with core concepts. The files were recently added to the control101 toolbox (Rossiter, 2023).

关键词： control101 toolbox PID compensation virtual laboratories independent learning

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Fuzzy Logic control of an Artificial Neural Network-Based Floating Offshore Wind Turbine Model Integrated with Four Oscillating Water Columns

SSRN

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SSRN 2022年

作者： Ahmad, Irfan M'zoughi, Fares Aboutalebi, Payam Garrido, Izaskun Garrido, Aitor J. Automatic Control Group—ACG Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Faculty of Engineering of Bilbao University of the Basque Country—UPV/EHU Po Rafael Moreno no3 Bilbao48013 Spain

The innovative hybrid offshore platform concept, which combines Floating Offshore Wind Turbines (FOWT) with Oscillating Water Columns (OWCs), has proven to be a promising solution to harvest clean energy. However, the dynamics of the tower and barge platform are extremely sensitive to wind speed and wave elevation, which causes substantial destabilization in extreme conditions, particularly to the tower-top-displacement and platform pitch angle. Therefore, the aim of this article is to focus on two main novel targets: (i) regressive modeling of the hybrid aero-hydro-elasto-servo-mooring coupled numerical system and (ii) an ad-hoc fuzzy-based control implementation for the stabilization of the platform. To analyze the performance of the hybrid FOWT, this article first employs computational Machine Learning (ML) techniques, i.e., Artificial Neural Networks (ANNs), to match the behavior of the detailed FOWT-OWCs numerical model. Then, FLC is developed and applied to establish a structural controller mitigating the undesired vibrations. Both modeling and control schemes are successfully implemented showing a superior performance when compared to the standard barge-based FOWT system. Experimental results demonstrate that the proposed ANN-based modeling is a promising alternative to other intricate non-linear NREL 5MW FOWT dynamical models. Meanwhile, the proposed FLC improves the platform's dynamic behavior with increasing its stability. © 2022, The Authors. All rights reserved.

关键词： Wind

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

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

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International Conference on systems and control (ICSC)

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

ISBN: (数字)9798331531812

ISBN: (纸本)9798331531829

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.

关键词： Target tracking Simulation Accelerated aging control systems Formation control Stability analysis Stress Time-varying systems Multi-agent systems

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Awakened at CheckThat! 2022: Fake News Detection using BiLSTM and Sentence Transformer

Awakened at CheckThat! 2022: Fake News Detection using BiLST...

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2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Truică, Ciprian-Octavian Apostol, Elena-Simona Paschke, Adrian Department of Information Technology Uppsala University Lägerhyddsvägen 1 Uppsala75105 Sweden Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Splaiul Independentei 313 Bucharest060042 Romania Fraunhofer Institute for Open Communication Systems Berlin10589 Germany

In recent years, online social networks and online news venues have become some of the main news and event-related information spreading mediums. Although using these mediums has facilitated the speed of accessing information, it also created a new phenomenon used for propaganda and disinformation: fake news. As fake news has detrimental consequences to society, new technologies need to be developed in order to stop their harmful effects. In this paper, we propose two Bidirectional Long Short-Term Memory (BiLSTM) architectures with sentence transformers to solve two tasks: (1) a multi-class mono-lingual fake news detection task (i.e., mono-lingual task);and (2) a multi-class cross-lingual fake news detection task (i.e., cross-lingual task). For the mono-lingual task, we train and test a BiLSTM with BART sentence transformers model on an English dataset and obtain an accuracy of ∼ 0.53 and an F1-Score of ∼ 0.32. For the cross-lingual task, we train a BiLSTM with XLM sentence transformers model on an English dataset and test the model using transfer learning on a German dataset. For this task, we obtain an accuracy of ∼ 0.28 and an F1-Score of ∼ 0.19. © 2022 Copyright for this paper by its authors.

关键词： Statistical tests

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Research to Improve the Quality of Servo Motor controller

Research to Improve the Quality of Servo Motor Controller

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IEEE International Conference on control in Technical systems (CTS)

作者： Hoang Thi Thu Giang Phan Thanh Chung Tran Thuy Van Nguyen Duy Khanh Faculty of Electrical Engineering HaNoi University of Industry Hanoi Vietnam Department for Standards Metrology and Quality Military Institute of Mechanical Engineering Hanoi Vietnam Department of Automatic Control Systems Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

To overcome the disadvantages that traditional controllers bring, in this study, it is proposed to use fractional order controller $\mathrm{PI}^{\lambda}$ instead of conventional PI controller, the research also focuses on improving the quality of the controller. the order controller itself by selecting the computational methods, the discretization methods, and the implementation methods that give the best results. The study conducted simulations and experiments and compared the results with conventional PI controllers, in order to demonstrate the effectiveness of the fractional order controller.

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Vision-based Runway Detection and Landing for Unmanned Aerial Vehicle Enhanced Autonomy

Vision-based Runway Detection and Landing for Unmanned Aeria...

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IEEE International Conference on Mechatronics and Automation

作者： Kyriacos Tsapparellas Nickolay Jelev Jonathon Waters Sabine Brunswicker Lyudmila S Mihaylova Department of Automatic Control and Systems Engineering University of Sheffield UK Windracers Distributed Avionics Healthaid House Malborough Hill London UK Research Center for Digital Innovation Purdue University USA

Introducing autonomy is a task of paramount importance and is currently investigated in many areas, especially for autonomous cars and Unmanned Aerial Vehicles (UAVs). Most UAVs are still remotely human-controlled. A necessity is to implement on-board solutions, able to work in all weather conditions and at any time. Hence, on this topic, we give an overview of recent advances for vision-based landing of UAVs. A thorough classification of the main recently developed methods is introduced with a discussion of their advantages and disadvantages. The paper presents a new solution for autonomous UAV vision-based landing, focusing on runway detection using a hybrid approach combining multi-image matching, SIFT and object tracking. The results are evaluated and validated using simulated images sampled with the X-Plane 11 flight simulator and real-world videos collected during automated flights performed by the ULTRA vehicle, one of the biggest UAVs in the UK [1]. The statistical analysis from the validation of the proposed approach shows a high level of accuracy around 94.89% in clear weather conditions and real-time computational performance.

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Roblets: Robotic Tablets That Self-Assemble and Self-Fold into a Robot

Roblets: Robotic Tablets That Self-Assemble and Self-Fold in...

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

作者： Junyi Han Daniela Rus Shuhei Miyashita Automatic Control and Systems Engineering Department University of Sheffield Sheffield UK Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Insigneo Institute for in silico Medicine

Inspired by human proteins that are synthesized from only 20 types of amino acids, the development of self-assembly methods that allow robots to be built simply by randomly stirring the parts has been explored for many years. The key challenges include how to synthesize parts in pieces into a three-dimensional functional structure in a practical time, and subsequently, achieve a controlled robotic motion, all with minimal human intervention. This study proposes a method of self-assembling a 3D robot by first self-assembling random parts into a 2D structure and then self-folding it into a 3D shape. Once self-folded, the robot, whose compositional parts contain magnets, becomes capable of performing basic tasks such as block-pushing upon an application of an external magnetic field. Self-assembly from parts into a two-dimensional structure was performed by repeatedly colliding the parts with each other, and combining them with complementary-shaped parts, like matching jigsaw puzzle pieces. Self-folding was performed by shrinking a heat-responsive film attached across the hinge of each assembly part in hot water, causing the entire 2D structure to self-fold. The experiment demonstrated a series of 13 parts self-assembling into the shape of a 3D beetle, then walking and pushing an object in 13 minutes. The self-assembly process is programmed (mechanically) to generate the same geometry even if the number of parts is greater than the necessary number for the structure, thus is capable of generating multiple structures simultaneously.

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3, 2, 1, Drones Go! A Testbed to Take off UAV Swarm Intelligence for Distributed Sensing

arXiv

引用

arXiv 2022年

作者： Qin, Chuhao Candan, Fethi Mihaylova, Lyudmila Pournaras, Evangelos School of Computing University of Leeds Leeds United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

This paper introduces a testbed to study distributed sensing problems of Unmanned Aerial Vehicles (UAVs) exhibiting swarm intelligence. Several Smart City applications, such as transport and disaster response, require efficient collection of sensor data by a swarm of intelligent and cooperative UAVs. This often proves to be too complex and costly to study systematically and rigorously without compromising scale, realism and external validity. With the proposed testbed, this paper sets a stepping stone to emulate, within small laboratory spaces, large sensing areas of interest originated from empirical data and simulation models. Over this sensing map, a swarm of low-cost drones can fly allowing the study of a large spectrum of problems such as energy consumption, charging control, navigation and collision avoidance. The applicability of a decentralized multi-agent collective learning algorithm (EPOS) for UAV swarm intelligence along with the assessment of power consumption measurements provide a proof-of-concept and validate the accuracy of the proposed testbed. Copyright © 2022, The Authors. All rights reserved.

关键词： Drones

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RTLAB-Based Performance Validation of PV- Powered Single-Phase Asynchronous Motor Pumps

RTLAB-Based Performance Validation of PV- Powered Single-Pha...

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International Conference on Advanced Electrical engineering (ICAEE)

作者： Fezazi Omar Abdelkader Semmak Hasan Köten Mohammed Abd El Djalil Djehaf Ayad Ahmed Nour El Islam Biswadip Basu Mallik ICEPS Laboratory (Intelligent Control & Electrical Power Systems) Djillali Liabes University Sidi Bel-Abbes Algeria Department of Automatic Control Faculty of Electrical Engineering Djillali Liabes University Sidi Bel-Abbes Algeria Mechanical Engineering Department Mechanical Engineering Department Engineering Faculty Istanbul Medeniyet University North Campus Uskudar Istanbul TURKEY Kasdi merbah university Ouargla Algeria Department of Basic Science and Humanities Institute of Engineering & Management University of Engineering & Management Kolkata West Bengal India

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349368

In this paper, a comprehensive investigation of the performance evaluation and validation of a single-phase asynchronous motor pump system coupled with a photovoltaic (PV) power source is presented. The purpose of this study was to evaluate the practicality and effectiveness of this configuration for pumping applications. The RTLAB platform, which enables real-time simulation and emulation of dynamic systems, includes important components, such as the use of a single-phase asynchronous motor as the pump's power source, the incorporation of a PV system to supply renewable energy, and the use of RTLAB for real-time validation. Through rigorous simulation and experimentation, the motor response performance, such as speed, torque, and current response, was analyzed under varying solar irradiance and load conditions. In addition, the PV power and DC bus voltage were supervised after motor driving. This research fills the knowledge gap between the theoretical analysis and real-world applications of single-phase asynchronous motor pump systems combined with PV sources. This approach, which validates the results using RTLAB, provides a validation method to evaluate the performance and guide design choices for effective and environmentally friendly pumping solutions. Keywords- Single-phase asynchronous motors, PV, RTLAB validation, SimPower System MATLAB.

关键词： Maximum power point trackers Solar irradiance Renewable energy sources Radiation effects Torque systems operation Motors Real-time systems Stability analysis Robustness

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Multi-Target Tracking Using a Swarm of UAVs by Q-learning Algorithm

Multi-Target Tracking Using a Swarm of UAVs by Q-learning Al...

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Sensor Signal Processing for Defence (SSPD)

作者： Seyed Ahmad Soleymani Shidrokh Goudarzi Xingchi Liu Lyudmila Mihaylova Wenwu Wang Pei Xiao Centre for Vision Speech and Signal Processing (CVSSP) The University of Surrey UK School of Computing and Engineering University of West London UK Department of Automatic Control and Systems Engineering The University of Sheffield UK Institute for Communication Systems (5GIC) The University of Surrey UK

This paper proposes a scheme for multiple un-manned aerial vehicles (UAVs) to track multiple targets in challenging 3-D environments while avoiding obstacle collisions. The scheme relies on Received-Signal-Strength-Indicator (RSSI) measurements to estimate and track target positions and uses a Q-Learning (QL) algorithm to enhance the intelligence of UAVs for autonomous navigation and obstacle avoidance. Considering the limitation of UAVs in their power and computing capacity, a global reward function is used to determine the optimal actions for the joint control of energy consumption, computation time, and tracking accuracy. Extensive simulations demonstrate the effectiveness of the proposed scheme, achieving accurate and efficient target tracking with low energy consumption.

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