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arXiv 2022年

作者： Lian, Yingzhao Jiang, Yuning Opila, Daniel F. Jones, Colin N. The Automatic Control Lab EPFL Switzerland The United States Naval Academy Electrical and Computer Engineering Department United States

Nonlinear model predictive control has been widely adopted to manipulate bilinear systems with dynamics that include products of the inputs and the states. These systems are ubiquitous in chemical processes, mechanical systems, and quantum physics, to name a few. Running a bilinear MPC controller in real time requires solving a non-convex optimization problem within a limited sampling time. This paper proposes a novel parallel proximal-point Lagrangian based bilinear MPC solver via an interlacing horizon-splitting scheme. The resulting algorithm converts the non-convex MPC control problem into a set of parallelizable small-scale multi-parametric quadratic programs (mpQPs) and an equality-constrained linear-quadratic regulator problem. As a result, the solutions of mpQPs can be pre-computed offline to enable efficient online computation. The proposed algorithm is validated on a simulation of an HVAC system control. It is deployed on a TI LaunchPad XL F28379D microcontroller to execute speed control on a field-controlled DC motor, where the MPC updates at 10 ms and solves the problem in 1.764 ms on average and at most 2.088 ms. © 2022, CC BY-NC-ND.

关键词： Model predictive control

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Optimization of Neural Networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture

Optimization of Neural Networks using Swarm Intelligence Tec...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： M. Karthick Raja Makhbuba Shermatova Mohammed Saleh Al Ansari Shokhjakhon Abdufattokhov Vuda Sreenivasa Rao I. Infant Raj Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Civil Engineering and Architecture Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Automatic Control and Computer Engineering Dept Turin Polytechnic University Dept of Information Technologies Tashkent Intl. University of Education Tashkent Uzbekistan Department of Computer Science and Engineering Koneru Lakshmaiah Education Fou. Green Fields Vaddeswaram A.P India Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches.

关键词： Training Energy consumption Smart buildings Architecture Heuristic algorithms Neural networks Energy efficiency

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On the existence and uniqueness of equilibria in meshed DC microgrids with CPLs

On the existence and uniqueness of equilibria in meshed DC m...

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Mediterranean Conference on control and Automation (MED)

作者： Andrei-Constantin Braitor George C. Konstantopoulos Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield U.K Department of Electrical and Computer Engineering University of Patras Rion Greece

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

In this paper, we analyse the existence and uniqueness of equilibria of constant power loads (CPLs) in meshed DC microgrid architectures. Given the CPLs’ nonlinear characteristic and negative impedance behaviour, they are commonly known to introduce a destabilising effect into the system, effect intuitively coined as negative impedance instability. In the present approach, we start by deriving the characteristic polynomial from the power balance equation aiming to observe the nature of the CPLs voltage solutions, and assess their feasibility. Then, the algebraic expression is transformed into a problem of existence and uniqueness of a fixed point, and further tested by means of contraction mapping theory. A sufficient condition for the sources’ voltage references is obtained to guarantee the existence and uniqueness of equilibria. This provides a useful guidance in selecting the voltage references in the control design process. A numerical investigation on a meshed DC microgrid is carried out to verify the acquired sufficient condition and its underlying developed theory.

关键词： Sufficient conditions control design Systems operation Process control Microgrids High-voltage techniques Stability analysis

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Microservice Deployment for Satellite Edge AI Inference via Deep Reinforcement Learning

Microservice Deployment for Satellite Edge AI Inference via ...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Zhiyong Yu Hei Victor Cheng Zhanpeng Yang Xin Liu Yuning Jiang Yong Zhou Yuanming Shi School of Information Science and Technology ShanghaiTech University Shanghai China Department of Electrical and Computer Engineering Aarhus University Denmark Automatic Control Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

Artificial intelligence (AI) is critical in evolving 5G and developing 6G networks, running on edge devices, and solving resource management challenges. The burgeoning number of edge devices draws attention to the potential of low-earth orbit (LEO) satellite networks with their onboard computing capabilities for edge inference. This paper explores LEO scenarios where multiple remote sensing edge AI inference tasks concurrently process data from a single source. However, due to there being parts with the same functions between different AI applications, traditional monolithic edge AI architecture must be deployed repeatedly and falls short in efficiently harnessing the heterogeneous resources of LEO satellite networks. To solve this problem, we utilize the microservice architecture to decouple a single AI application into several independent microservices to reuse these same functions. However, due to the high latency caused by multiple microservices’ communication, we need to design a deployment strategy to fully utilize resources to reduce the service latency. We present a microservice deployment model to minimize the total service latency across all AI applications and meet resource constraints with the constraints of hardware, energy, and memory limitations. This latency optimization problem is rewritten as a Markov decision process (MDP) to effectively deal with the challenge posed by the time-varying transmission rate caused by satellite mobility. To increase the training data utilization, we employ a Proximal Policy Optimization (PPO) based reinforcement learning algorithm to meet the dynamic environment challenge. Finally, we obtain a sub-optimal solution with minimal accuracy loss and an acceptable solution time.

关键词： Satellites Accuracy Heuristic algorithms Satellite broadcasting Microservice architectures Low earth orbit satellites Edge AI computer architecture Resource management Optimization

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A markerless high resolution structural health monitoring framework for smart cities

A markerless high resolution structural health monitoring fr...

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2021 IEEE Technology and engineering Management Conference - Europe, TEMSCON-EUR 2021

作者： Chan, Christopher Chun Ki Chen, Chih-Cheng Delaney, Steven Ferworn, Alexander Chaoyang University of Technology Department of Information Management Taichung Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan Ryerson University Department of Computer Science Toronto Canada

ISBN: (纸本)9781665440912

Our paper introduces a novel structural health monitoring (SHM) framework for preexisting surveillance camera video footage towards an automated structural engineering e-governance system in a smart city. We test our framework on a sample pole structure using a high-resolution camera and an off-the-shelf phone camera. A preliminary study suggests the efficacy of using the framework in monitoring noticeable degradation and aging in large structures in periodically captured images. Our framework is dissimilar to computer vision techniques in which deformation patterns are recognized;instead, our framework is purposed as a long term observation application in which large structures in public video surveillance footage is monitored for changes that may suggest signs of aging or degradation of a structure over a long period of time. We posit that this novel framework, with emerging technology and innovation, can pave the way to combine artificial intelligence and smart structural health monitoring techniques in a widespread, unprecedented way of ensuring safe public structures in smart cities. © 2021 IEEE.

关键词： Smart city

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Examining the Effects of STATCOM and MBPSS on the Integration of High-Level Wind Power into the Power Grid Under Fault Conditions

Examining the Effects of STATCOM and MBPSS on the Integratio...

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International Conference on Systems and control (ICSC)

作者： Bilel Dhouib Moncef Triki Mohamed Ali Zdiri Hsan Hadj Abdallah Electrical engineering department Sfax engineering school Control & energy management laboratory Sfax engineering school Sfax Tunisia Electrical Engineering Department Sfax laboratory of sciences and techniques of automatic & computer engineering (Lab-STA) Sfax engineering school Sfax Tunisia Electrical engineering department Sfax engineering school control & energy management laboratory Sfax engineering school Sfax Tunisia

With the increasing need for sustainable energy sources, wind power has gained prominence as a clean and renewable option. Nevertheless, incorporating wind power into the electrical grid at a significant scale can have a considerable impact on the dynamic behavior of the power system, leading to heightened operational uncertainties. The expanding requirement for power network connection to wind farms has led to the occurrence of rotor angle fluctuations and frequency instability. This paper utilizes a Static Synchronous Compensator (STATCOM) along with a multi-band power system stabilizer (MB-PSS) to assess the transient stability of a power grid. To demonstrate the system's efficiency under fault conditions, such as triple line-to-ground faults (LLLG), a nine-bus, three-machine test network incorporating a Doubly Fed Induction Generator (DFIG) has been employed. The system's performance has been evaluated through MATLAB/Simulink simulations, which reveal that the combination of STATCOM with MB-PSS produces superior results in enhancing transient stability when compared to the scenarios of using STATCOM alone or not using it at all.

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On-Device Learning with Raspberry Pi for GCN-Based Epilepsy EEG Classification

On-Device Learning with Raspberry Pi for GCN-Based Epilepsy ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhuoli He Chuansheng Wang Jiayan Huang Antoni Grau Edmundo Guerra Jiaquan Yan College of Computer and Big Data Fuzhou University Fuzhou China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain College of New Engineering Industry Putian University Putian China School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Epilepsy is a chronic brain disease characterized by recurrent and transient seizures, which is accompanied by super-synchronous abnormal discharge of electroencephalogram (EEG) signals. As a non-invasive auxiliary diagnostic technique, EEG is currently important means of seizure detection. However, due to ignoring the spatial topological relationship between electrodes, existing data-driver methods fail to fully reflect the interaction between signals. Meanwhile, their models usually be designed with a large number of redundant parameters, making it difficult to deploy to micro-embedded devices with limited-resources. In this paper, we propose a on-device learning with edge device for epilepsy EEG classification network based on GCN (oDLGCN-EEG). Specifically, to analyze the spatial relationships between various electrodes and their temporal dependencies, we design a Brain Topology Network (BTN) for the spatiotemporal dependency feature map construction. To capture the internal activity during epilepsy seizures, we design a Neural Feature Extraction Module (NFEM) for the neural activity feature map construction. Besides, we propose a pruning scheme to optimize the model, which successfully deploys the optimized oDLGCN-EEG on the embedded Raspberry Pi device for efficient and low-power consumption intelligent epilepsy classification. Experiments in comparison with state-of-the-art methods show that oDLGCN-EEG achieves the best classification accuracy and with the smallest parameter number on baseline EEG dataset. The code is available at https://***/cathnat/Epileptic_Classification.

关键词： Electrodes Accuracy Computational modeling Biological system modeling Neural activity Epilepsy Brain modeling Feature extraction Electroencephalography Spatiotemporal phenomena

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Adaptive Neuro-Fuzzy Inference Systems for Cognitive Sensor Communication in Smart Cities

Adaptive Neuro-Fuzzy Inference Systems for Cognitive Sensor ...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Rahul Pradhan Ch. Prathiba Timur Kholmukhamedov B.N. Patil S. Suma Christal Mary I Infant Raj Department of Computer Engineering and Applications GLA University Mathura India Department of CSM Vignana Bharathi Institute of Technology Ghatkesar Hyderabad India Automatic Control and Computer Engineering Department Turin Polytechnic University Tashkent Uzbekistan Department of Applied Physics Yeshwantrao Chavan College of Engineering Nagpur India Department of Information Technology Panimalar Engineering College Poonamalle Chennai India Department of CSE K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Modern urban development depends on optimizing cognitive sensor communications in smart cities, which is the focus of this investigation. In the era of expanding sensor networks, efficient data transmission is paramount for the success of smart city initiatives. The current state of sensor communication faces challenges like suboptimal adaptability, inefficient energy usage, and limited fault detection. This study proposes a solution through the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS), identifying these challenges as opportunities for improvement. The novelty lies in ANFIS’s introduction as an intelligent and adaptive solution. Its ability to learn from sensor data, adapt to changing conditions, and optimize communication parameters sets it apart in enhancing cognitive sensor communication. The research methodology involves a systematic approach, encompassing data collection, pre-processing, and ANFIS model development. The model is rigorously trained, validated, and tested for real-world effectiveness. Integration into the smart city communication system is explored, emphasizing applications in context-aware communication, energy-efficient protocols, and fault detection. Performance is evaluated using metrics like accuracy, reliability, and efficiency, demonstrating the model’s efficacy in addressing drawbacks and enhancing overall sensor communication in smart cities. The findings contribute to advancing intelligent systems for urban infrastructure and services.

关键词： Adaptation models Protocols Adaptive systems Systematics Smart cities Fault detection Computational modeling Scalability Energy efficiency Intelligent systems

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The value of formative evaluation in an education program

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International Journal of Applied Systemic Studies 2022年第4期9卷 381-388页

作者： Tsipianitis, Dimitris Mandellos, Georgios Systems and Automatic Control Division Electrical and Computer Engineering Department University of Patras RionGR-26504 Greece Telecommunications and Information Technology Division Electrical and Computer Engineering Department University of Patras RionGR-26504 Greece

Evaluation is a process that compares the results with the target, an initial situation with the final one. ‘Evaluate’ means making an assessment based on some measure or information and referring to some criteria. The Formative evaluation is a process which is used by trainers and trainees and provides feedback on updating current teaching and learning methods in order to improve trainer’s achievement in relation of teaching goals. In this case study a research was carried out using the technique of formative evaluation in order to evaluate two thematic areas with the cooperation of the program’s leaders. The aim of the study was to extract conclusions and opinions about the program’s value, the achievement goals and the organisation implementation requirements and how all this results will been feed backed to program managers and policymakers with the appropriate information which are necessary to adopt appropriate corrective interventions for programs quality improvement. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Quality control

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Discrete-Time Stress Matrix-Based Formation control of General Linear Multi-Agent Systems

arXiv

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arXiv 2024年

作者： Onuoha, Okechi Kurawa, Suleiman Tang, Zezhi Dong, Yi School of Computing Engineering and Intelligent Systems Ulster University United Kingdom School of Electrical and Electronic Engineering The University of Manchester United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom School of Electronics & Computer Science University of Southampton United Kingdom

This paper considers the distributed leader-follower stress-matrix-based affine formation control problem of discrete-time linear multi-agent systems with static and dynamic leaders. In leader-follower multi-agent formation control, the aim is to drive a set of agents comprising leaders and followers to form any desired geometric pattern and simultaneously execute any required manoeuvre by controlling only a few agents denoted as leaders. Existing works in literature are mostly limited to the cases where the agents’ inter-agent communications are either in the continuous-time settings or the sampled-data cases where the leaders are constrained to constant (or zero) velocities or accelerations. Here, we relax these constraints and study the discrete-time cases where the leaders can have stationary or time-varying velocities. We propose control laws in the study of different situations and provide some sufficient conditions to guarantee the overall system stability. Simulation study is used to demonstrate the efficacy of our proposed control laws. © 2024, CC BY.

关键词： Linear systems

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