Advanced Control for Applications最新文献

{"title":"ISO-QLCNet: Optimized Q-Learning Based Convolutional Network for Harmonic Mitigation in Traction Network","authors":"Sushma V. Sangle,&nbsp;Prakash G. Burade","doi":"10.1002/adc2.70062","DOIUrl":"https://doi.org/10.1002/adc2.70062","url":null,"abstract":"<div>\u0000 \u0000 <p>Specialized electrical infrastructure of Traction Power Supply Systems (TPSS) adopts the high voltage levels while varying load current dynamics, which is distinct from a general power system by handling the high power and train loads. However, transformer saturation, unbalanced loads, non-linear power electronic devices, and system resonance factors introduced the harmonics in the TPSS, which resulted in equipment damage issues and contributed to performance degradation. Consequently, the research work implements the Intelligent Swooping Optimization-based Q-Learning distributed one-dimensional Convolutional Network (ISO-QLCNet) model to perform the harmonic mitigation task in the TPSS. The QLCNet model predicts potential failures and learns the optimal control policies to mitigate the harmonics in the railway operations. Moreover, the developed model utilized the dynamic and non-linear loads to identify the complex harmonic patterns and indicate the power quality (PQ) issues to achieve the mitigation task effectively. Furthermore, the model parameters are optimized by the ISO algorithm, which helps to meet the maximum energy efficiency and lower total harmonic distortion (THD). Eventually, the ISO-QLCNet model exhibits the THD, train-1 voltage, train-1 current, switching output, train-2 current, and train-2 voltage are 0.62, 562.26 V, 8217.19 A, 5260.74, 961.54 A, and 632.49 V, respectively.</p>\u0000 </div>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"8 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.70062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Generation Control Optimization of an Isolated Power System Including Renewable Energy Penetration","authors":"Heba- Al-Tahhan,&nbsp;Omar Mohamed,&nbsp;Wejdan Abu Elhaija","doi":"10.1002/adc2.70061","DOIUrl":"https://doi.org/10.1002/adc2.70061","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents an enhanced version of controllers of frequency and voltage of an isolated power system. The optimal tuning includes the load-frequency control system integrator loop, the governor, and the automatic voltage regulator that includes the PID parameters and amplifier gain. With such parameters, two objective functions shall be minimized, which are the frequency deviation and voltage deviation. Modern power systems embed renewable energy sources in generation and/or demand sides. Therefore, two real-life signals of wind and photovoltaic power sources have been included as external disturbances affecting the main quality variables, the frequency and voltage, of the conventional thermal unit. With such assumptions, the problem of load-frequency control (LFC) and automatic voltage regulator (AVR) becomes more challenging since the power system is assumed to be isolated and without interconnection to release the excess renewable energies to neighboring regions. In addition, it is rare in the literature to include the LFC and AVR in the same system model and that was based on little coupling sensitivity between AVR and LFC. With all these considerations, an optimization problem has been successfully solved via two metaheuristic optimizers, the political optimizer (PO) and Gray-Wolf Optimizer (GWO). The improvements have been notable by dynamic simulations over sufficient time to allow abrupt system conditions. There are also some remarkable differences between PO and GWO that have been discovered and reported during research.</p>\u0000 </div>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"8 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.70061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of an Adaptive Estimator-Based Sliding Mode Controller With an Initial Voltage Error-Independent Reaching Time for Voltage Control of a DC Microgrid With Constant Power Loads","authors":"Can Sheng,&nbsp;Xuanfu Du,&nbsp;Jiawei Zhu,&nbsp;Dongxu Sheng","doi":"10.1002/adc2.70058","DOIUrl":"https://doi.org/10.1002/adc2.70058","url":null,"abstract":"<div>\u0000 \u0000 <p>DC microgrids face major operational challenges because constant power loads maintain their power consumption at fixed levels. The network experiences its main distortion source from these elements, which produce oscillations and lead to system breakdown through their negative incremental impedance effects. The authors of this study present a fast-SMC system that regulates DC microgrid voltage levels while operating with constant power loads. The system enables fast bus reference voltage tracking, together with better tracking performance and reduced system oscillations. To achieve these capabilities, a new reaching law is introduced that is independent of initial conditions and provides fixed-time convergence. It also helps reduce chattering by reducing the sign function coefficient. In addition, a new adaptive estimator is presented that is able to calculate the network power instantaneously and makes the control law robust to power variations. The research team conducted experimental testing to assess how well the control system performed. The research findings reveal that the recommended scheme achieves better results than all other tested methods.</p>\u0000 </div>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"8 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"System-Level Anomaly Detection in BLDC-Actuated Robotic Arms Using D-Bi-LSTM and BSLO","authors":"Neethu Salim,&nbsp;Francis Thomas Josh,&nbsp;J. Jency Joseph,&nbsp;Jarin Thankaswamy","doi":"10.1002/adc2.70060","DOIUrl":"https://doi.org/10.1002/adc2.70060","url":null,"abstract":"<div>\u0000 \u0000 <p>In robotic arms, Brushless DC (BLDC) motors are important in offering proper motion control and provide smooth and better operation. These motors are mounted on the frame of the robotic arm, providing smooth and stable joint movements. Anomaly detection in robotic arms is important to provide operational efficiency and reliability. This work introduces a deep learning (DL) Dual Bidirectional Long Short-Term Memory (D-Bi-LSTM) based Blood Sucking Leech Optimizer (BSLO) for anomaly detection, formulated as a binary anomaly detection task that distinguishes normal and anomalous operating states in the BLDC based robotic arm using velocity data. The proposed model combines the temporal learning capabilities of D-Bi-LSTM with the strengths of the BSLO algorithm for automatic hyperparameter tuning and faster convergence, and improves anomaly detection accuracy. The suggested work detects these motor-related faults effectively by analyzing sensor data and extracting key features. The outcomes show that the dual Bi-LSTM with BSLO attained superior anomaly detection accuracy of 98.9% and created it a robust solution for early anomaly detection in robotic arms. The proposed approach detects system-level anomalies reflected in joint motion behavior, rather than detecting internal electrical or mechanical BLDC motor faults.</p>\u0000 </div>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"8 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.70060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148462898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of a Three-Phase PWM Rectifier Based on Improved Whale Optimization Algorithm Optimized Fuzzy Control","authors":"Xiaosong Tian,&nbsp;Meiguang Jiang,&nbsp;Congguo Jiang,&nbsp;Guangya Liu,&nbsp;Jun Zhang,&nbsp;Xiangwen Lv,&nbsp;Weijiang Yang,&nbsp;Ruoyu Yang","doi":"10.1002/adc2.70059","DOIUrl":"https://doi.org/10.1002/adc2.70059","url":null,"abstract":"<p>To address the issues of slow dynamic response and insufficient anti-interference capability in the traditional voltage outer-loop PI control of three-phase PWM rectifiers, this paper proposes a control strategy based on an improved Whale Optimization Algorithm (WOA) optimized variable domain fuzzy PID. First, a voltage outer-loop control model is established in the two-phase rotating <i>dq</i> coordinate system. Second, to improve the slow dynamic response of the traditional PI voltage outer loop, fuzzy control combined with PI control is introduced, incorporating correction factors for the proportional and integral coefficients. Then, to better adapt the fuzzy PI control to the nonlinear characteristics of voltage and current, the Whale Optimization Algorithm is used to optimize the scaling factors of the variable domain fuzzy control, thereby solving the problem that traditional control cannot adjust parameters in real time. Finally, a system model is built in Matlab/Simulink for simulation experiments under step changes in DC voltage reference and load. The results show that after applying the improved WOA-optimized fuzzy PI control, the total harmonic distortion of the current is significantly reduced, the DC voltage recovery time is shortened with less overshoot, and the system dynamic response speed and disturbance robustness are improved.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"8 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.70059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148343079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Control of Uncertain PV System for Improved Performance by Smith Predictor and H∞ Loop Shaping Controller","authors":"Shilpa Saxena,&nbsp;Vijay Kumar Tayal,&nbsp;Hemender Pal Singh,&nbsp;Vinod Kumar Yadav","doi":"10.1002/adc2.70057","DOIUrl":"https://doi.org/10.1002/adc2.70057","url":null,"abstract":"<div>\u0000 \u0000 <p>The significance of photovoltaic (PV) plants among renewable energy systems is unparalleled due to their ozone-friendly features. However, their inherent nonlinear behavior for various uncertainties (due to change in irradiation) renders the suboptimal performance. Thus, there is a need for robust controllers. On the basis of daily operation curve of PV plant, parametric uncertainty is incorporated in nonlinear PV time delay models. Further, an intelligent H_∞ loop shaping controller is designed to improve the performance of PV plant subjected to parametric variations. The uncertain PV plant output with H_∞ loop shaping controller is compared with conventional PID controller and Smith Predictor control techniques. Simulation results show degraded performance with PID controller whereas Smith Predictor performs better. However, the best dynamics are obtained with H_∞ loop shaping controller for all operating points. The robust control design yields in significant reduction in rise time, settling time, rapid disturbance dismissal with a short peak time. Further, sensitivity and complementary sensitivity analysis is also carried out to validate the stability and robustness of plant with proposed H_∞ loop shaping controller under all operating points. Thus, proposed H_∞ loop shaping controller enhances the performance, stability and robustness of system manifolds for all operating points under uncertainty and nonlinearity.</p>\u0000 </div>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"8 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.70057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Load Frequency Control of Two-Area System Incorporating Wind Farms Through HVDC Lines and Electric Vehicles Using Hybrid Controller","authors":"Omkar Yadav,&nbsp;Guru Govind Prasad,&nbsp;Vinay Pratap Singh,&nbsp;Sugandh Pratap Singh","doi":"10.1002/adc2.70056","DOIUrl":"https://doi.org/10.1002/adc2.70056","url":null,"abstract":"<div>\u0000 \u0000 <p>The rising penetration level of Electric Vehicles (EVs) and Wind Farms (WFs) in power systems makes them more susceptible to issues with low inertia and significant impact on power system networks. To provide reliable frequency control in power system networks, this study introduces a novel control technique that combines Fuzzy Proportional Integral Derivative (F-PID) and Proportional Integral Derivative (PID) controllers in a single integrated entity. The proposed controller is effectively handling the variety of disturbances and Frequency control in the power system, which contains WFs and Electric Vehicle Aggregators (EVAs). EVAs, acting as intermediaries, manage the interaction between EVs and the grid. They can facilitate Vehicle-to-Grid (V2G) services, allowing EVs to provide power back to the grid when required. This unique capability allows EVAs to actively participate in load frequency control (LFC), contributing to the stability of the grid by adjusting their energy consumption and supply in response to grid frequency fluctuations. For optimal performance, all the variables of the proposed controller are fine-tuned using a novel soft computing technique known as the Incomprehensible but Intelligible-in-time Logic Algorithm (ILA). Moreover, constraints like the reheat turbine and the dead band are accounted for in different units. Existing EVs assist in providing support for frequency regulation in the power system when a sudden load disturbance occurs. A comparative performance assessment has been undertaken to illustrate the effectiveness of the proposed controller. Also, various step load disturbances (SLD) and random load disturbances on the system have been applied to evaluate the hardiness of the proposed controller. A comparison of the results among the different error criteria as the objective function in the optimization technique has been made. Ultimately, this novel idea is examined through several case studies in MATLAB/Simulink. The simulation outcomes validate that the proposed control scheme exhibits superior dynamic performance compared to F-PID and PID controllers across different scenarios of SLD as well as random load variations.</p>\u0000 </div>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"8 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.70056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IA2UCS: An Intelligent Atmospheric-Adaptive UAV Control System Using Machine Learning and Real-Time Weather Integration for Enhanced Flight Stability","authors":"Divyesh Divakar,&nbsp;Rajalakshmi Samaga B L","doi":"10.1002/adc2.70054","DOIUrl":"https://doi.org/10.1002/adc2.70054","url":null,"abstract":"<div>\u0000 \u0000 <p>Conventionally, Unmanned Aerial Vehicle (UAV) control systems are not able to handle atmospheric uncertainty such as wind turbulence, precipitation-induced aerodynamic changes, and temperature changes, which degrade flight stability and mission reliability. Intending to introduce an Intelligence Atmospheric-Adaptive UAV Control System (IA2UCS), this research uses Machine Learning (ML) algorithms with atmospheric data to improve the UAV performance in dynamic environmental conditions. These limitations are overcome in the proposed IA2UCS framework by the following three interconnected modules: (1) Robust Adaptive Control Module (RACM) that uses sliding mode control with Deep Reinforcement Learning (DRL) adaptation to deal with lumped uncertainties, (2) Predictive Atmospheric Intelligence System (PAIS) that uses ensemble learning algorithms, such as Random Forest, Long Short-Term Memory (LSTM) networks, and Physics-Informed Neural Networks (PINNs) to predict the weather, and (3) Flight Safety Assessment Protocol (FSAP) that uses fuzzy logic-based risk evaluation to determine the flight conditions. The approach is to include real-time sensor fusion, historical meteorological data analysis and adaptive parameter tuning to maximize flight performance. The proposed system demonstrates high resilience to actuator faults, rapid fault detection under 0.5 s, and stable recovery within 2 s, making it suitable for real-world deployment in complex atmospheric environments.</p>\u0000 </div>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"8 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147585038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Backstepping Control of a Multi-Machine Wind Generation System With Parallel Connection Utilizing a Fifteen-Switch Rectifier","authors":"Abderrahim Sakouchi,&nbsp;Abdelkader Djahbar,&nbsp;Elhadj Bounadja,&nbsp;Abdelhafidh Moualdia","doi":"10.1002/adc2.70055","DOIUrl":"https://doi.org/10.1002/adc2.70055","url":null,"abstract":"<div>\u0000 \u0000 <p>This academic article introduces a wind system (WS) that uses a parallel arrangement of five-phase permanent magnet synchronous generators (PMSGs). The control mechanism for this system uses a fifteen-switch rectifier (FSR) architecture, which has been particularly designed for grid-connected applications. To enhance the control performance of the WS being suggested, the backstepping control (BSC) approach is used for both the converters on the generators side and the converters on the grid side. The efficacy of the suggested control technique is assessed by conducting simulations under different wind speeds and comparing it to a control system that utilizes a proportional–integral (PI) controller. The simulation findings confirm that the suggested control mechanism guarantees precise monitoring of the regulated variables. Furthermore, significant enhancements are seen in several facets of the system's performance. More precisely, the suggested control technique decreases active power fluctuations by 10% and reactive power by 20%, limits the increase in DC bus voltage to 4.4%, and speeds up overshoot by 18%. The network current's total harmonic distortion is 41.8% lower compared to the traditional approach. In addition, the system's efficiency has been improved to 96%, exceeding the 94.3% efficiency attained using the backstepping approach. The suggested technique demonstrates higher performance when compared to a conventional method.</p>\u0000 </div>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"8 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flatness-Based Control in Successive Loops for Delta Parallel Robots in the Task Space","authors":"Gerasimos Rigatos,&nbsp;Masoud Abbaszadeh,&nbsp;Pierluigi Siano,&nbsp;Khmaies Ouahada,&nbsp;Zhiwei Gao","doi":"10.1002/adc2.70053","DOIUrl":"https://doi.org/10.1002/adc2.70053","url":null,"abstract":"<div>\u0000 \u0000 <p>Delta parallel robots are widely used in industry for executing various manufacturing tasks at high speed. Such robots exhibit a highly complex and nonlinear kinematic and dynamic model. In this article, the control problem of 3-DOF delta parallel robots is considered. The joint kinematic and dynamic model of these robots for operation in the task space is formulated in state-space form and is shown to be differentially flat. The control problem for these robotic systems is solved with the use of a flatness-based control approach, which is implemented in successive loops. To apply the multi-loop flatness-based control method, the state-space model of the delta parallel robot is separated into subsystems, which are connected in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system, and control of it can be performed with inversion of its dynamics as in the case of input-output linearized systems. The state variables of the subsequent (i+1)-th subsystem become virtual control inputs for the preceding i-th subsystem. Exogenous control inputs are applied to the last subsystem. The stabilizing feedback control signal is computed at the last subsystem, by tracing backwards at each sampling instance the virtual control inputs of all N-1 preceding subsystems. The whole control method is implemented in successive loops, and its global stability properties are also proven through Lyapunov stability analysis. The proposed method achieves stabilization of the delta parallel robot without the need for diffeomorphisms and complicated state-space model transformations.</p>\u0000 </div>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"8 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.70053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}