In this paper, for large-scale nonlinear first-order and second-order multi-agent systems (MASs) with external disturbance, the practically finite-time H ∞ deployment (PFTHD) problem is studied via the approach based on partial differential equations (PDEs). First of all, it is assumed that the number of first-order agents (FOAs) and second-order agents (SOAs) is sufficient, then, by designing two class of special network communication protocols (NCPs) and combining T-S fuzzy technology, the tracking errors of large-scale nonlinear FOAs and SOAs can be modelled as a first-order fuzzy PDE and a second-order fuzzy PDE, respectively, through the continuum method of discrete systems. It should be pointed out that to consistent with actual communication situations, the topological weights of the NCPs are designed as sequence-dependent and Markovian switched. In addition, two fuzzy boundary control strategies are designed, and the corresponding design criteria of controller gains are obtained based on the Lyapunov method, which could ensure the PFTHD of the considered large-scale MASs with external disturbance. Finally, numerical examples are given to illustrate the effectiveness of the designed NCPs and control strategies.

With the advent of social media and networking, Tweet Sentiment Analysis (TSA) has become a significant methodology to extract useful information from the “X” platform users. The extraction of useful information from the tweets enables us to develop a decision support process from which necessary opinion mining can be carried out. These opinions help improve business models, product reviews, customer satisfaction, and thereby improve services and quality of any system or product. In this research paper, an innovative fusion of a deep recurrent neural network (DRNN) and a fuzzy decision support system (FDSS) is done to evaluate customer satisfaction based on tweets. The proposed ensemble Bi-directional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU), which are deep recurrent neural network models, were employed to attain the polarity of the tweet sentiments (positive, negative or neutral). The innovative fuzzy decision support system determines the contentment level of customers based on the tweets. FDSS with ensemble Bi-LSTM-GRU (eBi-LSTM-GRU) handles uncertainties and imprecision in tweet sentiments, enhancing performance. The developed fusion model attained an accuracy of 84.33%, 96.92% and 93.81% for the sentiment 140 dataset, the T4SA dataset and the airline Twitter dataset, respectively. The proposed fusion eBi-LSTM-GRU-FDSS model outperforms previous baseline approaches.

This study introduces an innovative hybrid power quality system designed to address power quality issues in grid-connected photovoltaic (PV) systems. The system features a transformer-less eleven-level inverter and integrates an optimized Buck–Boost (BBC) controller with an optimized Proportional–Integral–Derivative (PID) controller. The optimized PID controller output drives multiple Pulse Width Modulation (PWM) techniques, including Sinusoidal PWM (SPWM), Staircase PWM, and Trapezoidal PWM, whose performances are compared. The output is processed through a Unified Power Quality Conditioner (UPQC-PQ) to manage active and reactive power. Optimization of the PID and BBC controllers is achieved using a novel hybrid metaheuristic algorithm, Tasmanian Floral Optimization (TFO), which combines Tasmanian Devil Optimization (TDO) with the Flower Pollination Algorithm (FPO). The inclusion of a transformer-less eleven-level inverter with six switches reduces switching losses, while a comparative Total Harmonic Distortion (THD) modulation technique minimizes THD in the output voltage. The proposed system is implemented and evaluated in Matlab/Simulink with comparative analysis showing its superior performance over existing methods. This makes it a highly effective solution for enhancing power quality in grid-connected PV systems.

Since the launch of the post-quantum cryptography standardization project by NIST, post-quantum cryptography has become a prominent research area. Non-commutative cryptography constructed using NP-complete problems is widely regarded resistant to quantum computing attacks, so it has become an important branch of post-quantum cryptography. Nonetheless, the existing non-commutative cryptographic protocol still exhibits certain shortcomings. In this paper, a non-commutative combinatorial semigroup with matrix power function is constructed from a modified medial semigroup by the method of semidirect product, and then a key exchange protocol is developed on it to provide a kind of novel non-commutative cryptographic protocol. Due to the non-commutativity of cryptographic platform, the proposed protocol could perform well in respect of antiquantum computing attack, which is superior to traditional cryptographic protocol. In addition, the security analysis shows that the protocol has significant advantages in resisting algebraic and brute force attacks, as well as aganist quantum cryptanalysis; the complexity analysis demonstrates that computation and storage complexities are of polynomial order, ensuring efficient operation even for large matrix sizes.

This study delves into fault-tolerant control strategies for underactuated surface vessels (USVs) in the context of global tracking control. Targeting the challenge of lateral drive vector alignment in USVs, this paper successfully constructs an effective lateral drive strategy by introducing supplementary control means. At the same time, a robust neural network (NN) adaptive technology is proposed to accurately reconstruct the unknown dynamic behaviour of the ship. A finite-time disturbance observer (FTDO) is developed to real-time reconstruct centralized uncertainties, encompassing unknown external disturbances and approximation errors. This method achieves fast and accurate online reconstruction of centralized uncertainties, reduces the dependence on accurate multi-system vehicle motion models, and thus expands the application scope of online disturbance estimation technology. The study conducts a rigorous stability analysis of the closed-loop system using Lyapunov stability theory. The effectiveness of the proposed control scheme in achieving global tracking is validated through comprehensive simulations.

During repeated rehabilitation training, the friction between the omnidirectional wheel and the desktop can lead to control response delays, causing deviations in the training trajectory. To enhance the training trajectory accuracy of rehabilitation robots in the presence of friction interference, a backstepping double-integral terminal sliding mode controller (BDITSMC) based on an extended state observer is proposed. An integral term was introduced into the terminal sliding surface to eliminate the arrival phase and minimize the estimation error. Meanwhile, the switching frequency in the sliding mode phase was reduced, thereby addressing the “complexity explosion” problem in inversion design. This method provides a solution to improve the trajectory tracking accuracy of upper limb rehabilitation robot controllers. In the rehabilitation trajectory tracking control, quantified by the Mean Absolute Error (MAE), the error of the backstepping integral non-singular terminal sliding mode control (BINTSMC) method is 0.0039, while the BDITSMC proposed in this paper reduces this value to 0.003, representing a 23.1% improvement.

The Industrial Internet of Things (IIoT) revolutionize industries such as manufacturing, logistics, energy, and healthcare by merging smart sensors and devices with sophisticated network connectivity and advanced data analysis. Digital Twin As a Service (DTaaS) for Internet of Healthcare Things (IoHT) in the healthcare industry opens exciting opportunities to create virtual replicas of real healthcare systems and assets. Digital twins relying on cloud platforms, such as Amazon Web Services, Microsoft Azure, and Google Cloud, provide several vital capabilities, including data informed decision making, personalized patient simulation, up to 30% decrease in equipment downtime with predictive maintenance, and operational efficiency of more than 25% through real-time remote monitoring. This paper proposes an all encompassing methodology towards the development and deployment of compositional digital twins utilizing services applied towards assisting the visually challenged with a smart stick. Federated learning has been proposed as one potential approach that could help in preserving the privacy of clients, particularly concerning the protection of patient's confidential information. One of the possible healthcare scenario that demonstrates how digital twin technology guiding visually impaired individuals, with a possible enhancement in the success rate of mobility by 40%.

To address the position and velocity tracking control problems of high-speed trains (HSTs), a neural network embedded fault-tolerant control (FTC) method is proposed in this paper. The unknown resistances and interactive forces between the connected carriages are taken into account. The stability of the neural networks (NNs) embedded FTC is proved by a common formal derivative of Lyapunov function, in which an NN-embedded item is integrated with a base controller which is stable for the system. On account of the system uncertainties and actuator faults, a value adaptive sliding mode control for estimating equivalent term composed of the unknown nonlinear terms and the disturbance is used and the base FTC is designed based on this method. The results of simulations show that the method of NN embedded optimization technology proposed in this paper can compensate and optimize the performance of the base FTC with only a few conditions. In the absence of actuator faults, NN-embedded FTC proposed in this paper reduces position error by about 5 % and velocity error by 94 % . In case of actuator faults, it reduces position error by about 3 % and velocity error by 71 % .

This paper proposes an adaptive backstepping control scheme for a class of nonlinear systems with dead-zone input and unknown external disturbances in each state equation. A nonlinear approximation function is constructed for the dead-zone hysteresis. The derivative of this approximation function is cascaded with the plant to form the extended system. The Nussbaum function is used to deal with the difficulty caused by the derivative of the approximate function. Unlike existing methods, the proposed controller fully considers the non-smooth nonlinearity without requiring its inverse. It is shown that the proposed adaptive control scheme ensures all signals in the closed-loop system remain bounded.

Cybersecurity threats pose a significant risk to IoT-based smart healthcare technologies by compromising patient safety, disrupting services, and exposing sensitive health data to unauthorized access and misuse. This research aims to strengthen data integrity and confidentiality in smart healthcare systems by developing edge-level cryptographic strategies tailored for IoT-enabled edge environments, addressing the security and privacy challenges of resource-constrained devices. The proposed methodology Cryptographic Security Framework with SignaVault Authentication (CSFVA) integrates lightweight cryptographic techniques with edge computing to secure healthcare data efficiently and in real time. The novelty of this research lies in the unified implementation of a layered cryptographic pipeline, comprising Elliptic Curve Cryptography (ECC) for encryption, a Secure Hash Crypto Technique (SHCT) for data integrity, and a Signa-Vault (SV) authentication mechanism for user and device verification. This tri-layered approach ensures data confidentiality, integrity, and authenticity while sup porting the low-latency requirements of edge computing environments. Performance evaluation shows the model's efficiency, achieving a processing time of 5.81 seconds, memory use of 45.78 MB, power consumption of 4.2 W, and throughput of 99.67%. These results indicate that the proposed solution effectively balances security and resource efficiency, making it suitable for resource-limited IoT healthcare and scalable smart healthcare systems.