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Attacking Digital Twins of Robotic systems to Compromise Security and Safety

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arXiv 2022年

作者： Carr, Christopher Wang, Shenglin Wang, Peng Han, Liangxiu Department of Mathematics and Computing Manchester Metropolitan University M15 6BH United Kingdom Department of Automatic Control and Systems Engineering The University of Sheffield SheffieldS10 2TN United Kingdom

Security and safety are of paramount importance to human-robot interaction, either for autonomous robots or human-robot collaborative manufacturing. The intertwined relationship of security and safety has imposed new challenges on the emerging digital twin systems of various types of robots. To be specific, the attack of either the cyber-physical system or the digital-twin system could cause severe consequences to the other. Particularly, the attack of a digital-twin system that is synchronised with a cyber-physical system could cause lateral damage to humans and other surrounding facilities. This paper demonstrates that for Robot Operating System (ROS) driven systems, attacks such as the person in the middle attack of the digital-twin system could eventually lead to a collapse of the cyber-physical system, whether it is an industrial robot or an autonomous mobile robot, causing unexpected consequences. We also discuss potential solutions to alleviate such attacks. Copyright © 2022, The Authors. All rights reserved.

关键词： Cyber Physical System

A Human-friendly Verbal Communication Platform for Multi-Robot systems: Design and Principles

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arXiv 2022年

作者： Carr, Christopher Wang, Peng Wang, Shenglin The Department of Mathematics and Computing Manchester Metropolitan University M15 6BH United Kingdom The Department of Automatic Control and Systems Engineering The University of Sheffield SheffieldS10 2TN United Kingdom

While multi-robot systems have been broadly researched and deployed, their success is built chiefly upon the dependency on network infrastructures, whether wired or wireless. Aiming at the first steps toward de-coupling the application of multi-robot systems from the reliance on network infrastructures, this paper proposes a human-friendly verbal communication platform for multi-robot systems, following the deliberately designed principles of being adaptable, transparent, and secure. The platform is network independent and is subsequently capable of functioning in network infrastructure lacking environments from underwater to planet explorations. A series of experiments were conducted to demonstrate the platform’s capability in multi-robot systems communication and task coordination, showing its potential in infrastructure-free applications. To benefit the community, we have made the codes open source at GitHub. Copyright © 2022, The Authors. All rights reserved.

关键词： Multipurpose robots

UAV-miniUGV Hybrid System for Hidden Area Exploration and Manipulation

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arXiv 2022年

作者： Pushp, Durgakant Kalhapure, Swapnil Das, Kaushik Liu, Lantao School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Automatic Control and Systems Engineering Department University of Sheffield Mappin St SheffieldS1 3JD United Kingdom TCS Research India

— We propose a novel hybrid system (both hardware and software) of an Unmanned Aerial Vehicle (UAV) carrying a miniature Unmanned Ground Vehicle (miniUGV) to perform a complex search and manipulation task. This system leverages heterogeneous robots to accomplish a task that cannot be done using a single robot system. It enables the UAV to explore a hidden space with a narrow opening through which the miniUGV can easily enter and escape. The hidden space is assumed to be navigable for the miniUGV. The miniUGV uses Infrared (IR) sensors and a monocular camera search for an object in the hidden space. The proposed system takes advantage of a wider field of view (fov) of the camera as well as the stochastic nature of the object detection algorithms to guide the miniUGV in the hidden space to find the object. Upon finding the object the miniUGV grabs it using visual servoing and then returns back to its start point from where the UAV retracts it back and transports the object to a safe place. In case there is no object found in the hidden space, UAV continues the aerial search. The tethered miniUGV gives the UAV an ability to act beyond its reach and perform a search and manipulation task which was not possible before for any of the robots individually. The system has a wide range of applications and we have demonstrated its feasibility through repetitive experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Antennas

Towards control of Autonomous Surface Vehicles in Rough Seas

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IFAC-PapersOnLine 2020年第2期53卷 14692-14697页

作者： Daniel R. McCullough Bryn L. Jones Oscar J.G. Villarreal J.A. Rossiter Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

This paper addresses the problem of controlling an Autonomous Surface Vehicle (ASV) in rough sea-states, with a view towards minimising wave-induced forces, whilst maintaining headway. This is a challenging control application since, and as is derived in the paper, the interaction between the vessel and the wave disturbance is nonlinear and coupled. This subsequently motivates the novel application of the Real Time Iteration Scheme (RTI) for Nonlinear Model Predictive control (NMPC) of the ASV. Analysis of the resulting control signal provides an important insight into the role of the wave encounter frequency. Specifically, by actuating at twice the average wave encounter frequency, the nonlinear controller is able to reduce the wave forces, compared to an open-loop controller that achieves the same average velocity.

关键词： Nonlinear optimal marine system control autonomous surface vehicle nonlinear model predictive control real time optimization

Model Predictive control for Wave Energy Converters: A Moving Window Blocking Approach

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IFAC-PapersOnLine 2020年第2期53卷 12815-12821页

作者： Juan Guerrero-Fernández Oscar J. González-Villarreal John Anthony Rossiter Bryn Jones Department of Automatic Control and Systems Engineering University of Sheffield UK

Ocean wave energy is one of the most concentrated sources of renewable energy. However, until now it has not reached the economic feasibility required to be commercialised. To improve the efficiency of wave energy converters, several advanced control strategies have been proposed, including Model Predictive control (MPC). Nevertheless, the computational burden of the underlying optimisation problem is a drawback of conventional (Full-Degree of Freedom, F-DoF) MPC, which typically limits its application for real-time control of systems. In this paper, a Moving Window Blocking (MWB) approach is proposed to speed-up the time required for each optimisation problem by reducing the number of decision variables using input parameterised solutions. Numerical simulation of a generic single device point absorber wave energy converter controlled by this scheme confirms the potential of this approach.

关键词： Wave energy converters Model predictive controller Moving window blocking

A Sparse Autoencoder Based Adversarial Open Set Domain Adaptation Model for Fault Diagnosis of Rotating Machinery

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A Sparse Autoencoder Based Adversarial Open Set Domain Adapt...

Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Zhao-Hua Liu Lin-Bo Jiang Hua-Liang Wei Chang-Tong Wang Ming-Yang Lv Lei Chen School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield U.K Harbin Electric Corporation Wind Power Co. Ltd Xiangtan China

Rotating machinery is an integral part of many industrial systems. Domain adaptation technique provides a powerful tool to detect faults under different working conditions. However, there is still a challenge: conventional domain adaptation approach only works under the ‘closed set’ assumption that all test classes are known at training time. In practice, a more realistic situation is ‘open set’, i.e., knowledge is incomplete in the training process, resulting in unknown classes during the testing. In this paper, a sparse autoencoder based adversarial open set domain adaptation (SAOSDA) model is proposed for rotating machinery fault diagnosis under open set scenarios, which can recognize the unknown faults and detect the known faults under different working conditions. This model utilizes adversarial learning to reduce the discrepancies between source samples and known target samples and reject the unknown target samples simultaneously. Experimental results of the actual bearing dataset verify the superiority and effectiveness of this method.

关键词： Fault diagnosis Employee welfare Training Adaptation models Data analysis Target recognition Adversarial machine learning

In-flight novelty detection with convolutional neural networks

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arXiv 2021年

作者： Hartwell, Adam Montana, Felipe Jacobs, William Kadirkamanathan, Visakan Mills, Andrew R. Clark, Tom Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom Rolls-Royce Plc United Kingdom

Gas turbine engines are complex machines that typically generate a vast amount of data, and require careful monitoring to allow for cost-effective preventative maintenance. In aerospace applications, returning all measured data to ground is prohibitively expensive, often causing useful, high value, data to be discarded. The ability to detect, prioritise, and return useful data in real-time is therefore vital. This paper proposes that system output measurements, described by a convolutional neural network model of normality, are prioritised in real-time for the attention of preventative maintenance decision makers. Due to the complexity of gas turbine engine time-varying behaviours, deriving accurate physical models is difficult, and often leads to models with low prediction accuracy and incompatibility with real-time execution. Data-driven modelling is a desirable alternative producing high accuracy, asset specific models without the need for derivation from first principles. We present a data-driven system for online detection and prioritisation of anomalous data. Biased data assessment deriving from novel operating conditions is avoided by uncertainty management integrated into the deep neural predictive model. Testing is performed on real and synthetic data, showing sensitivity to both real and synthetic faults. The system is capable of running in real-time on low-power embedded hardware and is currently in deployment on the Rolls-Royce Pearl 15 engine flight trials. © 2021, CC BY-NC-ND.

关键词： Convolution

A Special Issue:"Key Technologies of Virtual Power Plant and Distributed Energy Resources"for Global Energy Interconnection

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全球能源互联网（英文版） 2024年第2期7卷前插1-前插2页

作者： Wei Pei Xiaojun Shen Xin Zhang North China Electric Power University BeijingChina Electrical Engineering Department Shanghai Jiaotong UniversityShanghaiChina University of Sheffield United Kingdom Electrical Machines and Drives Research Group and the Department of Automatic Control and Systems Engineering UK Research and Innovation (UKRI)

The increasing penetration of renewable and distributed energy resources (DERs) is transforming the power grid into a new type of clean and low-carbon power system. However, the uncertainty and volatility of DERs have also brought severe challenges to the secure and reliable operation of the power systems. In order to successfully integrate renewable DERs, virtual power plant (VPP) has emerged as a new technique for coordinating demand-side DERs, which has drawn significant attention from industry and academia.

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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