Search Results (1,120)

Designing provably safe controllers for high-dimensional nonlinear systems with formal guarantees represents a fundamental challenge in control theory. While control barrier functions (CBFs) provide safety certificates through forward invariance, manually crafting these barriers for complex systems becomes intractable. Neural network approximation offers expressiveness but traditionally lacks formal guarantees on approximation error and Lipschitz continuity essential for safety-critical applications. This work establishes rigorous theoretical foundations for learned barrier functions through explicit probabilistic bounds relating neural approximation error to safety failure probability. The framework integrates Lipschitz-constrained neural networks trained via PAC learning within multi-objective model predictive control. Three principal results emerge: a probabilistic forward invariance theorem establishing $\mathbb{P}\left(\mathrm{violation}\right)\le T{\delta }_{\mathrm{local}}+exp(-h_{min}^2/\left(2L^{2}T{\sigma }^2\right))$, explicitly connecting network parameters to failure probability; sample complexity analysis proving $\mathcal{O}\left(N^{-1/4}\right)$ safe set expansion; and computational complexity bounds of $\mathcal{O}\left(H^3{m}^3\right)$ enabling 50 Hz real-time control. An experimental validation across 648,000 time steps demonstrates a 99.8% success rate with zero violations, a measured approximation error of $\sigma =0.047$ m, a matching theoretical bound of $\sigma \le 0.05$ m, and a 16.2 ms average solution time. The framework achieves a 52% conservatism reduction compared to manual barriers and a 21% improvement in multi-objective Pareto hypervolume while maintaining formal safety guarantees. Full article

Electromyography (EMG) has become essential in biomedical engineering, rehabilitation, and human–machine interfacing due to its ability to capture neuromuscular activation for control, monitoring, and diagnosis. Recent advances in sensing hardware, high-density and flexible electrodes, and embedded acquisition modules combined with modern signal processing and machine learning have significantly enhanced the robustness and applicability of EMG-based systems. This review provides an integrated overview of EMG generation, acquisition standards, and preprocessing techniques, including adaptive filtering, wavelet denoising, and empirical mode decomposition. Feature extraction methods across the time, frequency, time–frequency, and nonlinear domains are compared with respect to computational efficiency and suitability for real-time systems. The review synthesizes classical and contemporary pattern-recognition approaches, from statistical classifiers to deep architectures such as CNNs, RNNs, hybrid CNN–RNN models, transformer-based networks, and graph neural networks. Key challenges, including signal non-stationarity, electrode displacement, muscle fatigue, and poor cross-user or cross-session generalization, are examined alongside emerging strategies such as transfer learning, domain adaptation, and multimodal fusion with IMU or FMG signals. Finally, the paper surveys rapidly growing EMG applications in prosthetics, rehabilitation robotics, human–machine interfaces, clinical diagnostics, and sports analytics. The review highlights ongoing limitations and outlines future pathways toward robust, adaptive, and deployable EMG-driven intelligent systems. Full article

This study investigated heat-induced protein aggregation in skim camel milk by monitoring changes in the volume-weighted mean particle size (d_4,3) during isothermal heating (60–90 °C, up to 60 min, four temperature levels and 25 time–temperature conditions). Pronounced increases in d_4,3 with both time and temperature confirmed significant thermal aggregation. The reaction kinetics were described using a generalized exponential growth model, which fitted well at intermediate temperatures (e.g., coefficient of determination (R²) = 0.901 at 70 °C and 0.959 at 80 °C) but deviated at the lower (60 °C) and upper (90 °C) extremes, reflecting more complex behavior. Arrhenius analysis of the rate constant yielded an activation energy of 50.61 kJ mol⁻¹, lower than values typically reported for bovine milk systems, indicating that camel milk proteins require less thermal input to aggregate. In parallel, a machine learning model implemented as an artificial neural network (ANN) predicted d_4,3 from time-temperature inputs with high accuracy (R² > 0.97 across training, validation, and testing), capturing nonlinear patterns without mechanistic assumptions. Together, the kinetic and ANN approaches provide complementary insights into the heat sensitivity of camel milk proteins and offer predictive tools to support the optimization of thermal processing, formulation, and quality control in dairy applications. Full article

Carbon capture and storage (CCS) is essential for achieving net-zero emissions, yet amine-based capture systems face significant challenges including high energy penalties (20–30% of power plant output) and operational costs ($50–120/tonne CO${}_2$). This study develops and validates a novel multi-scale Digital Twin (DT) framework integrating Physics-Informed Neural Networks (PINNs) to address these challenges through real-time optimization. The framework combines molecular dynamics, process simulation, computational fluid dynamics, and deep learning to enable real-time predictive control. A key innovation is the sequential training algorithm with domain decomposition, specifically designed to handle the nonlinear transport equations governing CO${}_2$ absorption with enhanced convergence properties.The algorithm achieves prediction errors below 1% for key process variables (R${}^2$> 0.98) when validated against CFD simulations across 500 test cases. Experimental validation against pilot-scale absorber data (12 m packing, 30 wt% MEA) confirms good agreement with measured profiles, including temperature (RMSE = 1.2 K), CO${}_2$ loading (RMSE = 0.015 mol/mol), and capture efficiency (RMSE = 0.6%). The trained surrogate enables computational speedups of up to four orders of magnitude, supporting real-time inference with response times below 100 ms suitable for closed-loop control. Under the conditions studied, the framework demonstrates reboiler duty reductions of 18.5% and operational cost reductions of approximately 31%. Sensitivity analysis identifies liquid-to-gas ratio and MEA concentration as the most influential parameters, with mechanistic explanations linking these to mass transfer enhancement and reaction kinetics. Techno-economic assessment indicates favorable investment metrics, though results depend on site-specific factors. The framework architecture is designed for extensibility to alternative solvent systems, with future work planned for industrial-scale validation and uncertainty quantification through Bayesian approaches. Full article

In new distribution systems with high penetration of renewable energy, inverter-based sources exhibit significant differences in fault characteristics compared to traditional power sources due to the absence of a constant electromotive force and their operation under nonlinear control links, rendering conventional fault current calculation methods inadequate. To address these challenges, a full-time-domain analysis-based method for modelling and calculating fault transient characteristics is proposed. First, a dynamic model of inverter-based sources accounting for current loop saturation effects is established, and phase plane analysis is employed to resolve nonlinear control regions. On this basis, a full-time-domain fault current calculation method is proposed, wherein the steady-state operating point after a fault is determined by iteratively solving the network node voltage equations. By integrating control strategies and derived transient differential equations, the fault current expression across the full-time-domain scope is formulated. Furthermore, a multi-objective optimization control strategy is proposed to achieve effective fault current suppression, and an improved Simulated Annealing-Particle Swarm Optimization (SA-IPSO) hybrid algorithm is adopted for efficient solution. Finally, SIMULINK-based simulation experiments validate the accuracy and effectiveness of the proposed method in transient characteristic analysis and current suppression. Full article

For marine development in harsh sea states, floating-body salvage equipment serves as critical support infrastructure. Aiming at the challenges of nonlinear dead-zone, model uncertainty, and actuator failures in the wave compensation systems of such equipment, this paper proposes an intelligent fault-tolerant control method based on neural networks. First, the dead-zone nonlinearity of the hydraulic system is compensated using an inverse model approach. Then, neural networks are employed to online learn unmodeled dynamics, while adaptive laws are designed to handle partial actuator failures and Lyapunov theory is used to prove the global stability of the closed-loop system, effectively enhancing the robustness and fault-tolerance of the wave compensation system under complex sea conditions. Unlike existing studies that rely on accurate system models, the proposed method integrates data-driven learning with model-based compensation. This integration enables adaptive handling of wave disturbances, model uncertainties, and actuator faults, thereby overcoming the strong model dependence and complex observer design inherent in traditional sliding-mode fault-tolerant control. Simulation and experiment results show that the method ensures high-precision dynamic tracking and compensation performance under various sea conditions. Full article

This paper concentrates on fractional-order fuzzy cellular neural networks (FOFCNNs) with bounded uncertainties and transmission delays. To better capture real-world dynamic behaviors, the fuzzy AND and OR operators are employed to construct drive-response systems. For the fixed-time synchronization task of the systems, a novel multi-module feedback controller incorporating three functional terms is designed. These terms aim to eliminate delay effects, ensure fixed-time convergence, and reduce parameter conservativeness. Leveraging the properties of fractional-order operators and our multi-module control scheme, new synchronization criteria of the studied drive-response systems can be established within a predefined time. An upper bound on the settling time is derived, depending on the system size and control parameters, but independent of the initial conditions. A significant corollary is derived for the case of no uncertainties under the nonlinear controller. Numerical experiments discuss the impact of uncertainties and delays on synchronization, and confirm the validity of the results presented in this study. Full article

With the development of aerospace technology, hypersonic flight vehicles are evolving towards larger size, lighter weight, and higher performance. Their cross-domain maneuverability and extreme flight environment led to the rigid–flexible coupling effect and became the core bottleneck restricting performance improvement, seriously affecting flight stability and control accuracy. This paper systematically reviews the research status in the field of control for high-speed rigid–flexible coupling aircraft and conducts a review focusing on two core aspects: dynamic modeling and control strategies. In terms of modeling, the modeling framework based on the average shafting, the nondeformed aircraft fixed-coordinate system, and the transient coordinate system is summarized. In addition, the dedicated modeling methods for key issues, such as elastic mode coupling and liquid sloshing in the fuel tank, are also presented. The research progress and challenges of multi-physical field (thermal–structure–control, fluid–structure–control) coupling modeling are analyzed. In terms of control strategies, the development and application of linear control, nonlinear control (robust control, sliding mode variable structure control), and intelligent control (model predictive control, neural network control, prescribed performance control) are elaborated. Meanwhile, it is pointed out that the current research has limitations, such as insufficient characterization of multi-physical field coupling, neglect of the closed-loop coupling characteristics of elastic vibration, and lack of adaptability to special working conditions. Finally, the relevant research directions are prospected according to the priority of “near-term engineering requirements–long-term frontier exploration”, providing Refs. for the breakthrough of the rigid–flexible coupling control technology of the new-generation high-speed aircraft. Full article

The residential energy sector contributes substantially to global energy-related emissions. Effective energy management requires an understanding occupant behavior through activity detection and habit identification. Recent advances in artificial intelligence (AI) and machine learning (ML) enable the automatic detection of user activities and prediction of energy needs based on historical consumption data. Non-intrusive load monitoring (NILM) facilitates device-level disaggregation without additional sensors, supporting demand forecasting and behavior-aware control in Home Energy Management Systems (HEMSs). This review synthesizes various AI and ML approaches for detecting user activities and energy habits in HEMSs from 2020 to 2025. The analyses revealed that deep learning (DL) models, with their ability to capture complex temporal and nonlinear patterns in multisensor data, achieve superior accuracy in activity detection and load forecasting, with occupancy detection reaching 95–99% accuracy. Hybrid systems combining neural networks and optimization algorithms demonstrate enhanced robustness, but challenges remain in limited cross-building generalization, insufficient interpretability of deep models, and the absence of dataset standardized. Future work should prioritize lightweight, explainable edge-ready models, federated learning, and integration with digital twins and control systems. It should also extend energy optimization toward occupant wellbeing and grid flexibility, using standardized protocols and open datasets for ensuring trustworthy and sustainability. Full article

Drinking-water treatment systems must effectively control natural organic matter (NOM), a major precursor of regulated disinfection by-products (DBPs). Specific ultraviolet absorbance (SUVA) is widely used as an operational surrogate for NOM aromaticity and hydrophobicity; however, ozonation and subsequent filtration can disrupt the linear relationship between SUVA and trihalomethane formation potential (THMFP). This study evaluates whether SUVA can reliably predict THMFP under two ozonation configurations frequently applied in drinking-water treatment: pre-ozonation prior to coagulation–filtration and final ozonation following filtration. Experimental data were analyzed using conventional linear regression and artificial neural network (ANN) models, with SUVA employed as the sole predictor variable. Across all treatment configurations, reductions in SUVA were consistently more pronounced than corresponding decreases in THMFP, indicating a decoupling between chromophoric loss and chlorine-reactive precursor dynamics under ozonation-dominated conditions. Linear regression models exhibited only moderate predictive performance (R² = 0.63–0.76), reflecting the limitations of proportional surrogate-based approaches when NOM undergoes oxidative and adsorptive transformation. In contrast, single-parameter ANN models captured the nonlinear SUVA–THMFP relationship with substantially higher accuracy across both pre- and final-ozonation regimes (R² = 0.88–0.99), successfully resolving process-dependent patterns embedded within optically compressed SUVA signals. These findings demonstrate that, although SUVA alone cannot linearly represent the multistep transformation of NOM during ozonation and adsorption, it retains process-relevant structure information on DBP precursor reactivity that can be effectively extracted using nonlinear modelling. The results highlight the potential of integrating ANN-driven tools into advanced monitoring and DBP-control strategies in modern drinking-water treatment systems. Full article

Modern power distribution networks are increasingly challenged with nonlinear operating conditions, the high penetration of distributed energy resources, and conflicting operational objectives such as loss minimization and voltage regulation. Existing load-flow optimization approaches often suffer from slow convergence, premature stagnation in non-convex search spaces, and limited robustness when handling conflicting multi-objective performance criteria under fixed network constraints. To address these challenges, this paper proposes a Fractional Multi-Objective Load Flow Optimizer (FMOLFO), which integrates a fractional-order numerical regularization mechanism with an adaptive Pareto-based Differential Evolution framework. The fractional-order formulation employed in FMOLFO operates over an auxiliary iteration domain and serves as a numerical regularization strategy to improve the sensitivity conditioning and convergence stability of the load-flow solution, rather than modeling the physical time dynamics or memory effects of the power system. The optimization framework simultaneously minimizes physically consistent active power loss and voltage deviation within existing network operating constraints. Extensive simulations on IEEE 33-bus and 69-bus benchmark distribution systems demonstrate that FMOLFO achieves an up to 27% reduction in active power loss, improved voltage profile uniformity, and faster convergence compared with classical Newton–Raphson and metaheuristic baselines evaluated under identical conditions. The proposed framework is intended as a numerically enhanced, optimization-driven load-flow analysis tool, rather than a control- or dispatch-oriented optimal power flow formulation. Full article

An adaptive control strategy is developed and analyzed for trajectory tracking of mechanical systems subject to simultaneous model uncertainties and full-state constraints. To overcome the significant hurdle of guaranteeing both transient and steady-state performance within a user-defined time, a novel predefined-time adaptive neural network (NN) control scheme is proposed. By integrating predefined-time stability theory with a nonlinear mapping framework, a control scheme is developed to rigorously enforce full-state constraints while achieving predefined-time convergence. Radial basis function neural networks (RBFNNs) are employed to approximate the unknown system dynamics, with adaptive laws designed for online learning. The nonlinear mapping is strategically incorporated to ensure that the full-state constraints are never violated throughout the entire operation. Furthermore, through Lyapunov stability theory, it is proved that all signals of the resulting closed-loop system are uniformly ultimately bounded, and most importantly, the trajectory tracking error converges to a small neighborhood of zero within a predefined time, which can be explicitly set regardless of initial conditions. Comparative simulation results on a representative mechanical system are provided to demonstrate the superiority of the proposed controller, showcasing its faster convergence, higher tracking accuracy, and guaranteed constraint satisfaction compared to conventional finite-time and adaptive NN control methods. Full article

The purpose of this article is to develop optimal control strategies for discrete-time multi-agent systems (DT-MASs) with unknown disturbances, with the goal of enhancing their consensus performance and disturbance rejection capabilities. Complex flight conditions, such as the scenario of multi-unmanned aerial vehicle (multi-UAV) maintaining consensus under strong wind gusts, pose significant challenges for MAS control. To address these challenges, this article develops an optimal controller for UAV-based MASs with unknown disturbances to reach consensus. First, a novel improved nonlinear extended state observer (INESO) is designed to estimate disturbances in real time, accompanied by a corresponding disturbance compensation scheme. Subsequently, the consensus error systems and cost functions are established based on the disturbance-free DT-MASs. Building on this, a policy iterative algorithm based on a momentum-accelerated Actor–Critic network is proposed for the disturbance-free DT-MASs to synthesize an optimal consensus controller, whose integration with the disturbance compensation scheme yields an optimal disturbance rejection controller for the disturbance-affected DT-MASs to achieve consensus control. Comparative quantitative analysis demonstrates significant performance improvements over a standard gradient Actor–Critic network: the proposed approach reduces convergence time by 12.8%, improves steady-state position accuracy by 22.7%, enhances orientation accuracy by 42.1%, and reduces overshoot by 22.7%. Finally, numerical simulations confirm the efficacy and superiority of the method. Full article

This paper addresses a recursive adaptive anti-lock braking (AB) control design problem for electro-hydraulic brake (EHB) systems subject to unknown tire–road-friction coefficients and disturbances. Compared with the relevant literature, the primary contributions are (i) the development of a novel nonlinear AB model integrated with a bond-graph-based EHB (BGEHB) system, and (ii) the proposal of an adaptive neural AB control approach incorporating a nonlinear disturbance observer (NDO). The AB and BGEHB models are unified into a single nonlinear braking model, with the wheel speed as the system output and the duty ratios of the BGEHB inlet and outlet valves as control inputs. To maintain an optimal slip ratio during braking, we design the NDO-based adaptive AB controller to regulate the wheel speed in a recursive manner. The designed controller incorporates a delay-compensation term to address the time-delay characteristics of the hydraulic system, employs a neural-network function approximator in the NDO and controller to compensate for the unknown tire–road-friction coefficient, and applies NDOs to compensate for disturbances due to the vehicle motion and BGEHB dynamics. The stability of the proposed control scheme is established via the Lyapunov theory, and a simulation comparison is presented to demonstrate the effectiveness of the proposed design approach. Full article

Industrial aluminum-block heating processes exhibit nonlinear dynamics, substantial time delays, and stringent requirements for fault detection and diagnosis, especially in semiconductor manufacturing and other high-precision electronic processes, where slight temperature deviations can accelerate device degradation or even cause catastrophic failures. To address these challenges, this study presents a digital twin-based intelligent heating platform for aluminum blocks with a dual-artificial-intelligence framework (dual-AI) for control and diagnosis, which is applicable to multi-port aluminum-block heating systems. The system enables real-time observation and simulation of high-temperature operational conditions via virtual-real interaction. The platform precisely regulates a nonlinear temperature control system with a prolonged time delay by integrating a conventional proportional–integral–derivative (PID) controller with a Levenberg–Marquardt-optimized backpropagation (LM-optimized BP) neural network. Simultaneously, a relay is employed to sever the connection to the heater, thereby simulating an open-circuit fault. Throughout this procedure, sensor data are gathered simultaneously, facilitating the creation of a spatiotemporal time-series dataset under both normal and fault conditions. A one-dimensional convolutional neural network (1D-CNN) is trained to attain high-accuracy fault detection and localization. PID+LM-BP achieves a response time of about 200 s in simulation. In the 100 °C to 105 °C step experiment, it reaches a settling time of 6 min with a 3 °C overshoot. Fault detection uses a 0.38 °C threshold defined based on the absolute minute-to-minute change of the 1-min mean temperature. Full article