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A Nonlinear Optimal control Method for Attitude Stabilization of Micro-Satellites

Guidance, Navigation and control 2022年第3期2卷 30-67页

作者： G.Rigatos M.Abbaszadeh K.Busawon L.Dala Unit of Industrial Automation Industrial Systems Institute 26504 Rion PatrasGreece Department of ECSE Rensselaer Polytechnic Institute New York 12065 USA Nonlinear Control Group University of Northumbria Newcastle NE18ST UK Department of Mechanical Engineering University of Northumbria Newcastle NE18ST UK

Attitude control and stabilization of micro-satellites is a nontrivial problem due to the highly nonlinear and multivariable structure of the satellites'state-space *** this paper,a novel nonlinear optimal(H-infinity)control approach is developed for this control *** dynamic model of the satellite's attitude dynamics undergoesfirst approximate linearization around a temporary operating point which is updated at each iteration of the control *** linearization process relies on first-order Taylor series expansion and on the computation of the Jacobian matrices of the state-space model of the satellite's attitude *** the approximately linearized description of the satellite's attitude a stabilizing H-infinity feedback controller is *** compute the controller's feedback gains,an algebraic Riccati equation is solved at each time-step of the control *** stability properties of the control scheme are proven through Lyapunov *** is also demonstrated that the control method retains the advantages of linear optimal control that is fast and accurate tracking of the reference setpoints under moderate variations of the control inputs.

关键词： Micro-satellites attitude control nonlinear optimal control H-infinity control differentialfiatness properties Taylor series expansion Jacobian matrices Lyapunov analysis global asymptotic stability

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Classifying Fault Category and Severity of UAV Flight controllers’ Reported Issues

Classifying Fault Category and Severity of UAV Flight Contro...

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International Conference on System Reliability and Science (ICSRS)

作者： Anamta Khan Maria Lucia Ferramosca Naghmeh Ivaki Henrique Madeira Department of Informatics Engineering University of Coimbra Centre for Informatics and Systems of the University of Coimbra Coimbra Portugal Department of Control and Computer Engineering Polytechnic of Turin Italy

Unmanned Ariel Vehicles (UAVs) have gained significant importance in diverse sectors. Thus, a profound safety risk analysis/assessment to prevent any possible damage to themselves, the environment, and humans is fundamental for building and utilizing UAVs. To achieve that, two fundamental challenges should be addressed: i) identification of types and frequency of the issues and ii) assessment of their impact. In this paper, we aim to address the first challenge by automatizing the process of data field analysis. To do so, we first performed some statistical analysis of the reported issues of UAV systems (in Github) and manually extracted detailed data from the reports to better understand the type and nature of the issues. Then, to automatize the analysis, we used natural language processing algorithm to extract the keywords from the reports, and then applied four machine learning algorithms to build classifier models to classify the reports according to the fault category and severity level. The good performance results obtained suggest that these analyzes can be performed to further understand the UAV system issues, and help in the risk assessment procedure to identify the hazard and define the frequency and severity of the risk. Moreover, the results of this work can help a big community of developers and researchers in the precise and fast analysis of bug reports and safety risk assessment of any software system.

关键词： Fault diagnosis Machine learning algorithms Statistical analysis Computer bugs Software systems Classification algorithms Autopilot

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Experimental Research of Modes of Operation of Local Autonomous Sources of Energy Supply with Induction Generator

Experimental Research of Modes of Operation of Local Autonom...

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International Conference on Modern Electrical and Energy systems (MEES)

作者： Iurii Zachepa Nataliia Zachepa Oksana Khrebtova Boris Kobiliansky Ilya Turaev Department of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Department of Electromechanical and Computer Systems Ukrainian Engineering Pedagogics Academy Kharkov Ukraine

ISBN: (纸本)9798350346848

The paper presents the results of experimental studies of autonomous power supply sources formed according to the “internal combustion engine-induction generator” scheme. Autonomous local sources of energy supply according to this scheme are formed from existing electromechanical and electrotechnical equipment of other functional purposes only for the period of electricity demand. Such sources of electricity are universal for welding and other technological equipment of military and civil, including agricultural, machinery. According to the results, the viability of the proposed concept of creating autonomous power supply sources based on motor vehicles and electrical equipment has been proven. The limits of guaranteed excitation and subsequent trouble-free operation of autonomous local sources of power supply with power to consumers of various categories have been established.

关键词： Workability Induction motors Power supplies Welding Induction generators Generators Combustion

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Exact Representation and Efficient Approximations of Linear Model Predictive control Laws Via Hardtanh Type Deep Neural Networks

SSRN

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SSRN 2023年

作者： Lupu, Daniela Necoara, Ion Department of Automatic Control and Systems Engineering University Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics Romanian Academy Bucharest050711 Romania

Deep neural networks have revolutionized many fields, including image processing, inverse problems, text mining and more recently, give very promising results in systems and control. Neural networks with hidden layers have a strong potential as an approximation framework of predictive control laws as they usually yield better approximation quality and smaller memory requirements than existing explicit (multi-parametric) approaches. In this paper, we first show that neural networks with HardTanh activation functions can exactly represent predictive control laws of linear time-invariant systems. We derive theoretical bounds on the minimum number of hidden layers and neurons that a HardTanh neural network should have to exactly represent a given predictive control law. The choice of HardTanh deep neural networks is particularly suited for linear predictive control laws as they usually require less hidden layers and neurons than deep neural networks with ReLU units for representing exactly continuous piecewise affine (or equivalently min-max) maps. In the second part of the paper we bring the physics of the model and standard optimization techniques into the architecture design, in order to eliminate the disadvantages of the black-box HardTanh learning. More specifically, we design trainable unfolded HardTanh deep architectures for learning linear predictive control laws based on two standard iterative optimization algorithms, i.e., projected gradient descent and accelerated projected gradient descent. We also study the performance of the proposed HardTanh type deep neural networks on a linear model predictive control application. © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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Development of a Software Platform for automatic Train Operation: Integrating Meso- and Microscopic Layers

SSRN

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SSRN 2024年

作者： Bilbao-Moreno, Daniel Elorza, Iker Irigoyen, Eloy IKERLAN Paseo José María Arizmendiarreta 2 Mondragon Arrasate20500 Spain Department of Automatic Control and Systems Engineering Universidad del País Vasco UPV EHU Pl. Ingeniero Torres Quevedo Bilbao48013 Spain

This article presents a comprehensive study on the development and integration of the mesoscopic and microscopic layers within the control architecture of railway systems, enabling autonomous operation. A key outcome of this research is developing a software platform that provides the foundation for improving autonomous train control algorithms. The mesoscopic layer, defined as the high-level component of the automatic train operation (ATO) subsystem, focuses on generating setpoints to control the traction system, which are subsequently transmitted to the low-level component of the ATO subsystem. A simplified model is employed in the mesoscopic layer to enhance planning capabilities and facilitate efficient decision-making processes, while a detailed model represents the physical system. Simulations evaluate the effectiveness of this integrated system, considering various disturbances to thoroughly assess the performance of the mesoscopic layer algorithm and the microscopic layer control system. The results confirm that by successfully integrating these layers, precise and efficient control of train motion is achieved while considering all constraints imposed by the macroscopic layer. Furthermore, this software platform is set for future development, including implementing intelligent control techniques to enhance autonomous train control. © 2024, The Authors. All rights reserved.

关键词： Decision making

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An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System

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Sensors 2024年第10期24卷 3098页

作者： Chi, Tsun-Kuang Chen, Tsung-Yi Lin, Yu-Chen Lin, Ting-Lan Zhang, Jun-Ting Lu, Cheng-Lin Chen, Shih-Lun Li, Kuo-Chen Abu, Patricia Angela R. Department of Electronic Engineering Chung Yuan Christian University Taoyuan320314 Taiwan Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Information Management Chung Yuan Christian University Taoyuan320317 Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Quezon1108 Philippines

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems. © 2024 by the authors.

关键词： Deep learning

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WARA-PS:a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation

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Autonomous Intelligent systems 2021年第1期1卷 104-134页

作者： Olov Andersson Patrick Doherty Mårten Lager Jens-Olof Lindh Linnea Persson Elin A.Topp Jesper Tordenlid Bo Wahlberg Department of Computer and Information Science Linköping UniversityLinköpingSweden Saab Kockums MalmöSweden Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholmSweden Department of Computer Science Lund UniversityLundSweden Saab Combitech AB LinköpingSweden Division of Automatic Control Department of Electrical EngineeringLinköping UniversityLinköpingSweden

A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is *** objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal *** enabler is a computational infrastructure with a core system architecture for industrial and academic *** includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial *** motivating application for the demonstration is marine search and rescue operations.A state-of-art delegation framework for the mission planning together with three specific applications is also *** first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface *** second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles,and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine *** research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test *** would be most difficult to do experiments on this large scale without the WARA-PS research ***,these demonstrator activities have resulted in effective research dissemination with high public visibility,business impact and new research collaborations between academia and industry.

关键词： Autonomous systems Intelligent system architectures Research demonstration arena Autonomous drones Autonomous marine vessels Public safety and security Collaborative robotics

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Bio-inspired control by overlapping adaptive clusters: a vehicle platoon case study

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IFAC-PapersOnLine 2023年第2期56卷 1460-1465页

作者： Ovidiu Pauca Anca Maxim José M. Maestre Constantin F. Caruntu “Gheorghe Asachi” Technical University of Iasi Department of Automatic Control and Applied Informatics Blvd. D. Mangeron 27 700050 Iasi Romania University of Seville Department of Systems and Automation Engineering Camino Descubrimientos S/N.- Isla Cartuja 41092 Seville Spain

Clustering vehicles in a platoon by exchanging information through communication networks leads to an improvement in their performance with a moderate cooperation effort. In particular, in this paper, a bio-inspired methodology for overlapping coalitions of vehicles is proposed, which optimizes communication and control performance. The results obtained using the proposed adaptive clusters method are compared with the ones obtained by a conventional coalitional control strategy, and the comparison illustrates the improved efficiency of the proposed method.

关键词： Bio-inspired control coalitional control overlapping topology vehicle platoon

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Damping Linearization of Ankle Prosthesis Based on Electro-Hydrostatic Actuation

Damping Linearization of Ankle Prosthesis Based on Electro-H...

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2022 CSAA/IET International Conference on Aircraft Utility systems, AUS 2022

作者： Dong, Dengpeng Wang, Xingjian Tian, Xinyu Wang, Shaoping Wei, Yi Gavrilov, Alexander I. School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China Bauman Moscow State Technical University Department of Automatic Control Systems Moscow105005 Russia

ISBN: (数字)9781839537769

ISBN: (纸本)9781839537769

The research object of this paper is the ankle prosthesis system based on electro-hydrostatic actuation. In the passive state, the prosthesis uses PWM wave to drive a high speed ON/OFF valve (HOV), making it equivalent to a proportional valve with a certain opening size to adjust the damping of the system and achieve a compliant control. However, due to the mechanical delay characteristics of the HOV, there are obvious nonlinear regions in the low duty cycle and high duty cycle regions, which affect the control performance of the system. This paper will discuss this problem. Using the control strategies in PWM-PFM to widen the linear region of the prosthetic system damping control, improve the system's ability to adjust the damping, and facilitate the prosthesis to better achieve active compliance control. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Linearization

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Recurrent Dissipativity-Based Inequalities for controller Design

TechRxiv

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TechRxiv 2023年

作者： Madeira, Diego de S. Machado, Gabriel F. The Electrical Engineering Department Federal University of Ceará Fortaleza60455-760 Brazil The Department of Automatic Control and Systems Engineering The University of Sheffield Amy Johnson Building Mappin Street SheffieldS1 3JD United Kingdom

Using a new set of semidefinite constraints called recurrent dissipativity-based inequalities, this letter presents an iterative procedure to design polynomial feedback control laws for polynomial nonlinear systems, let it be a static state feedback or a linear static output feedback (SOF) controller one needs to determine. In addition to that, the problem of linear SOF design for linear time-invariant (LTI) systems is solved as well. In the case of polynomial systems, we use sum-of-squares (SOS) programming for controller design and to provide an estimate of the closed-loop domain of attraction. In the case of LTI models, a set of linear matrix inequalities (LMIs) is employed in an iterative strategy to determine a stabilizing gain. Numerical simulations on a few examples borrowed from literature are provided in order to emphasize the advantages of our new dissipativity-based control (DBC) method. © 2023, CC BY-NC-SA.

关键词： Polynomials

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