This article proposes a resilient control framework for securing cyber-physical systems (CPSs), specifically addressing nonlinear descriptor systems operating under communication constraints and subject to sensor and actuator attacks. We integrate Takagi-Sugeno (T-S) fuzzy models with a Q-learning-based event-triggered mechanism (ETM) and adopt a sliding-mode control strategy to establish a resilient security architecture that adaptively balances operational efficiency with robust protection against cyber-physical threats. A major contribution of this work lies in designing an adaptive fuzzy sliding-mode observer (SMO) with mismatched premise variables for the estimation of compromised system states. Additionally, a sliding-mode controller (SMC) is synthesized to maintain closed-loop admissibility and ensure the reachability of sliding surfaces. We advance beyond the existing approaches by employing the secretary bird optimization algorithm (SBOA) to optimize controller and observer gains, thereby solving the nonconvex optimization challenges present in controller and observer design. The effectiveness of the proposed method is validated through extensive Monte Carlo simulations on a truck-trailer system. These simulations demonstrate the efficacy of the approach in maintaining system stability and performance under various attack scenarios, thereby making a significant contribution to the security of nonlinear systems in networked environments.

This article proposes a resilient control framework for securing cyber-physical systems, specifically addressing large-scale interconnected systems operating over constrained networks subject to communication delays and deception attacks. A dynamic memory event-triggered mechanism (DMETM) is proposed to reduce network load while preserving control performance. The proposed sliding mode control (SMC) scheme combines an output-dependent integral sliding surface with a full-order dynamic output feedback compensator (DOFC) to enhance system robustness against external disturbances, matched nonlinear perturbations, and to mitigate cyberattack effects, while ensuring the sliding mode motion. Sufficient conditions are derived via a Lyapunov–Krasovskii functional to guarantee uniform ultimate boundedness (UUB) and a prescribed H∞ performance level. A homotopy iterative algorithm is adopted to address the computational challenges of the resulting bilinear matrix inequalities (BMIs) and to co-design the controller and DMETM parameters. Extensive Monte Carlo simulations on an LSS platform demonstrate the framework’s capacity for maintaining resilience and stability against adversarial attacks.

Inflation, defined as the trend of the continuous increasing of the general level of prices within a country's economy during a time period, affects both the private and public sectors of the economy. Policy makers have the need to control and stabilize the rate of inflation at low levels to achieve economic growth and prosperity. Particularly, they use the rate of inflation as a measure to diagnose economic problems and then to apply the corresponding macroeconomic policies. So, inflation rate forecasting must be accurate, and the measurement of the inflation rate, which is usually dependent on the consumer price index (CPI), should be as accurate as possible. Although there are many different ways to anticipate the CPI, the most accurate methods are those that use artificial neural network models. These methods usually outperform the traditional statistical-based forecasting techniques. This study spans the period from January 2015 to December 2024 using monthly, non-seasonally adjusted data from the Organization for Economic Co-operation and Development (OECD). Since WASD (weights and structure determination) neural networks have been demonstrated to address the drawbacks of traditional back-propagation neural networks, like poor training speed and local minimum, a three-layer power-activation WASD neural network model, termed WASDCPI, is taken into consideration. The WASDCPI model performs better than other well-known machine learning techniques for predicting the CPI of countries, including the USA, UK, Germany, France, India, Switzerland, Korea, and the Slovak Republic. All the data analysis is being conducted using the MATLAB environment.

Scholars have put a lot of emphasis on time-varying linear matrix equations (LMEs) problems because of its importance in science and engineering. The problem of determining the time-varying LME's minimum-norm least-squares solution (MLLE) is therefore tackled in this work. This is achieved by the use of NHZNN, a recently developed neutrosophic logic/fuzzy adaptive high-order zeroing neural network technique. The NHZNN is an advancement on the conventional zeroing neural network (ZNN) technique, which has shown great promise in solving time-varying tasks. To address the MLLE task for arbitrary-dimensional time-varying matrices, three novel ZNN models are presented. The models perform exceptionally well, as demonstrated by two simulation studies and two real-world applications to acoustic source tracking.

This paper proposes a novel approach to handling random actuator failures in control systems through robust and reliable control techniques. The investigation seeks to establish a framework for assessing the l(2) -l(infinity) stability characteristics of nonlinear Markov jump singularly perturbed systems (MJSPSs) using an output Takagi-Sugeno (TS) fuzzy-based controller. The study focuses on implementing a component-based dynamic event-triggered mechanism (CBDETM), including quantization, to investigate the transmission of the outputs across networked communication channels. The proposed approach is based on the following key attributes: 1) An event-triggered mechanism that establishes a suitable condition for independent signal transmission from each sensor node to the controller; 2) A stochastic failure model that describes actuator failures; 3) A non-stationary Markov chain that reflects the asynchronous relationship between the system and controller modes; 4) A mode-dependent Lyapunov function is used to establish sufficient conditions to show that the resultant closed-loop system is stochastically mean-square stable with a gamma level of l(2) -l(infinity) performance index; 5) The theoretical findings are validated by a real numerical example of the Van Der Pol circuit, confirming the effectiveness of the proposed strategy.

the last few years, researchers have concentrated on estimating and maximizing the Domain of Attraction of autonomous nonlinear systems. Based on the Lyapunov theory, the proposed approach in this paper aims to give an accurate estimation of the Domain of Attraction with high performance against the existing conventional methods. The Adaptive Sine-Cosine Algorithm has been considered one of the most advanced algorithms. It combines a large exploration with a strong local search and provides high-quality convergence conditions. This paper uses the benefits of the Adaptive Sine-Cosine Algorithm to develop a flexible method to estimate the Domain of Attraction by an oriented sampling to guarantee the largest sublevel related to the given Lyapunov function. The approach is applied to some benchmark examples and validates its efficiency and its ability to provide performant results.

With the aim of accelerating renewable energy integration and meeting Net-zero targets, maximizing the efficiency of photovoltaic (PV) systems is paramount. This necessity has driven researchers' attention to investigate advances in crucial techniques like maximum power point tracking (MPPT). This paper investigates and compares two approaches for optimizing the PV power generation. The first is the hybrid JAYA-SMC algorithm. JAYA is a simple and effective population-based technique like genetic algorithms, particle swarm optimization, and differential evolution. Sliding Mode Control (SMC) features a simple algorithm and a high degree of robustness. JAYA and SMC are combined for MPPT. The second approach is fuzzy logic control (FLC), which optimizes the power consumption of a PV system by adjusting the converter duty cycle, allowing the system to operate at its maximum efficiency under varying conditions. This study provides a critical evaluation of these two methods for MPPT applications, offering a clear framework for researchers to select the most appropriate control strategy based on the distinct demands of their target application.

The last few years marked remarkable progress in developing stability analysis techniques directed at nonlinear systems. In this context, this work presents a Lyapunov-based method to estimate the region of stability of nonlinear systems. Based on LMI techniques and JAYA optimization, this work provides a maximized volume of the DA. The LMI technique is used to get the largest estimation of the DA from a given Lyapunov function and input controller. The JAYA algorithm is introduced to attain a maximized volume of the DA through an optimal selection of the Lyapunov function's parameters and input controller coefficients.

This paper investigates a novel secure control scheme for a particular class of Takagi-Sugeno (TS) fuzzy singular systems susceptible to deception attacks. During these attacks, adversaries can randomly introduce erroneous data into the output and control signals. The proposed strategy addresses the impact of attacks and disturbances using an observer-based sliding mode control (SMC) approach. Moreover, an event-triggering protocol is implemented to manage network resources efficiently. Furthermore, by employing the stochastic Lyapunov theory and the finite-time analysis method, sufficient conditions are established to ensure the finite-time boundedness of the resulting closed-loop system throughout both the reaching and sliding motion phases. To mitigate the attack's effects and improve the system's performance, the Secretary Bird Optimization Algorithm (SBOA) with the linear matrix inequality (LMI) is explored as a new approach for designing the optimal gains of controllers and observers. Finally, a simulation study based on a disc rolling on a surface is performed to showcase the efficacy and resilience of the proposed control scheme.

This paper addresses the problem of cyber-security for nonlinear singularly perturbed systems when false data injection occurs on the sensors and actuators. The interval type 2 (IT2) fuzzy approach is investigated as a suitable model to design a sliding mode controller able to cope with cyber attacks. To begin with, a switching function is defined, and a sliding mode control (SMC) law is developed to drive the system’s state trajectories towards the sliding domain around the sliding surface. Then, the stability and reachability properties are demonstrated using the Lyapunov approach. Moreover, to address nonlinear constraints in the SMC design problem, the dandelion optimization algorithm (DOA) is employed in conjunction with linear matrix inequality (LMI) approach. In the end, a concrete demonstration is conducted to demonstrate the effectiveness of the proposed approach regarding the tunnel diode circuit application.

As the global demand for renewable energy sources increases, reliable and efficient photovoltaic (PV) systems are positioning themselves as one of the most promising candidates among sustainable energy resources. Accurate modelling of photovoltaic generators is essential to enhance their energy conversion efficiency, making this need even more paramount. Precise parameter extraction from PV generator models not only aids in optimizing performance but also plays a crucial role in fault diagnosis and maximum power point tracking. Metaheuristic optimization techniques are essential to address the challenges posed by the nonlinearity of PV cell models that are adversely affected and more complicated by shading, temperature and irradiance variations, and other environmental factors. This paper introduces a novel approach to extract key parameters of double diode model (DDM) of PV units using the recently developed Puma optimizer (PO), which has not been applied previously in this context. A new effort of using an improved version of PO when the Lambert W-function (POLam) is employed for truthful calculus of PV unit current is investigated. Lambert W-function is employed for the precise solving of the I-V curve in the DDM non-linear equations as an alternative to the iterative Newton-Raphson approach. Extensive simulations are conducted to validate the effectiveness of the PO and the POLam, comparing their performance against twelve well most cited and newest metaheuristic optimization techniques. The results obtained for wide-spread RTC france and aSi PV cells and the LSM 20 PV module demonstrate that POLam significantly reduces the Root Mean Square Error (RMSE) metric. It achieves the best RMSE of 7.218852E-04, 3.927434E-05 and 1.000744E-03 for RTC Cells, aSi Cells and LSM 20 Module, respectively. The results confirm the superior performance and accuracy of POLam compared with the existing approaches, revealing that it provides more reliable parameter estimates and helps to improve energy conversion efficiency in photovoltaic applications.

This paper proposes a method for estimating the largest domain of attraction for nonlinear systems with saturated inputs. By identifying this domain, the stability of the system can be ensured and the global controlled system’s performance can be designed under real-world constraints. Enlarging the attraction domain is critical to designing robust control systems that handle practical scenarios efficiently.This work uses JAYA algorithms and linear matrix inequality conditions to determine the optimal state space that maximizes the estimation of the large region of attraction. To demonstrate the effectiveness of the proposed approach, numerical results and analysis are presented.

Drones are highly autonomous, remote-controlled platforms capable of performing a variety of tasks in diverse environments. A digital twin (DT) is a virtual replica of a physical system. The integration of DT with drones gives the opportunity to manipulate the drone during a mission. In this paper, the architecture of DT is presented in order to explain how the physical environment can be represented. The techniques via which drones are collecting the necessary information for DT are compared as a next step to introduce the main methods that have been applied in DT progress by drones. The findings of this research indicated that the process of incorporating DTs into drones will result in the advancement of readings from all sensors, control code and intelligence. This can be executed on the DTs, remote control for the performance of complex tasks in a variety of application environments, and simulation on the DTs without having an effect on the actual drone. On the other hand, in order to develop three-dimensional representations of structures and construction sites, a method known as photogrammetry is used to generate these models employing drones as aerial scanners. In spite of this, there are a number of technological and social-political obstacles that should be taken in consideration. These challenges include the interoperability of different sensors, the creation of efficiently optimized data processing algorithms, and concerns over data privacy and security.

This paper introduces a novel Runge–Kutta (RK) pair of orders 8(6) designed specifically for solving linear inhomogeneous initial value problems (IVPs) with constant coefficients. The proposed method requires only 11 stages per iteration, a significant improvement over conventional RK pairs of orders 8(7), which typically demand 13 stages. The reduction in stages is achieved by leveraging a smaller set of order conditions tailored to linear inhomogeneous problems, where traditional simplification techniques are not applicable. To address the complexity of deriving such methods, the authors employ the Differential Evolution algorithm, a global optimization technique, to solve the resulting system of equations. The new RK pair, named NEW8(6)Lin, is tested on several benchmark problems, including scalar, linear inhomogeneous, and larger systems, demonstrating a superior performance in terms of accuracy and computational efficiency. The method’s high phase-lag accuracy and efficiency make it particularly suitable for problems requiring high precision over extended intervals. The coefficients of the method are provided with high precision, enabling direct implementation in computational environments like Mathematica. The results highlight the method’s potential as a robust tool for solving linear inhomogeneous IVPs, offering a balance between computational cost and accuracy. This work contributes to the ongoing development of specialized numerical methods for differential equations, particularly in scenarios where traditional approaches struggle with efficiency or stability.

Optimal Power Flow (OPF) is a critical challenge in electrical engineering, necessitating efficient and resilient optimization techniques for successful power distribution management. This study presents COGWO, an innovative hybrid metaheuristic that integrates the Grey Wolf Optimizer (GWO) with the Cuckoo Optimization Algorithm (COA) to enhance convergence quality and solution resilience. Before its implementation in OPF issues, the suggested technique was thoroughly verified against standard engineering problems in CEC2020, continuously surpassing several state-of-the-art methods. Subsequently, COGWO was utilized to tackle OPF issues in the IEEE 30-bus and 118-bus systems, accounting for the fluctuation of renewable energy sources (RESs), such as wind and solar, in conjunction with traditional power network configurations. The method exhibits an optimal balance between exploration and exploitation, successfully minimizing fuel costs, power loss, voltage variation, and emissions, even in the presence of intricate non-convex and non-smooth optimization functions. A comparative examination with COA, GWO, and other modern metaheuristics demonstrates the advantage of COGWO in attaining high-quality global solutions characterized by improved solution stability and convergence speed. When it comes to optimizing power systems on a grand scale, COGWO is an attractive solution due to its computational efficiency, flexibility, and resilience.

This paper presents a bi-level optimization approach to enlarge the region of attraction of nonlinear, nonpolynomial systems using Lyapunov functions. The inner level estimates the region by identifying the largest sublevel set with a negative time derivative, while the outer level optimizes the Lyapunov function parameters to maximize this region. Both levels are solved using particle swarm optimization, providing a flexible search. The proposed method systematically constructs Lyapunov functions with the largest possible region of attraction, offering improved stability guarantees compared to existing approaches.

This paper examines the concept of implementing a hybrid optimization approach through combining analytical and meta-heuristic approaches to improve the performance of practical engineering systems. Designed in support of artificial intelligence strategy, the proposed approach ensures high stability and efficiency under actuators saturation constraint. This is a well-known and sensitive problem in robotics and control. Specifically, this paper deals with the problem of computing the stability region for controlled systems. While addressing this issue, research approaches take into consideration the fact that actuator saturation may occur. It is imperative to maintain this propriety and ensure the reliability of design control systems, particularly those developed to control robot actuators. Models of the studied systems are based on differential algebraic representations and polytypic regions in state space. The developed technique combines LMI with an improved meta-heuristic based optimization approach that fast searches and enlarge domains of attraction for robot actuators. The direct Lyapunov theory is used to analyze and validate stability key performance. A numerical example study has been conducted to validate the proposed approach's efficacy and efficiency. A comparative benchmarking study has been carried out to highlight the main concepts and results of this study.

Drones play a vital role in the fundamental aspects of Industry 4.0 by converting conventional warehouses into intelligent ones, particularly in the realm of barcode scanning. Various potential issues frequently arise during barcode scanning by drones, specifically when the drone camera has difficulty obtaining distinct images due to certain factors, such as distance, capturing the image whilst flying, noise in the environment and different barcode dimensions. In addressing these challenges, this study proposes an approach that combines a proportional–integral–derivative (PID) controller with image processing techniques. The PID controller is responsible for continuously monitoring the camera’s input, detecting the difference between the planned and the real barcode image dimensions, and making immediate changes to the drone position to improve the process of detecting the potential barcode. The aforementioned procedure is implemented on the DJI Tello drone to verify the practical performance of the methodology introduced in this study. Results showed that drones can achieve remarkable barcode scanning performance by incorporating sophisticated computer vision technologies into PID controllers. PID computer vision algorithms are capable of analysing visual data acquired from the drone’s cameras and retrieving barcode information under a variety of situations, such as the size of the barcode, location of the barcode and noise of the warehouse environment.

In this paper, we introduce a new family of tenth-order hybrid Numerov-type methods designed for solving second-order initial value problems (IVPs) with enhanced accuracy and efficiency. Our proposed methods build on the foundation of existing high-order Numerov-type techniques and incorporate advanced strategies for step size adaptation and error control. We provide a comprehensive analysis of the theoretical underpinnings of these methods and validate their performance through extensive numerical experiments.

Recently, the focus on ecology and sustainable development has spurred the growth and development of fuel cells. In addition to alternative production sources, the proton exchange membrane fuel cell has become a promising source for producing both electricity and heat by cogeneration fora variety of stationary and onboard applications, requiring precise modeling and consistent control. This paper proposes a metaheuristic optimization algorithm that overcomes the non-linearities of the PEMFC model and estimates its key parameters. The algorithm proposed for the first time is called the Dimension Learning-based Modified Grey Wolf Optimizer (DLHMGWO). It enhances the traditional GWO by incorporating anew DLH strategy that addresses the exploitation-exploration trade-off, the lack of population diversity, premature convergence, and the falling into local maxima, ultimately minimizing the sum of squared error (SSE) between the experimental and estimated voltage-current points to yield a satisfactory fit with minimized parameter uncertainties. The effectiveness of the proposed DLHMGWO approach is validated through the experimental investigation of five commercialized PEMFC systems. The performance of the DLHMGWO approach is compared to eleven well- established and newly published algorithms including the original GWO and four of its extended versions. The results of a deep statistical study demonstrate that the DLHMGWO can achieve high accuracy, reliability, convergence speed, SSE value, and fitting of the estimated key parameters. This is evidenced by the lowest SSE values obtained: 0.61436 for the 250 W PEMFC, 0.01161 for the BCS-500 W, 1.05593 for Temasek 1 kW, 0.78489 for AVISTA SR-12 500 W, and 0.17689 for Heliocentris FC50. This highlights the DLHMGWO's exceptional ability to precisely estimate the parameters of PEMFC stacks.