Search Results (1,027)

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

Severe convective weather is marked by abrupt onset, rapid evolution, and substantial destructive potential, posing major threats to economic activities and human safety. To address this challenge, this study proposes MRKAN, a multi-parameter prediction algorithm for dual-polarization radar that integrates Mamba, radial basis functions (RBFs), and the Kolmogorov–Arnold Network (KAN). The method predicts radar reflectivity, differential reflectivity, and the specific differential phase, enabling a refined depiction of the dynamic structure of severe convective systems. MRKAN incorporates four key innovations. First, a Cross-Scan Mamba module is designed to enhance global spatiotemporal dependencies through point-wise modeling across multiple complementary scans. Second, a Multi-Order KAN module is developed that employs multi-order β-spline functions to overcome the linear limitations of convolution kernels and to achieve high-order representations of nonlinear local features. Third, a Gaussian and Inverse Multiquadratic RBF module is constructed to extract mesoscale features using a combination of Gaussian radial basis functions and Inverse Multiquadratic radial basis functions. Finally, a Multi-Scale Feature Fusion module is designed to integrate global, local, and mesoscale information, thereby enhancing multi-scale adaptive modeling capability. Experimental results show that MRKAN significantly outperforms mainstream methods across multiple key metrics and yields a more accurate depiction of the spatiotemporal evolution of severe convective weather. Full article

This study focuses on investigating the adaptive sliding mode control (SMC) problem for connected vehicles with input delays and unknown time-varying control coefficients. As a result of wear and tear of mechanical components, throttle response lags, and the internal data processing time of the controller, input delays widely exist in vehicle actuators. Since input delays may lead to instability of the vehicle platoon, an improved integral compensation mechanism (ICM) with the adjustment factor for input delays is developed to improve the platoon’s robustness. As the actuator efficiency, drive mechanism, and load of the vehicle may change during operation, the control coefficients of vehicle dynamics are usually unknown and time-varying. A novel adaptive updating mechanism utilizing a radial basis function neural network (RBFNN) is designed to deal with the unknown time-varying control coefficients, thereby improving the vehicle platoon’s tracking performance. By integrating the improved ICM and the RBFNN-based adaptive updating mechanism (RBFNN−AUM), an innovative distributed adaptive control scheme using sliding mode techniques is proposed to guarantee that the convergence of state errors to a predefined region and accomplish the vehicle platoon’s control objectives. Comparative numerical results confirm the effectiveness and superiority of the developed control strategy over existing method. Full article

Trajectory tracking and self-balancing capacity is crucial for an unmanned bicycle robot (UBR) applied in off-road trails and narrow space. However, self-balancing is hard to be guaranteed once the steering angle manipulates for the tracking task, both of which are closely linked to the steering angle, especially for the UBR without auxiliary mechanism. In this paper, we introduce a double closed-loop framework in which the outer loop controller plans the desired speed and heading angle to track the reference trajectory, and the inner loop controller track the desired signals obtained from the outer loop to maintain balance. To be specific, a saturated velocity planner is developed to realize fast convergence of tracking error considering the kinematic constraints in the outer loop. A fuzzy sliding model controller (FSMC) is designed to attenuate the chattering effect via adapting its control gain in the inner loop, and a radial basis function neural network (RBFNN) approximator is also integrated into the framework to enhance the adaptability and robustness against bounded disturbances. The feasibility and effectiveness of the proposed control framework and approaches are validated based on the Matlab and Gazebo environment. In particular, the UBR can follow the testing route with lateral deviation less than 0.5 m in the presence of lateral winds and physical parameter measurement error, and comparative simulation results highlighted the superiority of the proposed control scheme. Full article

With the widespread adoption of the Internet of Things (IoT) and smart devices, the volume of data generated in sensor networks has increased dramatically, with diverse and structurally complex types that pose growing security risks. Anomaly detection in sensor networks has become a key technology for ensuring system stability and secure operation. This paper proposes a sensor anomaly detection model, termed RESTADM, which integrates a Transformer and a Radial Basis Function (RBF) neural network. The model first employs the Transformer to effectively capture the temporal dependencies in sensor data and then uses the RBF neural network to accurately identify anomalies. Experimental results on two public benchmark datasets, SMD and PSM, demonstrate the state-of-the-art performance of RESTADM. Our model achieves impressive F1-scores of 98.56% on SMD and 97.70% on PSM. This represents a statistically significant improvement compared to a range of baseline algorithms, including traditional models like CNN and LSTM, as well as the standard Transformer model. This validates the effectiveness of our proposed Transformer-RBF fusion, confirming the model’s high accuracy and robustness and offering an efficient security solution for intelligent sensing systems. Full article

To enhance grid stability with high renewable energy penetration, this paper proposes an adaptive virtual synchronous generator (VSG) control using a backpropagation neural network (BPNN). Traditional VSG control methods exhibit limitations in handling nonlinear dynamics and suppressing power oscillations. Distinguishing from existing studies that apply BPNN solely for damping adjustment, this paper proposes a novel strategy where BPNN simultaneously regulates both VSG virtual inertia and damping coefficients by learning nonlinear relationships among inertia, angular velocity deviation, and its rate of change. A key innovation is redesigning the error function to minimize angular acceleration changes rather than frequency deviations, aligning with rotational inertia’s physical role and preventing excessive adjustments. Additionally, an adaptive damping coefficient is introduced based on optimal damping ratio principles to further suppress power oscillations. Simulation under load disturbances and grid frequency perturbations demonstrates that the proposed BPNN strategy significantly outperforms constant inertia, bang–bang, and radial basis function neural network methods. Full article

The adaptive event-triggered fault-tolerant control problem for bidirectional consensus of multi-agent systems (MASs) subject to sensor faults and external disturbances is investigated. A hierarchical algorithm is first introduced to eliminate the dependence on Laplacian matrix information, thereby reducing computational complexity. Subsequently, a disturbance observer (DO) and a compensation signal were constructed to accommodate external disturbances, filtering errors, and approximation errors introduced by the radial basis function neural network (RBFNN). Compared with the absence of a disturbance observer, the tracking performance was improved by $15.2\%$. In addition, a switching event-triggered mechanism is considered, in which the advantages of fixed-time triggering and relative triggering are integrated to balance communication frequency and tracking performance. Finally, the boundedness of all signals under the proposed fault-tolerant control (FTC) scheme is established. It has been clearly demonstrated by the simulation results that the proposed mechanism achieves a $39.8\%$ reduction in triggering frequency relative to the FT scheme, while simultaneously yielding a $5.0\%$ enhancement in tracking performance compared with the RT scheme, thereby highlighting its superior efficiency and effectiveness. Full article

Integral Sliding Mode Control (ISMC) is widely employed in motor position control systems due to its robustness against uncertainties. However, its control performance is critically dependent on the selection of the switching gain. Although Disturbance Observer-Based Control (DOBC) is commonly adopted as an effective alternative for uncertainty compensation, it may exhibit limitations when high gains are required, potentially leading to system instability. To address these issues, this study proposes a Radial Basis Function Neural Network (RBF-NN)-based supervisory learning approach designed to minimize switching gain requirements. The effectiveness of the proposed scheme is validated through comparative simulations and laboratory experiments, specifically under scenarios involving system parameter uncertainties and sinusoidal disturbances with unknown offsets. Both simulation and experimental results demonstrate the superior performance of the proposed RBF-NN approach in terms of switching gain reduction and tracking error norms compared to a conventional ISMC and a DOBC-based cascade P–PI controller. Full article

Efficient and high-precision prediction of underwater vehicle radiated noise is crucial for warship stealth assessment. To overcome the high modeling complexity and limited prediction capability of traditional methods, this paper proposes ADE-PNN-ResNet, a fast underwater radiated noise (URN) prediction model integrating Adaptive Differential Evolution (ADE) with a Parallel Residual Neural Network (PNN-ResNet). This data-driven framework replaces conventional physics-based modeling, significantly reducing complexity while preserving high prediction accuracy. This study includes three core points: Firstly, for each 1/3-octave target noise band, a joint feature selection strategy of measurement points and frequency bands based on the ADE is proposed to provide high-quality inputs for the subsequent model. Secondly, a Parallel Neural Network (PNN) is constructed by integrating Radial Basis Function Neural Network (RBFNN) that excels at handling local features and Multi-Layer Perceptron (MLP) that focuses on global features. PNN is then cascaded via residual connections to form PNN-ResNet, deepening the network layers and efficiently capturing the complex nonlinear relationships between vibration and noise. Thirdly, the proposed ADE-PNN-ResNet is validated using vibration and noise data collected from lake experiments of a scaled underwater vehicle model. Under the validation conditions, the absolute prediction error is below 3 dB for 96% of the 1/3-octave bands within the frequency range of 100–2000 Hz, with the inference time for prediction taking merely a few seconds. The research demonstrates that ADE-PNN-ResNet balances prediction accuracy and efficiency, providing a feasible intelligent solution for the rapid prediction of underwater vehicle radiated noise in engineering applications. Full article

Renewable energy’s growing penetration erodes traditional power systems’ inherent dynamic symmetry—balanced inertia, damping, and frequency response. This paper proposes a self-adaptive virtual synchronous generator (VSG) control strategy for a photovoltaic hybrid energy storage system (PV-HESS) based on a radial basis function (RBF) neural network. The strategy establishes a dynamic adjustment framework for inertia and damping parameters via online learning, demonstrating enhanced system stability and robustness compared to conventional VSG methods. In the structural design, the DC-side energy storage system integrates a passive filter to decouple high- and low-frequency power components, with the supercapacitor attenuating high-frequency power fluctuations and the battery stabilizing low-frequency power variations. A small-signal model of the VSG active power loop is developed, through which the parameter ranges for rotational inertia (J) and damping coefficient (D) are determined by comprehensively considering the active loop cutoff frequency, grid connection standards, stability margin, and frequency regulation time. Building on this analysis, an adaptive parameter control strategy based on an RBF neural network is proposed. Case studies show that under various conditions, the proposed RBF strategy significantly outperforms conventional methods, enhancing key performance metrics in stability and dynamic response by 16.98% to 70.37%. Full article

With the increasing power output of aero-engines, combustor hot-gas mass flow rate and temperature continue to rise, posing more severe challenges to combustor structural cooling design. To enhance the film-cooling performance of the Z-profile feature in a reverse-flow combustor, this study performs a multi-parameter numerical optimization by integrating computational fluid dynamics (CFD), a radial basis function neural network (RBFNN), and a genetic algorithm (GA). The hole inclination angle, hole pitch, row spacing, and the distance between the first-row holes and the hot-side wall are selected as design variables, and the area-averaged adiabatic film-cooling effectiveness over a critical downstream region is adopted as the optimization objective. The RBFNN surrogate model trained on 750 CFD samples exhibits high predictive accuracy (correlation coefficient (R > 0.999)). The GA converges after approximately 50 generations and identifies an optimal configuration (Opt C). Numerical results indicate that Opt C produces more favorable vortex organization and near-wall flow characteristics, thereby achieving superior cooling performance in the target region; its average adiabatic film-cooling effectiveness is improved by 7.01% and 9.64% relative to the reference configurations Ref D and Ref E, respectively. Full article

To mitigate the impact of the conductor’s inherent tension–torsion coupling effect on conductor quality during tension stringing, a method for tension–torsion analysis and structural parameter optimization of conductors is proposed based on the radial basis function neural network (RBFNN) surrogate model. The layer-wise lay ratios of conductors are selected as the structural parameters. Using the tension–torsion coupling computational method for conductors, the layer-wise lay ratios are sampled by Latin hypercube sampling (LHS) to construct the sample data by computing conductor torque under different combinations. The RBFNN surrogate model is trained with the data, and its shape parameter is optimized through Leave-One-Out Cross-Validation (LOOCV), achieving a coefficient of determination R² close to 1 with minimal errors. Targeting torque minimization, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is employed to identify the optimal combination of conductor lay ratio parameters, reducing conductor torque by approximately 18% under the same axial tension. For practical applications, prioritize the optimal combination for JL/G1A-630/45-45/7 and analogous conductors, and adopt the RBFNN model for rapid torque prediction. The proposed method also serves as a reference for design optimization of conductor structural parameters. Full article

This paper presents an Artificial Neural Network (ANN) approach for estimating total real and reactive power losses in electrical power systems. Three network architectures were explored: the Multilayer Perceptron (MLP), the Radial Basis Function (RBF) network, and the Generalized Regression Neural Network (GRNN). The main advantage of the proposed methodology lies in its ability to rapidly compute power loss values throughout the system. ANN models are especially effective due to their capacity to capture the nonlinear characteristics of power systems, thus eliminating the need for iterative procedures. The applicability and effectiveness of the approach were evaluated using the IEEE 14-bus test system and compared with the continuation power flow method, which estimates losses using conventional numerical techniques. The results indicate that the ANN-based models performed well, achieving mean squared error (MSE) values below the predefined threshold during both training and validation (0.001). Notably, the networks accurately estimated the total power losses within the expected range, with residuals on the order of 10⁻⁴. Among the models tested, the RBF network showed slightly superior performance in terms of error metrics, requiring fewer centers to meet the established criteria compared to the MLP and GRNN models (11 centers). However, the GRNN achieved the shortest processing time; even so, all three networks produced satisfactory and consistent results, particularly in identifying the critical points of electrical power systems, which is of fundamental importance for ensuring system stability and operational reliability. Full article

Biogas production plays a key role in the development of renewable energy systems; however, forecasting biomethane yield remains challenging due to the nonlinear nature of anaerobic digestion. The objective of this study was to develop a predictive model based on Radial Basis Function Neural Networks (RBF-NN) to approximate biomethane production using operational data from the Przybroda biogas plant in Poland. Two separate models were constructed: (1) the relationship between process temperature and daily methane production, and (2) the relationship between methane fraction and total biogas flow. Both models were trained using Gaussian activation functions, individually adjusted neuron parameters, and a zero-level correction algorithm. The developed RBF-NN models demonstrated high approximation accuracy. For the temperature-based model, root mean square error (RMSE) decreased from 531 m³ CH₄·day⁻¹ to 52 m³ CH₄·day⁻¹, while for the methane-fraction model, RMSE decreased from 244 m³ CH₄·day⁻¹ to 27 m³ CH₄·day⁻¹. The determination coefficients reached R² = 0.99 for both models. These results confirm that RBF-NN provides an effective and flexible tool for modeling complex nonlinear dependencies in anaerobic digestion, even when only limited datasets are available, and can support real-time monitoring and optimization in biogas plant operations. Full article

This study focuses on the demand for rapid prediction of the flow field of aero-engine inlet free jet tests and explores the application of radial basis function interpolation (RBF) and backpropagation neural network (BPNN) methods. The proper orthogonal decomposition (POD) method was used for model order reduction of the full-order flow field results from numerical simulations. Two rapid prediction methods, namely POD-RBF and POD-BPNN, were constructed by utilizing radial basis function interpolation and a backpropagation neural network. These methods successfully achieved rapid prediction of the test flow field under different Mach numbers and angles of attack. To verify the accuracy of the numerical simulation and rapid prediction methods, a free jet test of the jet inlet was conducted under the same conditions. The test results show good agreement with both the CFD calculation results and the rapid prediction results. The research results show that the ninth-order mode can accurately reconstruct the flow field structure with a reconstruction error of 1.83%. Both methods can quickly and accurately predict the flow field under different conditions, and the prediction results are in good agreement with the numerical simulation results. Generally speaking, the prediction error of the POD-BPNN method is smaller than that of the POD-RBF method. Full article