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Performance Comparison of Kalman Filter and Extended Kalman Filter for Human Tracking and Prediction with Particle Swarm Optimisation 1

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Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2022

作者： Ajasa, Abiodun Afis Wahyudi, Nawawi Sophan Department of Control and Mechatronics Engineering School of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia Department of Electronics and Computer Engineering Lagos State University Epe Campus Lagos Nigeria

ISBN: (数字)9789811987038

ISBN: (纸本)9789811987021

In the past two decades, object tracking has progressively advanced in computer vision and image processing. Tracking is a collection of algorithms that detect and track objects in a video sequence. This has resulted in a broad variety of applications, including surveillance, biometric identification or biological imaging, human-machine interactions, traffic control, and intelligent vehicles, all of which have benefited from tracking applications. This paper presents an IoT-based human tracking system that employs the Kalman filter (KF) and extended Kalman filter (EKF) algorithms. However, the performance of the filters is impacted by noise. Therefore, instead of manually tuning the KF and EKF’s process noise and measurement error, particle swarm optimisation (PSO) is utilised to optimise them. Four 2-dimensional models (namely conventional KF and EKF, optimised KF-PSO, and EKF-PSO) were developed and evaluated using ten distinct human sets of data containing 100 samples each. The object’s tracked positions are estimated in horizontal and vertical directions. Accuracy analysis was used to compare the four models’ quality performance. With an average mean square error of 5.99 mm (or 0.599%), the EKF-PSO model outperformed the conventional EKF model, which had an error of 7.18 mm (or 0.718%). A 17.2 mm (or 1.72%) error was found in the KF-PSO model. The last one is the traditional KF model, yielding an error of 22.4 mm (or 2.24%). As a result of its higher accuracy, the EKF-PSO model outperforms the other three models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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An adaptive observer-based SOC estimation method for lithium-ion batteries against sensor measurement uncertainties 38

An adaptive observer-based SOC estimation method for lithium...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Xu, Xiaoyi Luo, Hao Chow, Mo-Yuen Huang, Cong-Sheng Jiang, Yuchen Harbin Institute of Technology Department of Control Science and Engineering Harbin China North Carolina State University Department of Electrical and Computer Engineering RaleighNC United States

ISBN: (纸本)9798350303636

Accurate estimation of battery state of charge (SOC) is critical for efficient and safe battery applications. The measurement uncertainties of sensors, including measurement noises and sensor bias will affect the estimation accuracy inevitably. Therefore, quantifying the relationship between the sensor measurement uncertainties and the SOC estimation errors and seeking better SOC estimation methods has been a hot topic of research. In this paper, model errors and SOC estimation errors under measurement noise and sensor bias are derived. The resulting analyses can be used to assess the robustness of the SOC estimates. In addition, an adaptive observer-based SOC estimation method for lithium-ion batteries is proposed to cope with the measurement uncertainty. Simulation experiments demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： adaptive observer lithium-ion battery sensor bias sensor noises State-of-Charge (SOC) estimation

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Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

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Reference Model Design in Multiple Timescale Adaptive control

Reference Model Design in Multiple Timescale Adaptive Contro...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Eves, Kameron Valasek, John Electrical and Computer Engineering Engineering Department Utah Tech University Saint GeorgeUT84770 United States Vehicle Systems and Control Laboratory Aerospace Engineering Department Texas A and M University College StationTX77843-3141 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The method [K]control of Adaptive Multiple-timescale Systems (KAMS) has been used as a method of adaptive control for systems with states that evolve at vastly different rates and with uncertain parameters. Prior research on KAMS has shown a relationship between the asymptotic behavior of the reference models and the behavior of the full-order closed-loop system. Poor reference model design can cause steady-state error or instability even if that reference model is acceptable in a single timescale context, due to adverse feedback between the reference models and the fast-state manifold. This paper simplifies KAMS control design and provides insight into multiple timescale reference models by identifying design considerations for the fast and slow reference models. For fast reference models, it is shown that the stability proofs used in KAMS control design necessarily require the manifold to be an asymptotically stable equilibrium of the fast reference model, despite prior work that allowed other possibilities. In the case of a slow reference model that is not asymptotically stable to its reference signal, it is common for the inner connection conditions to be dependent on the reference signal. This can cause the interconnection conditions to be ill-formatted for the application of the stability proofs used in KAMS control design. This paper addresses that case and proposes the use of Raffoul’s condition as a method to prove stability in the sense of Lyapunov. A linear quadrotor attitude stabilization problem is used as a framework for the slow reference model analysis. Results presented in the paper show that 1) the asymptotic stability of the fast reference model is a necessary condition for the KAMS stability proofs used in prior work and 2) Raffoul’s condition is a sufficient condition for Lyapunov sense stability even when the inner connection conditions are ill-formatted for the KAMS stability proofs used in prior work. © 2025, American Institute of Aeronautics

关键词： Model reference adaptive control

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Machine Learning based Design of Energy Management System using Non-Intrusive Load Monitoring Strategy

Machine Learning based Design of Energy Management System us...

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2024 International Conference on control, Automation and Diagnosis, ICCAD 2024

作者： Shahid, Kamal Gulzar, Muhammad Majid Raza, Muhammad Ali Habib, Salman Ahmad, Hasnain University of The Punjab Institute of Electrical Electronics and Computer Engineering Lahore Pakistan King Fahd University of Petroleum & Minerals Department of Control & Instrumentation Engineering Dhahran31261 Saudi Arabia Dhahran31261 Saudi Arabia Dhahran31261 Saudi Arabia Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9798350361025

In this work, a Non-Intrusive Load Monitoring (NILM) system is designed for smart homes based on smart energy meters. The proposed solution simplifies the monitoring process by using a single set of sensors, in contrast to conventional systems that call for several sensors. From different appliances and their combinations, crucial electrical information, such as voltage, current, active power, reactive power, apparent power, power factor, and frequency was gathered. Proposed intelligent data analysis employing machine learning methods, particularly Artificial Neural Networks (ANN) and Deep Neural Networks (DNN), is the key innovation. These methods accurately categorize various appliance kinds, with DNN performing best in real-time circumstances. Additionally, there is a built-in email alert system that activates whenever there are odd electrical surges. The streamlined user interface, offers accurate identification of individual appliances and combinations in real-time forecasts based on past data. Future developments include a larger appliance data-set, improved categorization methods, load forecasting, and the creation of a smartphone app for real-time energy consumption data and automatic control, among other things. In a net-shell, this study offers an effective, single-sensorbased solution for load monitoring in smart homes, representing a substantial development in NILM technology. This technology claims to improve resource utilization and energy management in both domestic and commercial settings. © 2024 IEEE.

关键词： Deep neural networks

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Hamiltonian learning using machine-learning models trained with continuous measurements

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Physical Review Applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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A Comparative Evaluation of GA and PSO Tuned PID controllers for the Quadruple Tank System

A Comparative Evaluation of GA and PSO Tuned PID Controllers...

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2024 International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2024

作者： Hailu, Gashaye L. Yetayew, Tefera T. Bahir Dar University Electrical and Computer Engineering Faculty Bahir Dar Institute of Technology Bahir Dar Ethiopia Adama Science and Technology University Department of Power and Control Engineering Adama Ethiopia

ISBN: (纸本)9798331527792

In this research, nature inspired metaheuristic optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Techniques are formulated to tune optimal combinations of PID controller parameters for a quadruple tank level control application. The degree of relation of the best paring for both interacted control loops are justified by using Relative Gain Array (RGA) calculation. The overall system has been modeled and implemented in MATLAB/Simulink and performance evaluations has been done for time domain performance specifications of settling time, rise time, percentage overshoot and steady state error as comparison criterions of controllers. Accordingly, for the minimum phase case, GA tuned PID has settling time of 0.36sec and 0.31second for the tank 1 and tank 2 respectively. And PSO tuned PID controller has settling time of 0.96sec and 0.35sec for tank 1and tank 2 respectively. The quantitative and qualitative analysis of results reveals that there is fast dynamic response using GA PID relative to PSO PID even though the difference is not significant that may be due to stochastic feature of the metahueristic optimization algorithms. In summary, the optimal combination of PID controller algorithms' parameters can give robust performance compared to PID controller designed using the classical approaches or manual tuning techniques. © 2024 IEEE.

关键词： Genetic algorithms

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Intelligent Execution of computer Vision Tasks in Delay-Constrained UAV-Aided Networks

Intelligent Execution of Computer Vision Tasks in Delay-Cons...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Varshney, Nancy Puligheddu, Corrado Chiasserini, Carla Fabiana Casetti, Claudio De, Swades Indian Institute of Technology Department of Electrical Engineering Delhi India Department of Electronics and Telecommunications Politecnico di Torino Italy Department of Control and Computer Engineering Politecnico di Torino Italy

ISBN: (纸本)9798350370218

Autonomous unmanned aerial vehicles (UAVs) are crucial in critical target tracking and disaster management services. However, challenges arise due to limited channel capacity causing large transmission delays and constraints imposed by UAV batteries when running computationally intensive object detection and tracking algorithms. To address this, we propose in-telligent offloading computer vision tasks to a high-computational edge server at millimeter wave (mmWave) frequency. Transmission at mmWave needs large transmission power and is susceptible to blockages that necessitate considering the link quality in the offloading policy. Additionally, the timely processing of frames containing objects of interest is essential for context-based UAV operations to achieve a low frame drop rate. In this work, we present a delayed-reward reinforcement learning framework to determine the offloading policy of computer vision tasks in a delay-constrained environment. This approach considers the importance of the content within frames, which is unknown to the UAV. The objective is to jointly reduce UAV energy consumption and frame drop rates, leveraging statistical information of both the channel and the frame semantics. Through extensive simulations, we demonstrate by considering statistical information of the communication channel and frame semantics, we achieve approximately 45% energy savings compared to the UAV's energy consumption when processing all frames locally and maintaining the drop rate of delay-constrained frames below 5%. © 2023 IEEE.

关键词： Communication channel delay frame semantics offloading unmanned aerial vehicle (UAV)

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Fault-Tolerant Quantum Computation Using Large Spin-Cat Codes

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PRX Quantum 2024年第2期5卷 020355-020355页

作者： Sivaprasad Omanakuttan Vikas Buchemmavari Jonathan A. Gross Ivan H. Deutsch Milad Marvian Center for Quantum Information and Control Department of Physics and Astronomy Venice California 90291 USA Department of Electrical and Computer Engineering

We construct a fault-tolerant quantum error-correcting protocol based on a qubit encoded in a large spin qudit using a spin-cat code, analogous to the continuous-variable cat encoding. With this, we can correct the dominant error sources, namely processes that can be expressed as error operators that are linear or quadratic in the components of angular momentum. Such codes tailored to dominant error sources can exhibit superior thresholds and lower resource overheads when compared to those designed for unstructured noise models. A key component is the cnot gate that preserves the rank of spherical tensor operators. Categorizing the dominant errors as phase and amplitude errors, we demonstrate how phase errors, analogous to phase-flip errors for qubits, can be effectively corrected. Furthermore, we propose a measurement-free error-correction scheme to address amplitude errors without relying on syndrome measurements. Through an in-depth analysis of logical cnot gate errors, we establish that the fault-tolerant threshold for error correction in the spin-cat encoding surpasses that of standard qubit-based encodings. We consider a specific implementation based on neutral-atom quantum computing, with qudits encoded in the nuclear spin of 87Sr, and show how to generate the universal gate set, including the rank-preserving cnot gate, using quantum control and the Rydberg blockade. These findings pave the way for encoding a qubit in a large spin with the potential to achieve fault tolerance, high threshold, and reduced resource overhead in quantum information processing.

关键词： Quantum computation Quantum control Quantum error correction Quantum information theory

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High-performance and Multi-functional control of Transformerless Single-phase Smart Inverter for Grid-connected PV System

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Journal of Modern Power Systems and Clean Energy 2021年第6期9卷 1386-1394页

作者： Kamran Zeb Saif Ul Islam Waqar Uddin Imran Khan Muhammad Ishfaq Tiago Davi Curi Busarello H.J.Kim Department of Electrical Engineering Pusan National University School of Electrical Engineering and Computer Science National University of Sciences and Technology Department of Control Automation and Computing EngineeringFederal University of Santa Catarina Blumenau

Highly reliable and flexible control is required for distributed generation(DG) to efficiently connect to the *** inverters play a key role in the control and integration of DG into the power grid and provide advanced functionalities. In this paper, an energy-based single-phase voltage-source smart inverter(SPV-SSI) of 5 k VA is designed and analyzed in detail. SPV-SSI is capable of supplying the power to local load and the utility load up to the rated capacity of the inverter, injecting the power into the grid, storing the energy in lead-acid battery bank, controlling the voltage at the point of common coupling(PCC) during voltage sags or faults, and making decisions on real-time pricing information obtained from the utility grid through advanced metering. The complete design of smart inverter in dq frame, bi-directional DC-DC buck-boost converter, IEEE standard 1547 based islanding and recloser, and static synchronous compensator(STATCOM) functionalities is presented in this paper. Moreover, adaptive controllers, i. e., fuzzy proportional-integral(F-PI) controller and fuzzy-sliding mode controller(F-SMC) are designed. The performances of F-PI controller and F-SMC are superior, stable, and robust compared with those of conventionally tuned PI controllers for voltage control loop(islanded mode) and current control loop(grid-connected mode).

关键词： Distributed generation(DG) inverter convert-er fuzzy-sliding mode controller(F-SMC) fuzzy proportionalintegral(F-PI)controller

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