Search Results (1,325)

In space gravitational wave detection, various physical effects in the spacecraft, such as self-gravity, electricity, and magnetism, will introduce undesirable parasitic stiffness. The coupling noise between stiffness and the motion states of the test mass critically affects the performance of scientific detection, making accurate stiffness identification crucial. In response to the question, this paper proposes a method to optimize the test mass motion state for enhancing stiffness identification performance. First, the dynamics of the test mass are studied and a recursive least squares algorithm is applied for the implementation of on-orbit stiffness identification. Then, the motion state of the test mass is parametrically characterized by multi-frequency sinusoidal signals as the variable to be optimized, with the optimization objectives and constraints of stiffness identification defined based on convergence time, convergence accuracy, and engineering requirements. To tackle the dual-objective, computationally expensive nature of the problem, a multigranularity surrogate-assisted evolutionary algorithm with individual progressive constraints (MGSAEA-IPC) is proposed. A fuzzy radial basis function neural network PID (FRBF-PID) controller is also designed to address complex control needs under varying motion states. Numerical simulations demonstrate that the convergence time after optimization is less than 2 min, and the convergence accuracy is less than 1.5 × 10⁻¹⁰ s⁻². This study can provide ideas and design references for subsequent related identification and control missions. Full article

Two-wheeled self-balancing robots (TWSBRs) are underactuated, inherently nonlinear systems that exhibit unstable dynamics. Due to their structural simplicity and rich control challenges, TWSBRs have become a standard platform for validating and benchmarking various control algorithms. This paper presents a comprehensive and structured review of control strategies applied to TWSBRs, encompassing classical linear approaches such as PID and LQR, modern nonlinear methods including sliding mode control (SMC), model predictive control (MPC), and intelligent techniques such as fuzzy logic, neural networks, and reinforcement learning. Additionally, supporting techniques such as state estimation, observer design, and filtering are discussed in the context of their importance to control implementation. The evolution of control theory is analyzed, and a detailed taxonomy is proposed to classify existing works. Notably, a comparative analysis section is included, offering practical guidelines for selecting suitable control strategies based on system complexity, computational resources, and robustness requirements. This review aims to support both academic research and real-world applications by summarizing key methodologies, identifying open challenges, and highlighting promising directions for future development. Full article

Landslide susceptibility assessment (LSA) plays a crucial role in disaster prevention and mitigation. Traditional random selection of non-landslide samples (labeled as 0) suffers from poor representativeness and high randomness, which may include potential landslide areas and affect the accuracy of LSA. To address this issue, this study proposes a novel Landslide Susceptibility Index–based Semi-supervised Fuzzy C-Means (LSI-SFCM) sampling strategy combining membership degrees. It utilizes landslide and unlabeled samples to map landslide membership degree via Semi-supervised Fuzzy C-Means (SFCM). Non-landslide samples are selected from low-membership regions and assigned membership values as labels. This study developed three models for LSA—Convolutional Neural Network (CNN), U-Net, and Support Vector Machine (SVM), and compared three negative sample sampling strategies: Random Sampling (RS), SFCM (samples labeled 0), and LSI-SFCM. The results demonstrate that the LSI-SFCM effectively enhances the representativeness and diversity of negative samples, improving the predictive performance and classification reliability. Deep learning models using LSI-SFCM performed with superior predictive capability. The CNN model achieved an area under the receiver operating characteristic curve (AUC) of 95.52% and a prediction rate curve value of 0.859. Furthermore, compared with the traditional unsupervised fuzzy C-means (FCM) clustering, SFCM produced a more reasonable distribution of landslide membership degrees, better reflecting the distinction between landslides and non-landslides. This approach enhances the reliability of LSA and provides a scientific basis for disaster prevention and mitigation authorities. Full article

This paper presents a novel wireless control framework for electric drive systems by employing a fuzzy brain emotional learning neural network (FBELNN) controller in conjunction with a Disturbance Observer (DO). The communication scheme uses chaotic system dynamics to ensure data confidentiality and robustness against disturbance in wireless environments. To be applied to embedded microprocessors, the continuous-time chaotic system is discretized using the Grunwald–Letnikov approximation. To avoid the loss of generality of chaotic behavior, Lyapunov exponents are computed to validate the preservation of chaos in the discrete-time domain. The FBELNN controller is then developed to synchronize two non-identical chaotic systems under different initial conditions, enabling secure data encryption and decryption. Additionally, the DOB is introduced to estimate and mitigate the effects of bounded uncertainties and external disturbances, enhancing the system’s resilience to stealthy attacks. The proposed control structure is experimentally implemented on a wireless communication system utilizing ESP32 microcontrollers (Espressif Systems, Shanghai, China) based on the ESP-NOW protocol. Both control and feedback signals of the electric drive system are encrypted using chaotic states, and real-time decryption at the receiver confirms system integrity. Experimental results verify the effectiveness of the proposed method in achieving robust synchronization, accurate signal recovery, and a reliable wireless control system. The combination of FBELNN and DOB demonstrates significant potential for real-time, low-cost, and secure applications in smart electric drive systems and industrial automation. Full article

As electric vehicles (EVs) continue to advance toward widespread adoption, innovations in power electronics are playing a pivotal role in improving efficiency, performance, and sustainability. This review presents recent progress in bidirectional converters and regenerative braking systems (RBSs), highlighting their contributions to energy recovery, battery longevity, and vehicle-to-grid integration. Bidirectional converters support two-way energy flow, enabling efficient regenerative braking and advanced charging capabilities. The integration of wide-bandgap semiconductors, such as silicon carbide and gallium nitride, further enhances power density and thermal performance. The paper evaluates various converter topologies, including single-stage and multi-stage architectures, and assesses their suitability for high-voltage EV platforms. Intelligent control strategies, including fuzzy logic, neural networks, and sliding mode control, are discussed for optimizing braking force and maximizing energy recuperation. In addition, the paper explores the influence of regenerative braking on battery degradation and presents hybrid energy storage systems and AI-based methods as mitigation strategies. Special emphasis is placed on the integration of RBSs in advanced electric vehicle platforms, including autonomous systems. The review concludes by identifying current challenges, emerging trends, and key design considerations to inform future research and practical implementation in electric vehicle energy systems. Full article

Despite the rapid adoption of green buildings as a sustainable development strategy, robust, data-driven approaches for assessing and predicting project risks remain limited. This study proposes an innovative hybrid framework combining the fuzzy analytic hierarchy process (FAHP), multilayer perceptron neural networks (MLPNNs), and particle swarm optimization (PSO) to quantify and forecast the impact of critical risks on green buildings’ performance. Drawing on structured input from 30 domain experts in Shenzhen, China, ten risk categories were identified and prioritized, with economic, market, and functional risks emerging as the most influential. Using these expert-derived weights, an MLP was trained to predict the effects of the top five risks on four core performance metrics—cost, time, quality, and scope. PSO was applied to optimize the model’s architecture and hyperparameters, improving its predictive accuracy. The optimized framework achieved RMSE values ranging from 0.06 to 0.09 and R² values of up to 0.95 across all outputs, demonstrating strong predictive capability. These results substantiate the framework’s effectiveness in generating actionable, quantitative risk predictions under uncertainty. Full article

Outdoor cleaning robots must operate reliably across diverse and unstructured surfaces, yet many existing systems lack the adaptability to handle terrain variability. This paper proposes a terrain-aware cleaning framework that dynamically adjusts robot behavior based on real-time surface classification and slope estimation. A 128-channel LiDAR sensor captures signal intensity images, which are processed by a ResNet-18 convolutional neural network to classify floor types as wood, smooth, or rough. Simultaneously, pitch angles from an onboard IMU detect terrain inclination. These inputs are transformed into fuzzy sets and evaluated using a Mamdani-type fuzzy inference system. The controller adjusts brush height, brush speed, and robot velocity through 81 rules derived from 48 structured cleaning experiments across varying terrain and slopes. Validation was conducted in low-light (night-time) conditions, leveraging LiDAR’s lighting-invariant capabilities. Field trials confirm that the robot responds effectively to environmental conditions, such as reducing speed on slopes or increasing brush pressure on rough surfaces. The integration of deep learning and fuzzy control enables safe, energy-efficient, and adaptive cleaning in complex outdoor environments. This work demonstrates the feasibility and real-world applicability for combining perception and inference-based control in terrain-adaptive robotic systems. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

Compliant force control is a fundamental capability for enabling robots to interact safely and effectively with dynamic and uncertain environments. This paper presents a comprehensive survey of compliant force control strategies, intending to enhance safety, adaptability, and precision in applications such as physical human–robot interaction, robotic manipulation, and collaborative tasks. The review begins with a classification of compliant control methods into passive and active approaches, followed by a detailed examination of direct force control techniques—including hybrid and parallel force/position control—and indirect methods such as impedance and admittance control. Special emphasis is placed on advanced compliant control strategies applied to structurally complex robotic systems, including aerial, mobile, cable-driven, and bionic robots. In addition, intelligent compliant control approaches are systematically analyzed, encompassing neural networks, fuzzy logic, sliding mode control, and reinforcement learning. Sensorless compliance techniques are also discussed, along with emerging trends in hardware design and intelligent control methodologies. This survey provides a holistic view of the current landscape, identifies key technical challenges, and outlines future research directions for achieving more robust, intelligent, and adaptive compliant force control in robotic systems. Full article

Today, companies face complexities and uncertainties that make it difficult to manage various risks. One of the important tools for achieving success in water supply projects is the proper implementation of risk management processes and activities throughout the project’s make-span. Risk identification and assessment are two important steps in project risk management. In this research, the Fuzzy DEMATEL and Fuzzy ANP as well as Artificial Neural Network methods are exploited for the analyzing and ranking of environmental risks of water supply projects. Risks are classified and then prioritized by the Fuzzy ANP and Artificial Neural Network methods into four main categories, including technical, organizational, project management, and external risks. The weight of each of the technical, organizational, project management, and external risks using the ANP method was obtained as 0.31, 0.26, 0.25, and 0.18, respectively, and the following weights were obtained using the Artificial Neural Network: 0.42, 0.27, 0.22, and 0.09, respectively. The results show that although the exact weights differed between methods, especially for technical and external risks, the overall prioritization of risk categories followed a broadly consistent pattern. In addition, the risk associated with the suppliers obtained the highest weight among the external risks; the risk associated with the high cost of materials gained the highest weight among the organizational risks; the risk associated with the requirements acquired the highest weight among the technical risks; and finally, the risk associated with communication achieved the highest weight among the project management risks. The method presented in this research helps project managers and decision-makers in the water supply industry to make a better and more realistic risk assessment by considering the mutual effects of project risks. Full article

Aquaculture has emerged as a sustainable alternative to meet the growing demand for aquatic products while preserving natural ecosystems. This study presents the design, simulation, and experimental validation of an intelligent multivariable control system for aquaculture tanks aimed at cultivating Mugil incilis, a native species of the Colombian Caribbean. The system integrates three control strategies: a classical Proportional-Integral-Derivative (PID) controller, a fuzzy logic–based PID controller, and a neural network predictive controller. All strategies were evaluated in simulation using a third-order transfer function model identified from real pond data. The fuzzy PID controller reduced the mean squared error (MSE) by 66.5% compared to the classical PID and showed faster settling times and lower overshoot. The neural predictive controller, although anticipatory, exhibited high computational cost and instability. Only the fuzzy PID controller was implemented and validated experimentally, demonstrating robust, accurate, and stable regulation of potential hydrogen (pH), dissolved oxygen, and salinity under dynamic environmental conditions. The system operated in real time on embedded hardware powered by a solar kit, confirming its suitability for rural or off-grid aquaculture contexts. This approach provides a viable and scalable solution for advancing intelligent, sustainable aquaculture practices, particularly for sensitive native species in tropical regions. Full article

This study presents advanced control and energy management strategies for uncertain wind energy systems using a Takagi–Sugeno (T-S) fuzzy modeling framework. To address key challenges, such as system uncertainties, external disturbances, and input delays, the study integrates a fuzzy H∞ robust control approach with a neural network-based delay compensation mechanism. A fuzzy observer-based H∞ tracking controller is developed to enhance robustness and minimize the impact of disturbances. The stability conditions are rigorously derived using a quadratic Lyapunov function, H∞ performance criteria, and Young’s inequality and are expressed as Linear Matrix Inequalities (LMIs) for computational efficiency. In parallel, a neural network-based controller is employed to compensate for the input delays introduced by online learning processes. Furthermore, an energy management layer is incorporated to regulate the power flow and optimize energy utilization under varying operating conditions. The proposed framework effectively combines control and energy coordination to improve the systems’ performance. The simulation results confirm the effectiveness of the proposed strategies, demonstrating enhanced stability, robustness, delay tolerance, and energy efficiency in wind energy systems. Full article

The deep and ultra-deep oil and gas resources often have the characteristics of high temperature and high pressure, with complex pressure systems and narrow safety density windows, so risks such as gas invasion and overflow are easy to occur during the drilling. In response to the problems of low management efficiency and large gas kick by traditional gas invasion treatment methods, this paper respectively established and compared three intelligent control models for bottom hole pressure (BHP) based on a PID controller, a fuzzy PID controller, and a fuzzy neural network PID controller based on the non-isothermal gas–liquid–solid three-phase transient flow heat transfer model in the annulus. The results show that compared with the PID controller and the fuzzy PID controller, the fuzzy neural network PID controller can adjust the control parameters adaptively and optimize the control rules in real-time; the efficiency of the fuzzy neural network PID controller to deal with a gas kick is improved by 45%, and the gas kick volume in the process of gas kick is reduced by 63.12%. The principal scientific novelty of this study lies in the integration of a fuzzy neural network PID controller with a non-isothermal three-phase flow model, enabling adaptive and robust bottom hole pressure regulation under complex gas invasion conditions, which is of great significance for reducing drilling risks and ensuring safe and efficient drilling. Full article

This study proposes an enhanced derivative control strategy, named $PI$-$D^{\AE }$, designed to overcome key limitations of the derivative ($D$) term, such as noise amplification, derivative kick $(D$-$k)$, and tuning difficulties. These issues often arise in high-frequency or rapidly changing systems, in which traditional PID controllers struggle. The proposed solution introduces a novel adaptive exponent algorithm (Æ) that dynamically modulates the $D$ term based on the evolving relationship between system output and setpoint. This yields the $PI$-$D^{\AE }$ controller, which adapts in real time to changing conditions. The results show significant performance improvements. Simulation results on two systems demonstrate that $PI$-$D^{\AE }$ achieves a 90% faster response time, a 35% reduction in peak time, and a 100% improvement in settling time compared with conventional PID controllers, all while maintaining a near-zero steady-state error even under external disturbances. Unlike more-complex alternatives such as fuzzy logic, neural networks, or sliding mode control, $PI$-$D^{\AE }$ retains the simplicity and robustness of PID, avoiding high computational costs or intricate setups. This adaptive exponent strategy offers a practical and scalable enhancement to classical PID, improving performance and robustness without added complexity, and thus provides a promising control solution for real-world applications in which simplicity, adaptability, and reliability are essential. Full article

Wire electrical discharge machining (WEDM), as a significant branch of non-traditional machining technologies, is widely applied in fields such as mold manufacturing and aerospace due to its high-precision machining capabilities for hard and complex materials. This paper systematically reviews the research progress in WEDM process optimization from two main perspectives: traditional optimization methods and artificial intelligence (AI) techniques. Firstly, it discusses in detail the applications and limitations of traditional optimization methods—such as statistical approaches (Taguchi method and response surface methodology), Adaptive Neuro-Fuzzy Inference Systems, and regression analysis—in parameter control, surface quality improvement, and material removal-rate optimization for cutting metal materials in WEDM. Subsequently, this paper reviews AI-based approaches, traditional machine-learning methods (e.g., neural networks, support vector machines, and random forests), and deep-learning models (e.g., convolutional neural networks and deep neural networks) in aspects such as state recognition, process prediction, multi-objective optimization, and intelligent control. The review systematically compares the advantages and disadvantages of traditional methods and AI models in terms of nonlinear modeling capabilities, adaptability, and generalization. It highlights that the integration of AI by optimization algorithms (such as Genetic Algorithms, particle swarm optimization, and manta ray foraging optimization) offers an effective path toward the intelligent evolution of WEDM processes. Finally, this investigation looks ahead to the key application scenarios and development trends of AI techniques in the WEDM field for cutting metal materials. Full article