JournalTOCs

Journal of Control, Automation and Electrical Systems
Journal Prestige (SJR): 0.274

Citation Impact (citeScore): 1
Number of Followers: 11

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2195-3880 - ISSN (Online) 2195-3899
Published by Springer-Verlag

[2468 journals]

A Novel Nonlinear Extended State Observer-Based Internal Model Control for
a Class of Nonlinear Systems Applied to Remotely Operated Vehicles
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: This paper presents a tracking control strategy for underwater remotely operated vehicles (ROVs) that addresses modeling uncertainties and external disturbances. A nonlinear flatness-based internal model control (NLIMC) law is initially synthesized for a class of nonlinear systems. To mitigate the effects of modeling imperfections and unknown external disturbances, a novel nonlinear disturbance observer, incorporating exponential and hyperbolic tangent functions, is integrated with the NLIMC. This observer efficiently estimates the total disturbance and the system’s full states. The stability of the nominal controller and the exponential convergence of the observer’s error are rigorously proven using Lyapunov stability and Linear Matrix Inequality (LMI) theories. Compared to the traditional active disturbance rejection control method based on the linear Extended State Observer (ESO), and a disturbance observer-based sliding mode control, our proposed control strategy demonstrates superior performance in terms of precision, convergence speed, and disturbance rejection.
  PubDate: 2025-07-07
Interpretable TSK Fuzzy Classification with Preserved Approximate Physical
Properties for Source Space Features
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: Takagi–Sugeno–Kang (TSK) fuzzy classifier, due to its simplicity in computation, ease of implementation, and inherent interpretability, has been studied for handling various uncertainties. However, most existing variants of TSK classifier face the following challenges: (1) high coupling between features, (2) difficulties in expressing the importance of features in fuzzy rule setting, and (3) neglect of the transmission of important rules in the decision-making process. In this study, a novel weighted-based hierarchical TSK fuzzy classifier, WB-D-TSK-FC, is constructed to address the above challenges. A feature scoring mechanism and a short-rule antecedent parameters optimization strategy with enlarged weight of fuzzy membership expectation value are proposed to weaken the coupling relationship between features and reduce computational complexity. An error-active intervention is proposed to constrain the training errors of each sub-classifier to improve the classifier’s generalization ability. Furthermore, an important rule transfer fusion mechanism is designed to fully exploit the decision-making ability of important rules and their crucial roles in information transmission. Finally, an input space optimization is proposed, which emphasizes continuous historical judgment information without deviating from the original training space, aiming to maintain high interpretability. Experimental results show that WB-D-TSK-FC indeed exhibits strong classification advantages compared to several selected classifiers.
  PubDate: 2025-06-29
Design of Fixed-Time Tracking Control Scheme for the Perturbed Systems by
Using Dynamic Surface Control and A New Disturbance-Observer
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: In this article, a new fast tracking control algorithm of uncertain nonlinear systems is proposed based on the dynamic surface control (DSC) and fixed-time disturbance-observer. Firstly, to improve the response performance, a new stability theorem, which plays a critical role in the control design, is established for the nonlinear systems. Secondly, to enhance the ability of disturbance attenuation, a disturbance-observer is constructed to estimate the system disturbance in a bounded time. Thirdly, to address the inherent problem of “explosion of complexity” in backstepping design, a new dynamic surface control algorithm is carefully designed, and tracking error of closed-loop system will converge to a small region of origin in a short time. Furthermore, the proposed control scheme can not only overcome the “complexity explosion" problem caused by repeated derivation of virtual controller, but also provide a fast and robust convergence performance. In the end, simulation results are given to show the effectiveness of obtained results.
  PubDate: 2025-06-21
Iterative Learning Control for Distributed Parameter Systems with
Sensor/Actuator Networks Based on High-Order Internal Mode
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: The issue of iterative learning control (ILC) for distributed parameter systems with sensor/actuator networks is explored. Unlike the traditional setting of ILC where the desired trajectories are identical, here the desired trajectories are iteratively varying and characterized by a high-order internal mode (HOIM). To address this challenge, the D-type ILC algorithm based on HOIM is devised in this paper. This algorithm enables the systems to accurately track the desired trajectories that vary with each iteration. Using the principle of compressive mapping, the convergence conditions of the output error of the systems are given. In conclusion, numerical simulations are performed to verify the effectiveness of the proposed algorithm.
  PubDate: 2025-06-18
State-Space Modeling with Type-2 Fuzzy Logic: An Evolving Neural-Fuzzy
Model for Handling Uncertain Experimental Data
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: In real-world identification problems, dynamic systems are typically nonlinear, complex, and subject to uncertainty. Additionally, experimental data can be corrupted by stochastic noise with varying statistical properties, such as uniform, Gaussian, or autocorrelated noise. To address these challenges, this paper proposes a novel evolving Interval Type-2 Neural-Fuzzy Model. The proposed methodology has two key contributions. First, a filtering layer performs data filtering, mitigating noise and ensuring robustness in both antecedent and consequent estimation. The filtered data enhance the accuracy of the model. Second, an Interval Type-2 fuzzy inference engine computes a confidence region, where the degree of uncertainty is dynamically adjusted based on the noise level in the experimental data. The model is structured into five layers: (1) a filtering layer that applies a recursive moving-average filter; (2) an evolving antecedent estimation layer that partitions the data space using an evolving type-2 fuzzy clustering algorithm; (3) a rule activation layer that computes the degree of rule activation; (4) a recursive submodel estimation layer that updates model parameters using interval type-2 instrumental variables method; and (5) a type-2 fuzzy inference engine that estimates the confidence region. Experimental results on nonlinear SISO systems and rocket trajectory estimation demonstrate the competitive model performance in handling noise and achieving accurate identification. The eIT2NFSO provides a robust and adaptable framework for modeling complex systems in noisy environments.
  PubDate: 2025-06-16
Coordinated Power and Mechanical Loads Optimization Strategy of Wind
Turbine Based on Model Predictive Control
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: Modern wind turbine design is evolving toward large-scale, high-capacity configurations. Under complex operational conditions, these turbines are subjected to significant mechanical loads coupled with power fluctuations. The pitch control system, a critical component of wind turbines, plays a pivotal role in regulating power output and alleviating load fluctuations. Given the limitations of conventional pitch control in adapting to wide-ranging wind speed variations and the need to balance power regulation with load mitigation objectives, this study proposes a pitch control framework based on nonlinear model predictive control. Using the National Renewable Energy Laboratory 5MW turbine as the research object, a reduced-order dynamic model is developed through mechanistic analysis and applied to the pitch control system design. Leveraging the OpenFAST and MATLAB/Simulink co-simulation platform, numerical validation is performed under diverse wind conditions. The results demonstrate that the controller adapts dynamically to the turbine’s real-time operating conditions and dynamic scenarios. The multi-objective optimization framework enhances power regulation performance while effectively suppressing tower fore-aft oscillations, thereby reducing tower mechanical loads. Compared to traditional strategies, the proposed framework achieves simultaneous optimization of power output and mechanical load mitigation.
  PubDate: 2025-06-14
Unsupervised Machine Learning-Based Intrusion Detection in PROFINET
Networks
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: Modern industry requires the convergence of Operational Technologies (OT) with Information Technologies (IT). Industrial communications networks have been exhibiting characteristics of both domains. However, inherent problems from the IT world are now being considered on the factory floor. Once communication protocols based on Real-Time Ethernet are susceptible to intrusions, this work proposes a method for detecting attacks in PROFINET networks. The methodology employed feature extraction from data traffic using a sliding window technique, dimensionality reduction with an autoencoder, and classification using a One-Class Support Vector Machine (OCSVM). The algorithm is an unsupervised machine learning classifier, which reduces computational effort. This technique is well-established and meets the defined requirements, contributing to its implementation in other approaches. A total of six distinct scenarios are analyzed. It is highlighted that two of them involve data acquired in real industry applications. The results show accuracy in the range of 97.3–100%, depending on the scenario.
  PubDate: 2025-06-14
The Latent Variable Subspace in Synchronization Problems
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: Detecting and quantifying phase synchronization of chaotic oscillators are not trivial tasks. Many approaches involve creating a phase model, or estimating the phase. An approach is here introduced that does not require a phase definition, rather it is based on the monitoring of the so-called latent variables, or principal components (PCs). Although PCs are well-known in signal processing, their use in synchronization problems has not been fully exploited. As the phase synchronization deteriorates, the latent space becomes disorganized and this can be characterized through a rather simple measure taken from principal components analysis, namely the explained variance ratio (EVR). It is shown that such a measure of the latent space correlates well with an already established phase synchronization measure, the mean phase coherence. Further, by monitoring this ratio when increasing the coupling strength in a network, it is possible to identify the formation of clusters of synchronized oscillators. The proposed methodology has shown to be consistent over a range of scenarios, including pairs of coupled oscillators, and different topologies of networks. The EVR can be used in cases where phase estimation is difficult or inaccurate or in larger networks, where the phase error has to be computed pairwise. It is shown that EVR can also be a measure of generalized synchronization of systems with different topologies.
  PubDate: 2025-06-10
Adaptive Fuzzy Dynamic Surface Control with Sliding Mode Control for
Enhanced Trajectory Tracking of Delta Robots
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: This paper proposes an Adaptive Fuzzy Dynamic Surface Sliding Mode Control (AFDSC-SMC) method, specifically designed for precise trajectory tracking in a three-degree-of-freedom (3-DOF) Delta robot. By integrating Dynamic Surface Control (DSC) with Sliding Mode Control (SMC), the proposed approach effectively mitigates chattering, ensuring smooth control actions and enhanced tracking accuracy. An adaptive fuzzy logic mechanism is incorporated to dynamically adjust control parameters, thereby improving robustness against uncertainties and external disturbances. The stability of the closed-loop system is rigorously analyzed using Lyapunov theory, guaranteeing input-to-state stability (ISS). To validate the effectiveness of the proposed method, extensive simulations are conducted in MATLAB/Simulink across various trajectory scenarios. The performance of AFDSC-SMC is compared with that of traditional DSC and Backstepping Sliding Mode Control (BSP-SMC) under conditions involving disturbances and variations in model parameters. Simulation results demonstrate that AFDSC-SMC achieves stability within 0.15 s for three joints without overshooting. Furthermore, the proposed controller minimizes steady-state tracking errors to zero, delivering smooth control inputs within a range of ±15 Nm, outperforming conventional DSC and BSP-SMC. Quantitative visualizations further highlight that AFDSC-SMC significantly enhances trajectory tracking performance, providing precise end-effector positioning and robust operation in high-speed automation applications.
  PubDate: 2025-06-09
Influence of Different Excitations in the Identification of a Nonlinear
Cart-Pendulum System
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: The identification performance of a typical nonlinear mechanical system, as a gantry equipment, is affected not only by the most appropriate method to be applied. The excitation signal used plays a crucial role in conjunction with the method. In this work, the classical Cart-Pendulum nonlinear system is simulated with different input excitation to provide the dataset required for the tasks of System identification. Gaussian White Noise (GWN) and Pseudo Random Binary Sequence (PRBS) signals are compared as excitations for evaluating the identification effectiveness when applying the Autoregressive Moving Average with External input (ARMAX) method. The choice of a traditional algorithm becomes appropriate in the context of this work since the focus was to analyze the influence of excitations on the identification performance. The identification process was also submitted to an additive measurement noise of 3% during the system simulations, which fulfilled an amount of 100 datasets for each identification setting. Based on a statistical analysis of 100 identifications performed for each configuration, the results showed an identification effectiveness of more than 96%. That outcome was achieved specifically when the ARMAX was applied in conjunction with the PRBS adjusted with a clock frequency reduction compared to GWN excitation. In addition to the ARMAX method, the comparative tests with other identification algorithms for both the GWN and PRBS excitations indicated a feasible and leaner possibility of identification applying the ARMAX when the nonlinear system is excited by a PRBS appropriately configured in terms of switching determined by the clock frequency.
  PubDate: 2025-05-31
Master-Slave Synchronization for Parameter Estimation of Photovoltaic
Module Model
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: Photovoltaic systems are the most mature and promising technology for the generation of clean electricity. However, due to their non-convex, nonlinear, and multi-parametric characteristics, models and methods must be developed for optimizing their operation in different environments and conditions for forecasting power and determining the efficiency of a PV plant. Several algorithms have aimed at accurately defining the parameters and most of them focus on improving both exploration and exploitation of the method by combining different techniques. On the other hand, a simple change in a model can improve the accuracy of parameters. This study proposes a small alternation in the model, i.e., inclusion of synchronization inputs by master-slave coupling between real and mathematical systems. The coupling was used with swarm mean-variance mapping optimization (Swarm MVMO) metaheuristic method and applied to a photovoltaic module model (PVM) for different temperature and irradiance measurements. The results showed an improvement in the estimated parameters after the aforementioned inclusion in comparison with another method from the literature.
  PubDate: 2025-05-31
Development and Modeling of the K-Nearest Neighbors Algorithm Using
Coloured Petri Nets
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: The K-nearest neighbor algorithm is among the most widely used methods for classification and regression problems in machine learning and is preferred over many other methods because of its simplicity and availability as a tool in many open-source software libraries. Despite its importance, it is usually offered as an implementation with limited insights into the algorithm steps, which can lead researchers to miss valuable comprehension about the intrinsic details of the method itself. To mitigate this, we introduce a K-Nearest Neighbor (KNN) model implementation that relies on colored Petri nets to enhance the understanding and graphical visualization of the algorithm. The proposed approach uses CPN Tools for modeling the KNN algorithm and conducting tests with various recognized classification and regression datasets from the literature. The model was validated via a comparison with a Python KNN implementation using the Scikit-learn library. An application example of dynamic system modeling is presented in the context of fault detection for robotic manipulators. The results showed that the proposed implementation achieved performance equivalent to the Python implementation and that it allows for a detailed diagnosis, enabling greater understanding and highlighting the relevant steps of the operation of complex discrete event systems.
  PubDate: 2025-05-30
Skeleton-based Human Action Recognition using Ricci Curvature and Graphs
Neural Networks
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: Human action recognition is a critical task in computer vision, enabling systems to interpret and respond to human movements. This paper introduces a novel approach that utilizes Augmentations of Forman-Ricci Curvature (AFRC) within Graph Neural Networks (GNNs) to enhance the recognition of actions based on skeletal data. Traditional methods often struggle with issues like information over-squashing, which can limit their effectiveness. The proposed framework addresses these challenges by improving the expressiveness of graph representations of human skeletons, allowing for more accurate action classification. Through extensive experiments on the Chalearn benchmark dataset, we demonstrate that the proposed method significantly outperforms existing techniques, achieving competitive accuracy levels while maintaining computational efficiency. The integration of AFRC not only mitigates bottleneck effects but also captures complex relationships between skeletal joints, which are essential for precise gesture recognition. The implications of this research extend to various applications, including human-computer interaction, sports analysis, and surveillance, highlighting the potential for real-time action recognition systems. Future work will focus on refining the AFRC algorithm and exploring its applicability across different domains.
  PubDate: 2025-05-28
Bald Eagle Search Optimization Based MPPT Control Strategy for a
Stand-Alone PV System Operating Under Real Climatic Conditions
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: This paper presents a robust MPPT control strategy for photovoltaic (PV) systems under real climatic conditions. The proposed method integrates Backstepping Sliding Mode Control (BSMC) with the Bald Eagles Search (BES) optimization algorithm to enhance tracking performance and system robustness. By combining the adaptability of BSMC with the optimization capability of BES, the approach ensures precise and stable convergence to the Maximum Power Point (MPP). Lyapunov-based analysis confirms system stability, while simulations using both synthetic and real meteorological data from Errachidia, Morocco, validate the method’s effectiveness. The BES-BSMC strategy achieves superior performance, with a steady-state error of 6 × 10⁻4 V, response time of 1.72 s, overshoot of 1.14%, chattering magnitude of 4.5 × 10⁻⁷ V, and efficiency of 99.53%. These results clearly outperform conventional SMC and ISMC controllers. The study demonstrates the benefits of integrating intelligent optimization with nonlinear control for reliable solar energy harvesting under dynamic environmental conditions.
  PubDate: 2025-05-28
Sizing and Placement Strategy for Integration of Large-Scale Photovoltaic
Systems in Power Transmission Networks
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: To meet the ever-increasing electricity demand in environment-friendly manner, renewable energy sources must be brought into the energy mix. Due to this, the PV systems are getting integrated with the grid in huge proportions, referred as large-scale photovoltaic (LSPV) systems, across the globe. The inadequate LSPV size integrated at an undesirable location can result in higher system losses and alter the voltage and may result in system instability, which is otherwise meant for improvement of system power balance and reduction of carbon emissions. In this connection, this paper aims to provide a methodology to identify the ideal location and size of LSPV. In the process, this paper validates multiple approaches, like load flow analysis, sensitivity factor analysis, and P–V and Q–V curves, for their merits and limitations. As a test case, IEEE 14-bus system is considered and all these approaches are implemented, confirming the limitations of different methodologies, and inferences are drawn from the merits of different approaches, which will be used as inputs for subsequent decision making. The conclusion about location is validated through multiple approaches, while the sizing of LSPV is confirmed with the help of particle swarm optimization (PSO) algorithm to affirm the decision making. From the results obtained and subsequent validations, it is identified that the integration of LSPV of size 51.8 MW at Bus 14 is the best size and location for the considered test system. This addition improves bus voltage profiles and system voltage stability by increasing critical load to 1122 MW from 1038 MW and reduces transmission line losses by 30.47%.
  PubDate: 2025-05-27
Stepwise Dynamic Control Method for A Class of Parallel Mechanism with
Constant Force Transmission
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: This paper introduces an innovative control strategy for a specially designed parallel mechanism, characterized by a constant force mapping relationship between the moving platform and the driving system. Leveraging this property, a stepwise control method is proposed that enhances the system’s control performance by meticulously addressing the interplay between the actuators and the applied driving forces. To refine the controller parameters, a parallel optimization technique is presented. This approach takes into account both the positional accuracy demands and the permissible output torque limits of the motors, thereby achieving a balance between precision and actuation capacity. Stability of the system is assured through an energy-based analysis, complemented by a geometric method for calculating energy shifts within the mechanism. Simulation studies were performed to assess the influence of various errors on system performance and to benchmark our strategy against the conventional use of a comprehensive dynamic model. Additionally, we have constructed a prototype to validate the proposed control method under real-world machining and assembly conditions. The experimental results demonstrate that our approach not only excels in terms of position tracking but also outperforms traditional methods in error detection and compensation.
  PubDate: 2025-05-27
A Recursive and Adaptive Algorithm for Harmonic and Interharmonic Analysis
in Electrical Power Systems
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: This paper presents a technique to characterize, extract, and analyse harmonic and interharmonic components in electrical power system (EPS) voltage signals. The contributions include an adaptive threshold that optimizes the number of signal samples. In addition, the treatment of amplitudes with frequencies very close to each other is performed to solve the problem that can arise when the threshold selects a high number of samples due to spectral leakage. The following frequency concerning a target frequency is defined to find the search direction, and a new correction term for the frequency drift is proposed. The methodology was validated with actual measurements and with an extensive set of synthetic signals with and without noise, created according to recommendations of national and international standards. The results demonstrate effective component characterization and high-fidelity signal reconstruction, offering a practical tool for extracting harmonic and interharmonic components from voltage signals.
  PubDate: 2025-05-23
Online Reinforcement Learning and Adaptive Control for Networked 2-DOF
Helicopters Using Smartphones and Embedded Computers
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: This paper contributes to the development of an online learning and adaptive control law for networked 2-degree-of-freedom (2-DOF) laboratory helicopters, utilizing smartphones and embedded computers. The primary objective is to address the challenges associated with controlling motion trajectories, specifically time-varying pitch and yaw angles, of fixed-base helicopters with partially known dynamics. These challenges are further amplified when coordinating multiple helicopters simultaneously using onboard embedded computer and a smartphone application. The proposed methodology employs reinforcement learning (RL) formulated as an approximate dynamic programming (ADP) problem to design an adaptive control strategy. Unlike conventional control approaches, such as linear quadratic regulators (LQR), H-infinity control, or proportional–integral–derivative control, that often assume complete knowledge of system dynamics or are validated in simulations or single-helicopter setups, the ADP technique introduced in this work leverages real-time state measurements to compute actuator commands. This enables effective tracking of reference motion trajectories without relying on full system dynamics. Additionally, the ADP framework is augmented by implementing an optimal output tracking and a linear quadratic regulator controller as benchmarks for comparative analysis. Experimental results demonstrate the efficacy of the proposed RL-based ADP control strategy in achieving accurate trajectory tracking for multiple networked helicopters in real-time. The performance comparison highlights significant improvements in adaptability and robustness over the benchmark controller. This work contributes to the field of intelligent control by demonstrating a practical and scalable approach to real-time motion trajectory control for networked multi-agent helicopter systems, validated through embedded computing and smartphone integration.
  PubDate: 2025-05-22
Resilient Dynamic Event-Triggered Fuzzy Control Against DoS Attacks on
Cyber-Physical Systems
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: This study addresses the resilient event-triggered fuzzy control of nonlinear cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. When event-triggered fuzzy control is employed, a mismatch between the premise variables of the fuzzy controller and the Takagi-Sugeno (TS) fuzzy model is induced by the event-based sampling and the data loss caused by DoS attacks. To properly deal with this issue, we propose a dynamic event-triggering mechanism that can compensate for the parameter mismatch and reduce the usage of communication resources. We also estimate the region of admissible initial conditions where convergence of the closed-loop trajectories is guaranteed even in the presence of DoS cyberattacks. The relation between the size of this region and the average duration of the DoS attack is also established. Furthermore, an optimization problem is provided to maximize resilience to DoS cyberattacks, minimize the number of transmissions, and maximize the region of attraction estimation. The proposed methodology is validated via two numerical examples.
  PubDate: 2025-05-17
A Modeling Formalism for the Identification of Reinitializable
Discrete-Event Systems with the Aim of Fault Detection
- Free pre-print version: Loading...
  Rate this result: What is this?
  
  Please help us test our new pre-print finding feature by giving the pre-print link a rating.
  A 5 star rating indicates the linked pre-print has the exact same content as the published article.
  
  Abstract: Recently, discrete-event system (DES) identification techniques with the aim of fault detection have been proposed in the literature. In simple words, identification of DES with the aim of fault detection consists in computing a model that is capable of simulating the observed fault-free behavior generated by the system. Then, a fault is detected when a discrepancy between the evolution of the system and the model estimate is observed. Although several modeling formalisms for the identification of DES have been proposed in the literature, to the authors’ knowledge, they are not capable of correctly simulating the fault-free behavior of reinitializable systems in some cases. In this paper, we propose a new modeling formalism that simulates the fault-free behavior generated by reinitializable systems in all cases. In addition, a didactic mechatronic plant is used to illustrate the proposed method.
  PubDate: 2025-05-10

HOME > Browse the 73 Subjects covered by JournalTOCs
Subject	Total Journals