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Model-Free Predictive control in Anaerobic Digestion Wastewater Treatment

Model-Free Predictive Control in Anaerobic Digestion Wastewa...

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2025 SICE International Symposium on control systems, SICE ISCS 2025

作者： Pratvittaya, Jiravit King Mongkut's University of Technology Thonburi Department of Control Systems and Instrumentation Engineering Bangkok Thailand

ISBN: (纸本)9784907764852

Anaerobic digestion is a wastewater treatment method that utilizes bacterial decomposition to convert organic matter into biogas, which provides both environmental and economic benefits. However, controlling biogas production in anaerobic digestion systems poses significant challenges due to the complexity and non-linearity of the digestion process. This study explores the application of model-free predictive control for anaerobic digestion systems, utilizing only inputoutput data without using mathematical models as in conventional control approaches. Previous research has demonstrated the applicability of this approach for nonlinear systems, particularly in multi-input, multi-output nonlinear systems. In this study extends the methodology to controlling in methane production of anaerobic digestion systems, evaluating its effectiveness through numerical simulations. © 2025 The Society of Instrument and control Engineers.

关键词： Anaerobic digestion

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Stabilization of Inverted Pendulum using Neural Network controller 10

Stabilization of Inverted Pendulum using Neural Network Cont...

引用

10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Kumar, Swetha R. Gunavarshini, R. Nithiyashri, S. Priyanka, G.S. Rithiga, R. Psg College of Technology Department of Instrumentation and Control Systems Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

The paper aims to develop an innovative and robust control system for stabilizing an inverted pendulum using a neural network controller. This combines principles of control theory with the power of artificial intelligence to address the complex and dynamic nature of the inverted pendulum problem. The controller used here is neural network model predictive controller. The data obtained from the plant model is used to train the neural network. Various numbers of hidden layers were given and their behaviour was analyzed and the one with the minimum error was chosen. The controller is trained to produce a result with the least amount of error by providing the reference signal and the error value as inputs. After the successful training, the neural network controller stabilizes the inverted pendulum model. © 2024 IEEE.

关键词： Inverted pendulum

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Sector-Based Incremental Clustering and Scalable Deletion for Real-Time Big Data Streaming Application 2nd

Sector-Based Incremental Clustering and Scalable Deletion fo...

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2nd International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2023

作者： Ananthi, M. Mangayarkarasi, T. Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai India

ISBN: (纸本)9783031689079

Massive streaming data is the most current technology for storing and manipulating large quantities of data. Processing the substantial amount of streaming data is still a challenging issue. The speed and throughput can be improved while extracting this huge amount of data by using a dynamic query processing technique. The sector-based incremental clustering indexing and scalable deletion approach is used in this paper to improve the system performance. Stream processing can be more effective and flexible by applying dynamic query processing technique. Incremental clustering using sector-based method and scalable deletion which removes unwanted, spurious data is proposed in this work in order to process incoming streaming data effectively. Incremental clustering is one of the clustering methods used for processing dynamic information. It is not necessary to plan the entire dataset ahead of time in this method which is suitable for live streaming data, because it processes one instance at a time. So, less space and time is used. Various queries with different types of datasets executed using sector based incremental clustering and scalable deletion methods. The proposed system achieves the highest results for more than 90% accuracy with less space and more speed when the execution time and data retrieval are empirically increased compared with conventional approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data streams

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Real Time Environmental Fault Detection and Diagnosis in Photovoltaic systems 4

Real Time Environmental Fault Detection and Diagnosis in Pho...

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4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Kumar, Swetha R. Sri Ramalakshmi, P. Nithiyashri, S. Priyanka, G.S. Esakkirajan, S. School of Electrical Engineering Vellore Institute of Technology Chennai600127 India Department of Instrumentation and Control Systems Engineering PSG College of Technology Coimbatore641004 India

ISBN: (纸本)9798331528614

- Renewable resources are adopted nowadays to promote sustainable development, enhance energy security, and build resilient communities. The increase in demand for renewable resources has led to the use of photovoltaic systems. These systems must be efficiently monitored for fault and maintained properly for maximum productivity. Faults like cracks, dust, bird droppings and shadows may arise in the solar panel over a period, reducing the overall performance of photovoltaic systems. These faults can significantly reduce the lifespan of the solar panel. In this paper, voltage, current and surface temperature are measured using sensors from the photovoltaic panel. Real-time data from the solar cell via sensors are collected under no-fault, dust-induced, and partial shading-induced fault conditions. A fault detection methodology for photovoltaic (PV) systems is proposed using a combination of real-time power prediction and classification algorithms. Linear regression is employed to predict the fault. The fault is then classified using a random forest algorithm. ©2024 IEEE.

关键词： Power distribution faults

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Robust control and Unified Operation of Integrated Fuel Cell Electric Vehicle and Supercapacitor 25

Robust Control and Unified Operation of Integrated Fuel Cell...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Eltoum, M. A Mohammed Jacknoon, Aman Khan, Khalid Khalid, Muhammad Control & Instrumentation Engineering Department Saudi Arabia Kfupm Electrical Engineering Department Dhahran31261 Saudi Arabia Irc For Sustainable Energy Systems Kfupm Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350340266

The slow response rate of fuel cells (FC) proves to hinder the progression of fuel cell hybrid electric vehicles (FCHEVs). In this paper, a unified robust nonlinear control technique is designed for FCHEV-integrated supercapacitors (SC). Therefore, the FCHEV's requirements can be systematically balanced between FC with high energy density and SC with high power density. Accordingly, the hybrid energy storage system enables better driving range as well as a comparatively lower weight than battery-based electric vehicles. The proposed control technique utilizes feedback linearization in combination with sliding mode control to mitigate the impact of system uncertainties with minimal tracking error. The efficacy is demonstrated based on the presented DC-bus voltage regulation, SC current, and FC current. © 2024 IEEE.

关键词： Sliding mode control

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Voltage Quality Improvement for Hybrid Energy Storage of DC Microgrid 11

Voltage Quality Improvement for Hybrid Energy Storage of DC ...

引用

11th International Conference on Power and Energy systems engineering, CPESE 2024

作者： Mahmoud, Abdulrahman Babiker, Ahmed Mohamed, Mazen Ahmed, Ijaz Khalid, Muhammad Control and Instrumentation Engineering Department Saudi Arabia Department of Electrical Engineering Islamabad Pakistan Electrical Engineering Department Dhahran31261 Saudi Arabia KFUPM Interdisciplinary Research Center for Sustainable Energy Systems Saudi Arabia

ISBN: (纸本)9798350377439

DC microgrids (MGs) have recently garnered significant interest due to their efficient power conversion and simpler control systems compared to AC MGs. However, managing DC MGs presents specific challenges, especially when dealing with pulsed power loads and constant power loads, as both require rapid dynamic responses from the MG, impacting voltage quality. This paper introduces a novel control methods, including a highpass filter with model predictive control (MPC) for current distribution between the battery and supercapacitor (SC) and a neuro-fuzzy system, to address these issues. These methods adjust the SC's state of charge (SoC) following abrupt load changes and maintain its SoC within a predefined range to ensure continuous operation. Using the proposed feedback neuro-fuzzy technique, the MG can improve voltage quality and transient response, achieving results that are nearly identical to those of the MPC. Simulations conducted in MATLAB/Simulink confirm the effectiveness of this approach. © 2024 IEEE.

关键词： High pass filters

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A Resilient Consensus-Based Energy 5.0 Framework for Micro-Grids Under Ramp-Rate Constraints and Stochastic FDI Attacks 25

A Resilient Consensus-Based Energy 5.0 Framework for Micro-G...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Ahmed, Ijaz Basit, Abdul Rehan, Muhammad Ali, Amjad Maaruf, Muhammad Khalid, Muhammad Department Of Electrical Engineering Islamabad Pakistan Irc For Sustainable Energy Systems Saudi Arabia Kfupm Control & Instrumentation Engineering Department Dhahran31261 Saudi Arabia Kfupm Electrical Engineering Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350340266

Distributed power allocation (DPA) in micro-grids (MGs) has a significant potential for attaining a cost effective and reliable solution of power dispatch. Considering the significant risks posed by cyber-attacks and system limitations to the MGs, it is important to develop resilient methods for an efficient DPA, which is valuable both in terms of theoretical understanding and practical application for energy 5.0 framework. This work addresses the DPA problem in MGs, specifically focusing on the economic dispatch (ED) for the generation side while taking into account ramp-rate limits (RRLs) and stochastic false-data injection (FDI) attacks. A consensus-based protocol has been developed by considering the information of the incremental costs of neighbouring generators and local power mismatch for a generation facility. A stochastic FDI attack at the shared information over the network has been accounted. The proposed technique guarantees robust (and resilient) convergence by providing a sufficient criterion under RRLs and stochastic FDI attacks. The proposed approach is based of the distributed ED framework, and convergence analysis has been performed via two Lyapunov functions. The convergence of total power mismatch and error in synchronization of incremental costs to bounded sets are ensured by the proposed approach. The presented framework achieves an efficient energy production cost. The network's robustness and resilience for the shared information of incremental cost of energy production units as the consensus variable are accounted. The simulation study for a set of five generators under stochastic FDI attacks and RRLs are provided which confirm the theoretical findings of the proposed scheme. © 2024 IEEE.

关键词： Network topology

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Sliding Mode control of Non-Minimum Phase Autonomous Underwater Vehicles in the Dive Plane

Sliding Mode Control of Non-Minimum Phase Autonomous Underwa...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Pore, Gauravi Patil, MacHhindranath Vachhani, Leena Indian Institute of Technology Bombay Systems and Control Engineering Mumbai India V.E.S Institute of Technology Department of Instrumentation Engineering Mumbai India

ISBN: (纸本)9798218142186

This paper shows that the torpedo-type Autonomous Underwater Vehicle (AUV) exhibits the non-minimum phase behavior in the depth control operation for a certain range of forward velocity. Contributions in this paper are (i) Transform AUV's 6 DOF dynamic model in a diving plane to the normal form. (ii) A method to determine the range of the forward surge velocity for which zero dynamics is unstable. Understanding the non-minimum phase behavior provides insight into the controller designs in the future, specifically for the identified ranges of the forward surge velocities. © 2023 The Marine Technology Society (MTS).

关键词： Autonomous underwater vehicles

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Swarm Intelligence-Based Tuning of Hybrid controller for control of Neutron Density in Nonlinear Pressurized Water Reactor

Swarm Intelligence-Based Tuning of Hybrid Controller for Con...

引用

International Conference on Sustainable Technology and Advanced Computing in Electrical engineering, ICSTACE 2021

作者： Kumar, Swetha R. Jayaprasanth, D. Department of Instrumentation and Control Systems Engineering PSG College of Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9789811943638

Nuclear energy has zero-emission and can generate power as a baseload station to meet demand in this energy crisis. To enhance the safety and control in power generation, artificial intelligence shall be utilized. This study aims design a healthy control system for regulating the neutron density in the highly nonlinear pressurized water reactor (PWR) core. The conventional way of controlling reactor power or the neutron density is by using a proportional-integral-derivative controller. But the internal variables of the reactor core are not considered in the PID controller. The proposed methodology implements hybrid state feedback and PID controller for neutron density regulation where the controller parameters are tuned optimally via swarm intelligence like particle swarm optimization and ant colony optimization. Swarm intelligence uses metaheuristic algorithms for resolving optimization problems and obtains optimal controller gains. This hybrid controller gives robust performance in the occurrence of disturbances on internal variables. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Swarm intelligence

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Detection of Cracks in the Industrial System Using Adaptive Principal Component Analysis and Wavelet Denoising 25

Detection of Cracks in the Industrial System Using Adaptive ...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Faizan-E-Mustafa Ahmed, Ijaz Maaruf, Muhammad Khalid, Muhammad Islamabad44650 Pakistan Control & Instrumentation Engineering Department Dhahran31261 Saudi Arabia Kfupm Electrical Engineering Department Dhahran31261 Saudi Arabia Irc For Sustainable Energy Systems Kfupm Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350340266

In evaluating industrial cyber-physical systems, there is an emerging inclination toward employing data-driven exploration techniques for the purpose of fault detecting and diagnosing typical procedural behaviors. The primary objective is to identify occurrences of typical behavior of the fundamental factors contributing to the aberrations. Multivariate statistical analysis, namely Principal Component Analysis (PCA), has con-siderable importance within the realm of research methodologies. This method is frequently employed in the context of outlier detection metrics, including the Hotelling T2 statistic and the Squared Prediction Error (SPE), which are commonly utilized for fault identification purposes. The present work presents a novel and adaptable thresholding technique that utilizes a modified variant of the PCA with wavelet denoising. This method en-ables enhanced sensitivity and robustness to abnormalities while minimizing the occurrence of false alarms and missed detection rate. In this paper, the novel methodology employs a dynamic threshold that undergoes adjustments in accordance with the available data of the Hanoi University of Science and Technology ball-bearing experimental setup. The findings indicated that the novel strategy exhibited significantly more efficacy in identifying anomalies compared to the conventional technique. Moreover, it exhibited a reduced probability of producing erroneous alerts raising sensitivity and robustness for fault detection. © 2024 IEEE.

关键词： Principal component analysis

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