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This work focuses on the issue of neural networks-based adaptive output-feedback control for nonlinear systems with dead zone output and immeasurable states. Radial basis function neural networks (RBFNN) are utilised to approximate the unknown functions and an input-driven filter is used to estimate the immeasurable states. Nussbaum function is employed to address the issue of uncertain virtual control coefficient, which is brought by the dead zone in the output mechanism, and the presented control scheme requires only one adaptive law, making the structure of the controllers very realistic. Based on the approximation capabilities of NNs and the backstepping method, an adaptive controller is designed. Based on the Lyapunov stability theory, all signals in closed-loop systems are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small area near the origin. The effectiveness of the proposed adaptive control method is proved with the help of two examples. [ABSTRACT FROM AUTHOR]

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This paper addresses the high‐precision control problem for nonlinear strict feedback systems with external time‐varying disturbances and proposes a novel composite learning control algorithm. Unlike previous research that only uses tracking errors for neural network updates, this paper prioritizes the accuracy of neural network learning. The article uses a selective memory recursive least squares algorithm to construct system information prediction errors, which are combined with tracking errors to update the neural network weights. A new composite learning control algorithm is developed to design dynamic surface control and neural network disturbance observers, which achieves high‐precision control of nonlinear strict feedback systems under external time‐varying disturbance conditions. Lyapunov's method demonstrates the stability of the closed‐loop system and the boundedness of errors. The simulation results show that the proposed control algorithm can effectively estimate system nonlinearity and suppress the impact of disturbances. [ABSTRACT FROM AUTHOR]

Since the trajectory-tracking control performance of multi-joint robot manipulator may be degraded due to modeling errors and external disturbances, this paper designs a new adaptive robot manipulator trajectory tracking control method through improved genetic algorithm and radial basis function neural network sliding mode control (IGA-RBFNNSMC). Firstly, the genetic algorithm (GA) is improved by establishing superior populations centered on individuals with high fitness values and selecting individuals in the superior populations for crossover and variation. Secondly, the improved genetic algorithm (IGA) is used for the optimization of the center vector and width vector of the Gaussian basis function in radial basis function (RBF) neural network. Then, based on the dynamics model of the robot manipulator, the modeling errors are approximated by RBF neural network and eliminated by sliding mode control (SMC), and the Lyapunov theorem is used to prove the stability and convergence of the control system. Finally, a two-joint robot manipulator is taken as the research objective and the simulation results show that IGA can significantly reduce the solution time on the basis of guaranteed accuracy and IGA-RBFNNSMC can make the trajectory tracking control accurate and more efficient, which proves the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

Given the large hysteresis in the material outlet temperature of the heating furnace, a differential evolution algorithm with adaptive adjustment factors (AAFDE)-radial basis function (RBF)-proportional integral derivative (PID)-PI cascade is proposed. First, we introduce an adaptive mutation factor into the differential evolution (DE) algorithm and define the individual merit coefficient to incorporate a self-adaptive crossover probability factor. Second, the initial parameters of RBF are optimized by the AFFDE algorithm, and RBF neural network model is established. Then, we obtain gradient information by RBF online identification. Finally, we perform online adjustments to the three parameters of PID based on the gradient information. The three parameters are applied to the adjustment of the main controller, while the sub-controller employs PI control. Experimental results show that the anti-disturbance performance of the AFFDE-RBF-PID-PI cascade control outperforms the improved differential evolution (IDE)-RBF-PID-PI cascade control, generalized opposition-based differential evolution (GODE)-RBF-PID-PI cascade control, and MCOBDE-RBF-PID-PI cascade control respectively by 16%, 11%, and 7%, with the response speed improving by 18%, 12%, and 9%, and the stability improving by 19%, 13%, and 8%. These results show that AFFDE-RBF-PID-PI cascade control exhibits enhanced anti-disturbance performance, faster response speed, improved stability, and superior control effect.

Given the increased significance of electric vehicles in recent years, this study aimed to develop a novel form of direct yaw-moment control (DYC) to enhance the driving stability of four-wheel independent drive (4WID) electric vehicles. Specifically, this study developed an innovative non-singular fast terminal sliding mode control (NFTSMC) method that integrates NFTSM and a fast-reaching control law. Moreover, this study employed a radial basis function neural network (RBFNN) to approximate both the entire system model and uncertain components, thereby reducing the computational load associated with a complex system model and augmenting the overall control performance. Using the aforementioned factors, the optimal additional yaw moment to ensure the lateral stability of a vehicle is determined. To generate the additional yaw moment, we introduce a real-time optimal torque distribution method based on the vertical load ratio. The stability of the proposed approach is comprehensively verified using the Lyapunov theory. Lastly, the validity of the proposed DYC system is confirmed by simulation tests involving step and sinusoidal inputs conducted using Matlab/Simulink and CarSim software. Compared to conventional sliding mode control (SMC) and NFTSMC methods, the proposed approach showed improvements in yaw rate tracking accuracy for all scenarios, along with a significant reduction in the chattering phenomenon in control torques. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of the navigation process is to determine the most efficient route for the hovercraft and regulate its movement along that route without any oscillation. The primary goal of this work is to determine the most efficient routes for a hovercraft operating in a global environment. The proposed approach, called Artificial Bee Colony Self Perception Particle Swarm Optimization (ABC-SPPSO), is utilized to accomplish this objective. The main benefit of utilizing the ABC-SPPSO algorithm is its ability to design the most efficient route while preventing collisions with stationary obstructions. In addition, a suggested controller that consists of a Feedforward Numerical Inverse Dynamic Controller (FNIDC) and a Feedback Neural Network Radial Basis Function (FNNRBF) control technique with the Grey Wolf Optimization (GWO) algorithm will be utilized to guide the hovercraft along predetermined paths. This suggested controller is designed to regulate the nonlinear dynamics of the hovercraft system in order to efficiently and rapidly generate the forces exerted by the starboard and portboard fans. These forces are utilized to control the orientation and position of the hovercraft. Moreover, the utilization of the suggested controller effectively reduces the differences between the desired and the actual positions in both the X-axis and the Y-axis. Additionally, the controller nearly eliminates any deviation in orientation and ensures a stable response without any oscillation. Specifically, the controller ensures that the hovercraft will promptly and accurately adhere to its intended trajectories. Ultimately, we verify the accuracy of the numerical simulation outcomes of the suggested control approach by contrasting them with those of other controllers, specifically in relation to the highest level of error improvement in the X-position and the Y-position. In particular, when comparing the proposed controller to the Improved Quasi-Velocities (IQV) controller, the results show that the suggested controller decreases the error rate of tracking on the X-position by 22% and enhances the tracking error rate on the Y-position by 14.8%. Furthermore, the suggested controller was evaluated against the terminal sliding mode controller (TSMC), and the results of the comparison indicate that the proposed controller enhances the error rate of tracking on the X-position by 50.7% and on the Y-position by 64.5%. Additionally, the suggested controller was evaluated against the nonlinear cascade controller, and the comparative analysis demonstrates that the suggested controller enhances the X-position and the Y-position tracking error rates by 25.9% and by 33%, respectively. Finally, from a comparative study with the neural network-based adaptive dynamic inversion controller, the proposed controller enhances the error rate of tracking on the X-position by 51% and on the Y-position by 42.9%. [ABSTRACT FROM AUTHOR]

The stability of each train, high control accuracy, and minimum safe separation distance are important indexes to measure the performance of the cooperative control system of multiple trains. In this article, aiming at the problem of low accuracy of multiple trains cooperative control with nonlinear running resistance and external disturbance, the distributed cooperative robust adaptive control scheme for multiple trains with RBFNN position output constraints based on train running curve tracking is proposed. Multiple different control techniques are offered for different trains, and that they are based on local knowledge of position, speed, and acceleration. The leading train's speed and position precisely match the planned operation curve, while the following train keeps the tracking interval at the minimum safe distance between two trains. In order to reduce the influence of the uncertainty of basic resistance parameters and external interference on the cooperative control of multiple trains, the parameter uncertainties are compensated by adding a robust adaptive law to the multiple trains control based on position output constraints. The lumped exogenous disturbances (additional resistance, external interference, measurement noise, etc.) are estimated using an RBFNN approximator for the unknown term of the cooperative system. The stability of the cooperative operation of multiple trains is confirmed using the Lyapunov stability theorem. The performance of the proposed scheme was evaluated by the cooperative control system of multiple trains in predecessor following (PF) and bidirectional control (BC) modes. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of controlling multiple unmanned aerial vehicles (UAVs) cooperating in a formation to carry out a complex task such as surface inspection. We first use the virtual leader-follower model to determine the topology and trajectory of the formation. A double-loop control system combining backstepping and sliding mode control techniques is then designed for the UAVs to track the trajectory. A radial basis function neural network capable of estimating external disturbances is developed to enhance the robustness of the controller. The stability of the controller is proven by using the Lyapunov theorem. A number of comparisons and software-in-the-loop tests have been conducted to evaluate the performance of the proposed controller. The results show that our controller not only outperforms other state-of-the-art controllers but is also sufficient for complex tasks of UAVs such as collecting surface data for inspection. The source code of our controller can be found at https://github.com/duynamrcv/rbf_bsmc. [ABSTRACT FROM AUTHOR]

With the increasing popularity of clean energy, the use of micro turbines to recover surplus energy in the water supply pipelines of high-rise buildings has attracted more attention. This study adopts a predictor model based on Radial Basis Function Neural Network (RBFNN) to optimize the draft tube shape for micro-Francis turbines. The predictor model is formed on a dataset provided by numerical simulations, which are validated by lab tests. Specifically, numerical investigations are carried out in the shape of a draft tube to determine an optimal model. Additionally, the superiority of the RBFNN model in nonlinear optimization is verified by comparing it with other models under the same date sets. After that, the design parameters are optimized using RBFNN and sequential quadratic programming algorithm (SQPA). Finally, the turbine prototype is fabricated and tested on a lab test rig. The experimental results indicate that the numerical method adopted in this research is accurate enough for such a micro-Francis turbine performance prediction. Under the design conditions, the proposed micro-Francis turbine produces a power of 147 W with an efficiency of over 29%, which shows a considerable improvement compared to the initial prototype. [ABSTRACT FROM AUTHOR]

In order to improve the hydraulic efficiency and internal flow pattern of a multi-stage pump under multiple flow conditions, an intelligent optimization design was proposed for its hydraulic components. Sensitivity analysis was used to select the key parameters influencing the hydraulic efficiency of a multi-stage pump. The optimal Latin hypercube sampling and non-dominated sorting genetic algorithm Ⅱ were employed to build a multi-objective optimization system. Moreover, a radial basis function neural network was adopted as the surrogate model of hydraulic efficiency. The research results showed that the impeller outlet width, impeller blade wrap angle, impeller outlet blade angle, and diffuser inlet width were the key factors affecting the hydraulic efficiency. The efficiency of the optimized model increased by 4.35% under the design condition and the matching of the internal flow between the optimized impeller and diffuser was significantly enhanced under the nominal condition. The improved flow pattern could be clearly observed in the flow passage of both the pump impeller and the diffuser. After optimization, the wear performance of the model was also improved compared to the original design. The wear area decreased in size and was distributed more evenly, resulting in a noticeable decrease in the maximum amount of wear. [ABSTRACT FROM AUTHOR]

The paper studies the control problem of an uncertain nonlinear system with tracking error constraint, unknown nonlinear functions and unknown control input gains. An asymmetric barrier Lyapunov function composed with predefined time prescribed performance function is constructed to ensure tracking error enters into the predefined asymmetric constraint in a given time. Then radial basis function neural network is adopted to approximate unknown functions. To reduce computation load, minimal-learning parameter technique is applied. Meanwhile, adaptive method is used to solve actuator faults and the unknown control input gains. Moreover, an adaptive neural control strategy is designed in the framework of backstepping method. An adaptive fixed time filter is developed for avoiding the "explosion of complexity" problem, where the convergence speed of the filter error is improved compared with fixed time filter. It is proved that all signals of the closed-loop system are bounded and tracking error is kept in its constraint boundary. At the end, compared numerical simulations and application simulation of a hypersonic vehicle are demonstrated to verify the efficiency of the designed control scheme. • A composed performance function is designed to ensure the tracking error enters into the constraint in a given time. • An adaptive fixed time filter is developed to solve the 'explosion of complexity' problem. • Based on minimal-learning parameter method, neural networks estimate unknown functions, which reduce computation load. • Compared simulation and application simulation of a hypersonic vehicle are given to test the efficiency of the controller. [ABSTRACT FROM AUTHOR]

To handle with the nonlinear external disturbances and unmodeled dynamics of self-balanced vehicle (SBV), a novel adaptive trajectory tracking controller based on asymptotic prescribed performance is proposed. First, a velocity planner based on kinematic is constructed to control the velocity signal to improve the motion stability of SBV. Second, the prescribed performance function (PPF) is designed to prescribe transient-state and steady-state performances (TSP). Afterwards, an optimization-based predictive control (OPC) is proposed for accurate trajectory tracking of SBV. Furthermore, a modified radial basis function neural network (RBFNN) approximator is developed to compensate the unmodeled dynamics and the nonlinear external disturbances of the SBV. The overall system stability is proved with the help of Lyapunov theorem. Finally, the tracking performance and anti-interference robustness of the proposed control method are verified by comparative numerical simulations. [ABSTRACT FROM AUTHOR]

Metal magnetic memory (MMM) is an innovative, nondestructive testing method. It can detect both stress concentrations and macroscopic defects. The three-dimensional force-magnetic coupling model was established by the ANSYS simulation software, the evolution process of different defect depths was studied in detail, and the change of the signal characteristic was analyzed. The results showed that the variation trend and amplitude characteristic of MMM signals resulted in obvious differences among different defect types. Meanwhile, the impacts caused by the defect parameters and the type are complex, which cannot be decoupled or calculated by a certain formula. The accuracy of the simulation data was verified by experiments. To solve the classification prediction problem in MMM detection, the signal peak and valley Hp-v, the signal width W, the gradient Ky, and the peak energy Hy were selected as characteristic parameters to evaluate different defect types according to the change in the signal waveform. Finally, using these vectors as the input variables, the radial basis function neural network (RBFNN) pre-classification test model was established to realize the classification recognition of pit defects, crack defects, and porosity defects. The results show that the accuracy of the training and test sets, and it is feasible to use this model to complete the intelligent classification of defects. [ABSTRACT FROM AUTHOR]

The diagnosis and treatment of cancer is one of the most challenging aspects of the medical profession, despite advances in disease diagnosis. MicroRNAs are small noncoding RNA molecules involved in regulating gene expression and are associated with several cancer types. Therefore, the analysis of microRNA data has become one of the most important areas of cancer research in recent years. This paper presents an improved method for cancer‐type classification based on microRNA expression data using a hybrid radial basis function (RBF) and particle swarm optimization (PSO) algorithm. Two datasets containing microRNA information were used, and preprocessing and normalization operations were performed on the raw data. Feature selection was carried out by using the PSO algorithm, which can identify the most relevant and informative features in the data along with helping to prioritize them. Using a PSO algorithm for feature selection is an effective approach to microRNA analysis. This enhances the accuracy and reliability of cancer‐type classifications based on microRNA expression data. In the proposed method, we, respectively, achieved an accuracy of 0.95% and 0.91% on both datasets, with an average of 0.93%, using an improved RBF neural network classifier. These results demonstrate that the proposed method outperforms previous works. Research Highlights: To enhance the accuracy of cancer‐type classifications based on microRNA expression data.We present a minimal feature selection method using particle swarm optimization to reduce computational load & radial basis function to improve accuracy. [ABSTRACT FROM AUTHOR]

Magnetorheological and electrorheological fluids manifest a change in rheological behavior when subjected to a magnetic or electric field, respectively, such that they require electrical and magnetic characterization. In this paper, a simple and accurate mathematical model based on a small number of parameters provides the relative magnetic permeability of magnetorheological fluids as a function of the applied magnetic field. Furthermore, for the testing and magnetic characterization of magnetorheological fluids, a new metering equipment setup is implemented. Starting with the achieved experimental data, the mathematical relation μ r = f (B) is represented by means of a radial basis function neural network, with neurons having a Gaussian activation function; by means of post-training pruning procedures, the trained neural network is applied using the proposed data. Therefore, the obtained mathematical relation μ r = f (B) is in good agreement with the experimental data, with an approximate error of 8%. [ABSTRACT FROM AUTHOR]

Drought has a significant impact on crop growth and productivity, highlighting the critical need for precise and timely soil moisture estimation to mitigate agricultural losses. This study focuses on soil moisture retrieval in northern Hebei Province during July 2012, utilizing eight widely employed remote sensing drought indices derived from MODIS satellite data. These indices were cross-referenced with measured soil moisture levels for analysis. Based on their correlation coefficients, a composite remote sensing drought index set comprising six indices was identified. Furthermore, a radial basis function neural network (RBFNN) was employed to estimate soil relative humidity. The accuracy evaluation of the soil moisture estimation model, which integrates multiple remote sensing drought indices and the RBFNN, demonstrated clear superiority over models relying on single drought indices. The model achieved an average estimation accuracy of 87.54 % for soil relative humidity at a depth of 10 cm (SM10) and 87.36 % for a 20 cm depth (SM20). The root mean square errors (RMSE) for the test sets were 0.093 and 0.092, respectively. Validation results for July 2013 indicated that the inversion accurately reflected the actual soil moisture conditions, effectively capturing dynamic moisture changes. These results fully verify the reliability and practicability of the model. These findings introduce a novel approach to local agricultural soil moisture estimation, with significant implications for enhancing agricultural water resource management and decision-making processes.

Friction torque disturbance affects the tracking performance of electromechanical actuator servo system, bringing position and speed tracking errors, and even may leading to instability of the servo system. Aiming at the problem of poor tracking performance of electromechanical actuator servo system under friction torque disturbance, a FOANN friction compensation algorithm is proposed to estimate and compensate the friction torque. Firstly, base on LuGre friction model, a electromechanical actuator model is established, and the unmeasured state variable in the LuGre model is estimated by radial basis function neural network. Secondly, a FOANN controller is designed, and the stability of corresponding closed-loop system is proved by Lyapunov stability theory. Finally, through simulation and experimental platform, the dynamic performance of FOANN is compared with those of traditional PD and MRAC. The simulation and experimental results show that, with the proposed FOANN friction torque compensation algorithm, the tracking errors of both position and velocity of electromechanical actuator servo system are greatly reduced. FOANN algorithm can effectively estimate and compensate friction torque, reduce the impact of friction disturbance and enhance the dynamic performance of the servo system.

Most satellite missions have extremely stringent requirements for attitude reliability. However, the Inertial Measurement Unit (IMU) in the Attitude Determination System (ADS), is susceptible to performance degradation in the space environment and can lead to mission failure. The proposed fault tolerance scheme includes two-layer fault detection with isolation and two-layered recovery. An Adaptive Unscented Kalman Filter (AUKF), quaternion estimator (QUEST) algorithm, and residual generator constitute the first layer of fault detection. At the same time, Radial Basis Function (RBF) neural networks and an adaptive complementary filter (ACF) make up the second layer of fault detection. These two fault detection layers aim to isolate and identify faults while decreasing the rate of false alarms. The AUKF and Fault Detection, Isolation, and Reconstruction (FDIR) residual generator make up the two-layered attitude recovery system. Compared to traditional fault-tolerant systems, this scheme solves the outlier problem of sensors and has higher accuracy. When one of the IMU sensors fails, it will be detected, and the proposed scheme can maintain accurate attitude estimation by leveraging a trained neural network. In addition, the secondary fault detection and isolation layer can minimize the rate of false alarms, meaning more reliable ADS for satellites. • Fault detection, isolation and correction with Kalman Filter and Neural Network. • The hypothesis testing algorithm decreases the false alarm rate. • The double fault recovery strategy achieves better precision. [ABSTRACT FROM AUTHOR]

In this paper, the controller modelling and design for the Active Switched Capacitor/Switched Inductor Quasi-Z-Source with Multilevel Inverter (ASC/SL-QZSI) related three-phase grid-tied photovoltaic (PV) power system is proposed. The ASC/SL-QZSI control method consists of two phases: these are assessed with the use of the proposed effective controller. The proposed control system is the hybridization of the Student Psychology-Based Optimization (SPBO) and the Radial Basis Function Neural Network (RBFNN), hence it is named SPBO–RBFNN control scheme. The ASC/SL-QZS offers greater boost capability, uses fewer passive components, like inductors and capacitors, and reduces the voltage stress across main inverter switching devices. The expandability of this topology is another advantage. Extra cells can simply be cascaded at the network's impedance if a higher boost rate is required by adding an inductor and three diodes. In the proposed control scheme, SPBO is developed for determining the total PV voltages. The input PV reference voltages and gain parameters of the SPBO are created as output for optimal tuning of the Proportional Integral (PI) controller. RBFNN is trained with offline process and it is used to extract the reference currents of the grid, and the output of RBFNN is provided with SPBO. It delivers the corresponding tuning parameters to accomplish the grid current. With this proper control, the input power is reduced and the current, voltage and frequency conditions of DC-link are regulated. Finally, the performance of the QZS-CMI is executed in MATLAB and the performance is compared with existing methods. [ABSTRACT FROM AUTHOR]

Overhead cranes are used to move heavy, bulky objects above the factory floor instead of along floor walkways. They are commonly used to load and unload goods in factories, outdoor warehouses and serve at stations, ports. During operation, chain hoists or cable hoists are the main equipment, plays the role of hoisting/lowering materials and moving mechanism along the main beam. Therefore, vibration cannot be avoided during the process of moving heavy objects, causing danger to people and affecting the product. In addition, the overhead crane is an uncertain nonlinear system, compared to the single pendulum type, moving two loads at the same time is much more complicated. That's why the author proposes to design a new controller that not only helps balance the cart but also the two pendulums during operation. First, the system dynamics model is built. Next, an adaptive controller based on the radial basis function neural network (RBFNN) is designed and proven to be stable according to Lyapunov theory. Simulation results of the overhead crane system on MATLAB/Simulink software have shown the effectiveness of the proposed algorithm even when the working system is affected by model uncertainty. [ABSTRACT FROM AUTHOR]

The traditional loader drive system is based on the hydraulic torque converter as the key component, and its gear is shifted through the coordination of the clutch and gearbox, which greatly increases power loss and operator fatigue. To address the above problem, a variable-structure hydrostatic drive system is proposed. This system adopts a closed-loop design and adjusts the displacement of the pump and dual motors to follow the throttle opening of the vehicle, achieving automatic gear shifting and smooth speed regulation. It can also automatically change the system structure according to the vehicle's speed, meeting the vehicle's demand for rapid switching output of high torque and high speed. At the same time, in the displacement matching control process, an adaptive sliding mode control scheme based on radial basis function neural network compensation is proposed. This scheme designs an adaptive hyperparameter update strategy according to the characteristics of the system to effectively compensate for changes in uncertain factors. Experimental results show that, compared to traditional drive systems, this system has the characteristics of simple operation, smooth speed regulation, and high fuel economy. [ABSTRACT FROM AUTHOR]

Electricity is an essential energy resource in the industrial, commercial and housing sector, having a very important role in the development of societies. Urbanization and industrialization implies a great demand of energy for developing economies. In the search to be able to know how much electrical energy is consumed, a modeling of the electrical energy demand is carried out. However, the inherent intricacy and nonlinear nature of electricity consumption patterns present a significant obstacle to achieve precise modeling. In this article, a chaos theory approach is carried out to analyze the behavior of the system and to obtain properties of its dynamic system. A network consisting of a convolutional part, a hidden part and an output part is proposed. Convolutional operations are employed for dimensionality reduction in transformed data sets by reconstruction of the phase space. A radial basis function neural is used in the hidden part. The dynamic analysis approach using chaos theory, and the proposed neural network is compared with the radial basis function neural network for the modeling of electrical energy consumption. [ABSTRACT FROM AUTHOR]

It is a crucial basis to improve the performance of neural network by constructing an appropriate activation function. This paper proposes a novel modulation window radial basis function neural network (MW-RBFNN) with an adjustable activation function. In this MW-RBFNN, a raised cosine radial basis function (RC-RBF) is adaptively modulated by an exponential function, and served as a shape-tunable activation function of MW-RBFNN. Compared with the basic RC-RBF neural network, the approximating ability of MW-RBFNN is improved due to its shape-tunable activation function. Besides, the computation of MW-RBFNN is far less than that of Gaussian radial basis function neural network (GRBFNN) because the MW-RBFNN is compactly supported. The training algorithm of MW-RBFNN is provided and its approximating ability is proved. Moreover, the regulation mechanism of the modulation index for the NN's performance is proved and the regulating algorithm of the modulation index in MW-RBFNN is given. The computational complexity of MW-RBFNN is also analyzed. Five typical application cases are presented to illustrate the effectiveness of this proposed MW-RBFNN. [ABSTRACT FROM AUTHOR]

Understanding subterranean reservoirs, geological characteristics, fluid composition, and hydrocarbon potential strongly relies on precise reservoir characterization. Seismic inversion is a key method in reservoir characterization to approximate the acoustic impedance and porosity of underlying rock formations using seismic and well-log data. A sparse layer reflectivity (SLR) post-stack inversion method approach is used in this study to make thin layers more visible. To generate an impedance volume, it uses a predetermined wavelet library, an objective function, and a regularization parameter, the regularization parameter is a tunable parameter used to control the balance between fitting the data closely (minimizing the misfit) and ensuring a smooth and stable model for and sparseness computed coefficients. This study uses Blackfoot data to estimate the density, velocity, impedance, and porosity of a particular region using the SLR and Radial Basis Function Neural Network (RBFNN). According to the interpretation of the impedance section, a low impedance anomaly zone with an impedance range of (8500–9000) m/s*g/cc is present at a time of (1040–1065) ms. The low impedance zone is classified as a clastic glauconitic sand channel (reservoir zone) based on the correlation between seismic and borehole data. Further, a Radial Basis Function Neural Network (RBFNN) has been applied to the data to estimate porosity volume and to conduct a more thorough examination of the reservoir zone and cross-validate inverted results. The research demonstrates that the high porosity zone, low velocity, and density zone are discovered by the RBFNN technique, and the low impedance zone interpreted in inversion findings are correlating, which confirms the existence of the glauconitic sand channel. This research is crucial for understanding how well SLR, RBFNN, and multi-attribute analysis work to define sand channels. [ABSTRACT FROM AUTHOR]

Effective vibration control of flexible cables is increasingly demanded for space flexible structure. For the low frequency dynamics of flexible cables, a novel vibration control strategy based on sliding mode control is proposed in this paper. First, the complex dynamical behavior of flexible cables is modeled using D'Alembert's principle. Second, an improved dual power reaching law is put forward to better address the chattering problem of the sliding mode controller. Then, an extended state observer (ESO) with the radial basis function (RBF) neural network is designed to compensate for internal uncertainties and external disturbances as well as further improve the control accuracy. Lyapunov function is employed to analyze the stability of the system. Finally, considering the large number of parameters of the proposed controller, an improved particle swarm optimization (PSO) algorithm is designed to automatically adjust these parameters. The effectiveness of the proposed method is demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

This paper proposes an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme for a class of nonlinear batch processes with a priori P-type feedback control structure subject to nonrepetitive uncertainties, by only using the process input and output data available in practice. Firstly, the unknown process dynamics is equivalently transformed into an iterative dynamic linearization data model (IDLDM) with a residual term. A radial basis function neural network is adopted to estimate the pseudo partial derivative information related to IDLDM, and meanwhile, a data-driven iterative ESO is constructed to estimate the unknown residual term along the batch direction. Then, an adaptive set-point learning control law is designed to merely regulate the set-point command of the closed-loop control structure for realizing batch optimization. Robust convergence of the output tracking error along the batch direction is rigorously analyzed by using the contraction mapping approach and mathematical induction. Finally, two illustrative examples from the literature are used to validate the effectiveness and advantage of the proposed design. • Novel extended state observer (ESO) based data-driven set-point learning control (DDSPLC). • Iterative dynamic linearized data model (IDLDM) for nonlinear batch processes. • Use the radial basis function neural network (RBFNN) to approximate pseudo partial derivative. • Set-point learning law with an adaptive learning gain for a priori P-type feedback structure. • Robust convergence of the resulting ILC system along the batch direction is rigorously analyzed. [ABSTRACT FROM AUTHOR]

With the increasing prevalence of high-order systems in engineering applications, these systems often exhibit significant disturbances and can be challenging to model accurately. As a result, the active disturbance rejection controller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmanned aerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances and the possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address these issues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neural network (RBFNN) with a second-order ADRC and leverages a fractional gradient descent (FGD) algorithm. We integrate the plant protection UAV model's uncertain parameters, load disturbance parameters, and actuator fault parameters and utilize the RBFNN for system parameter identification. The resulting ADRC exhibits load disturbance suppression and fault tolerance capabilities, and our proposed active fault-tolerant control law has Lyapunov stability implications. Experimental results obtained using a multi-rotor fault-tolerant test platform demonstrate that the proposed method outperforms other control strategies regarding load disturbance suppression and fault-tolerant performance. [ABSTRACT FROM AUTHOR]

The prescribed performance tracking control problem of intelligent vehicle steering system with model nonlinearity and parameter uncertainty is studied. The RBFNN (Radial Basis Function Neural Network) is used to approximate the uncertain nonlinearity in the steering system. The prescribed performance controller is designed for the steer-by-wire system of intelligent vehicle based on the barrier Lyapunov function technology. In the design of the controller, the dynamic gain technology is used to compensate the effect of unknown control gain on the system control performance. Finally, the stability of the system is analyzed by Lyapunov method, and it is proved that the tracking error of the front wheel angle can converge to the prescribed neighborhood of the origin within the prescribed time under the action of the proposed controller. The rationality of the control method is verified by numerical simulation and vehicle experiment. [ABSTRACT FROM AUTHOR]

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, we processed the flame images of biodiesel combustion in industrial furnaces, classified and evaluated flame states using digital image processing techniques, and proposed a combustion stability index (CSI) using the particle swarm optimization (PSO) algorithm. In order to more accurately predict the combustion stability under different oxygen concentrations, we proposed a method that combines the Multi-Input Radial basis function neural network (RBF-NN) with empirical mode decomposition (EMD). Initially, the EMD method was employed to decompose the original time series of CSI. Subsequently, a decomposition model incorporating initial parameters and CSI was established using the radial basis function. The results of the computations indicate that the EMD-RBF-NN model significantly outperforms existing models in enhancing the accuracy of CSI. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive composite anti-disturbance control of heavy haul trains (HHTs) is proposed. First, the mechanical principle and characteristics of couplers are analysed and the longitudinal multi-particles nonlinear dynamic model of HHTs is established, which can satisfy that the forces of vehicles in different positions are different. Subsequently, a radial basis function network (RBFNN) is employed to approximate the uncertainties of HHTs, and a nonlinear disturbance observer (NDO) is constructed to estimate the approximation error and external disturbances. To indicate and improve the approximation accuracy, a serial-parallel identification model of HHTs is constructed to generate a prediction error, and an adaptive composite anti-disturbance control scheme is developed, where the prediction error and tracking error are employed to update RBFNN weights and an auxiliary variable of NDO. Finally, the feasibility and effectiveness of the proposed control scheme are demonstrated through the Lyapunov theory and simulation experiments. [ABSTRACT FROM AUTHOR]

High gain single switch DC-DC converter for Proton Exchange Membrane Fuel Cell (PEMFC) with Neural-Network (NN) algorithm based Maximum Power Point Tracking (MPPT) technique to drive Electric Vehicle (EV) is proposed in this paper. Apart from many maximum power extraction techniques, Neural Network (NN) based algorithm control is proposed, which uses Radial Basis Function Network (RBFN) to harvest maximum power from PEMFC under different temperature variations, which is compared with traditional Incremental Conductance (InC) method. Apart from it, a novel Single Ended Primary Inductor (SEPI) converter (boost converter) with the Coupled Inductor and Switched-Capacitor Circuit (CI + SCC) is designed to lower stress in the voltage across the switch with improved voltage gain to meet drive train (Three-Phase BLDC Motor) scenarios. Proposed PEMFC performance with RBFN-based MPPT technique integrated to drive train using a novel high gain converter is simulated using MATLAB/Simulink and implemented using dSPACE-DS1104 real-time controller. The compact sizing of the converter is proposed to integrate with PEMFC, which would be suitable for low-power EV applications.

To address the control challenges of an aerial manipulator arm (AMA) mounted on a drone under conditions of model inaccuracy and strong disturbances, this paper proposes a hierarchical control architecture. In the upper-level control, Bézier curves are first used to generate smooth and continuous desired trajectory points, and the theory of singular trajectory lines along with a Radial Basis Function Neural Network (RBFNN) is introduced to construct a highly accurate multi-configuration inverse kinematic solver. This solver not only effectively avoids singular solutions but also enhances its precision online through data-driven methods, ensuring the accurate calculation of joint angles. The lower-level control focuses on optimizing the dynamic model of the manipulator. Using a Model Predictive Control (MPC) strategy, the dynamic behavior of the manipulator is predicted, and a rolling optimization process is executed to solve for the optimal control sequence. To enhance system robustness, an RBFNN is specifically introduced to compensate for external disturbances, ensuring that the manipulator maintains stable performance in dynamic environments and computes the optimal control commands. Physical prototype testing results show that this control strategy achieves a root mean square (RMS) error of 0.035, demonstrating the adaptability and disturbance rejection capabilities of the proposed method.

Renewable energy resources like solar power contribute greatly to decreasing emissions of carbon dioxide and substituting generators fueled by fossil fuels. Due to the unpredictable and intermittent nature of solar power production as a result of solar radiance and other weather conditions, it is very difficult to integrate solar power into conventional power systems operation economically in a reliable manner, which would emphasize demand for accurate prediction techniques. The study proposes and applies a revised radial basis function neural network (RBFNN) scheme to predict the short-term power output of photovoltaic plant in a day- ahead prediction manner. In the proposed method, the linear as well as non-linear variables in the RBFNN scheme are efficiently trained using the whale optimization algorithm to speed the convergence of prediction results. A nonlinear benchmark function has also been used to validate the suggested scheme, which was also used in predicting the power output of solar energy for a well-designed experiment. A comparison study case generating different outcomes shows that the suggested approach could provide a higher level of prediction precision than other methods in similar scenarios, which suggests the proposed method can be used as a more suitable tool to deal such solar energy forecasting issues. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of achieving distributed coverage control of a mobile sensor network on an unknown region using local measurements. To accomplish this objective, each sensor is equipped with two-layer dynamics. The upper layer dynamic employs a completely distributed observer algorithm on the target region for state estimation of the density function. The lower layer dynamic utilizes a radial basis function neural network-based motion algorithm, which involves only the estimated state obtained by the upper layer dynamics, to guide the sensors towards an optimal coverage configuration. We demonstrate that with only the joint detectability of the partial outputs measurement, it is possible to achieve distributed coverage control in the unknown region without requiring additional information about the density function, communication topology associated with the sensors, or coupling gains. Finally, two examples are used to validate the theoretical findings. [ABSTRACT FROM AUTHOR]

This study integrates fuzzy logic-controlled data switching and the radial basis function neural network (RBFNN) for fault detection and classification in grid-tied solar energy systems. The fuzzy logic controller filters out invalid sensor data through a data switch, ensuring that the fault detection and classification system receives reliable input. Training data were prepared through data normalization using the z-score function and principal component analysis, thereby reducing data complexity and standardizing the inputs. The resulting RBFNN model exhibited a low mean squared error with a value of 7.67×10-4, indicating its ability to classify faults based on the actual system scenarios. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, were used to assess the effectiveness of the RBFNN model. The model demonstrated high accuracy (96.4%), precision (98.281%), recall (98.013%), and F1-score (98.147%), indicating the suitability and effectiveness of the RBFNN model to identify and classify faults in grid-tied solar energy systems. [ABSTRACT FROM AUTHOR]

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Adsorption is an effective and economical alternative to remove herbicides from polluted water. The aim of this study is to investigate the adsorption of the most common herbicides (2,4-dichlorophenoxy-acetic acid (2,4-D) and 4-chloro-2-methylphenoxyacetic acid (MCPA)) onto activated carbon (AC) fabricated from wheat straw under different conditions. The adsorption of MCPA and 2,4-D onto the selected AC (CLW) and the effects of the ionic strength, the solution pH, and the presence of microorganisms in the medium were investigated. The results showed that the selected AC had a high surface area (1437 m2/g). The adsorption rate increased with an increase in the AC mass. The selected AC had a higher adsorption capacity (1.32 mmol/g) for 2,4-D compared to MCPA (0.76 mmol/g). The adsorption of 2,4-D and MCPA was not affected by variation in the solution pH. However, the presence of electrolytes exerted a major effect on adsorption. The presence of microorganisms enhanced adsorption onto the AC by 17% and 32% for 2,4-D and MCPA, respectively. Moreover, a radial basis function neural network (RBFNN) was employed to accurately predict the adsorption capacity based on the pollutant type, carbon dose, initial concentration, pH, ionic strength, and presence of bacteria. The RBFNN showed excellent accuracy in predicting the adsorption capacity, with an R2 value of 0.96 and a root mean square error (RMSE) of 0.054. These findings showed that the AC fabricated from biomass residues of wheat straw is a promising option to recycle this type of biomass waste and reduce environmental threats, consequently contributing to achieving sustainability. [ABSTRACT FROM AUTHOR]