Search Results (284)

The paper presents an experimentally validated regression model for dry clutch friction lining wear, accounting for the influence of clutch temperature, initial slip speed, torque, and closing time. The experimental data have been collected by using a custom-designed disk-on-disk computer-controlled tribometer and conducting repetitive real operation-like clutch closing cycles for different levels of the above operating parameters. The model is designed to be cycle-wise, predicting cumulative worn volume expectation and standard deviation after each closing cycle. It is organized around three distinctive submodels, which provide predictions of: (i) wear rate expectation, (ii) wear rate variance, and (iii) elevated wear rate during run-in operation. Finally, the wear rate expectation and variance submodels and the overall, cumulative worn volume model are validated on independent experimental datasets. The main novelty of the presented research lies in the development of stochastic multi-input cycle-wise dry cutch wear model for clutch design and monitoring applications. Full article

Series elastic actuators (SEAs) represent a key technological solution to enhance safety, performance, and adaptability in robotic devices for physical training. Their ability to decouple the rigid actuator’s mechanical impedance from the load, combined with passive absorption of external disturbances, makes them particularly suitable for pediatric applications. In children aged 2 to 5 years—where motor control is still developing and movements can be unpredictable or unstructured—SEAs provide a compliant mechanical response that ensures user protection and enables safe physical interaction. This study explores the role of SEAs as a central component for imparting compliance and backdrivability in robotic systems designed for upper-limb training. A dynamic model is proposed, incorporating interaction with the user’s limb, along with a computed torque control strategy featuring integral action. The system’s performance is validated through simulations and experimental tests, demonstrating stable trajectory tracking, disturbance absorption, and effective impedance decoupling. The results support the use of SEAs as a foundational technology for developing safe adaptive robotic solutions in pediatric contexts capable of responding flexibly to user variability and promoting secure interaction in early motor development environments. Full article

This study presents a novel state estimation approach integrating Deep Neural Networks (DNNs) into Moving Horizon Estimation (MHE). This is a shift from using traditional physics-based models within MHE towards data-driven techniques. Specifically, a Long Short-Term Memory (LSTM)-based DNN is trained using synthetic data derived from a high-fidelity thermal model of a Permanent Magnet Synchronous Machine (PMSM), applied within a thermal derating torque control strategy for battery electric vehicles. The trained DNN is directly embedded within an MHE formulation, forming a discrete-time nonlinear optimal control problem (OCP) solved via the acados optimization framework. Model-in-the-Loop simulations demonstrate accurate temperature estimation even under noisy sensor conditions and simulated sensor failures. Real-time implementation on embedded hardware confirms practical feasibility, achieving computational performance exceeding real-time requirements threefold. By integrating the learned LSTM-based dynamics directly into MHE, this work achieves state estimation accuracy, robustness, and adaptability while reducing modeling efforts and complexity. Overall, the results highlight the effectiveness of combining model-based and data-driven methods in safety-critical automotive control systems. Full article

Driver models, which provide mathematical or computational representations of human driving behavior, are crucial for intelligent driving systems by enabling stable and repeatable operations. However, existing models typically employ fixed weighting parameters to simulate preview delay, failing to reflect individual driver differences and real-time dynamic behaviors. This paper proposes a Brain-Memory Driver Model (BMDM) that emulates human brain memory mechanisms to dynamically adjust preview weights by integrating global path curvature, real-time vehicle speed, and steering torque. This emulation involves a three-stage process: capturing data in an Instantaneous Memory (IM) region, filtering data via a forgetting mechanism in a Short-Time Memory (STM) region to reduce scale, and retaining data based on correlation strength in a Long-Time Memory (LTM) region for persistent mining. By deploying a trained behavioral memory database, the model dynamically calibrates preview weights based on the driver’s state and real-time curvature variations under different road conditions. This enables the model to more accurately simulate authentic preview characteristics and improves its adaptability. Simulation results from an automated steering case study demonstrate that the improved model exhibits control performance closer to the real driving process, reproducing authentic steering behavior within the human–vehicle–road closed-loop system from an intelligent biomimetic perspective. Full article

This paper presents the development and analysis of a novel 6-DOF Cartesian parallel mechanism intended for use as a haptic device for initial sonography training. The system integrates a manipulator designed for delivering force feedback in five degrees of freedom; however, in the current stage, only mechanical architecture and kinematic validation have been conducted. Future enhancements will focus on implementing and evaluating closed-loop force control to enable complete haptic feedback. To assess the kinematic performance of the mechanism, a detailed kinematic model was developed, and both the Kinematic Conditioning Index (KCI) and Global Conditioning Index (GCI) were computed to evaluate the system’s dexterity. A trajectory simulation was conducted to validate the mechanism’s movement, using motion patterns typical in sonography procedures. Quasi-static analysis was performed to study the transmission of force and torque for generating realistic haptic feedback, critical for simulating real-life sonography. The simulation results showed consistent performance, with dexterity and torque distribution confirming the suitability of the mechanism for haptic applications in sonography training. Additionally, structural analysis verified the robustness of key components under expected loads. In order to validate the proposed design, the prototype was constructed using a combination of aluminum components and 3D-printed ABS parts, with Igus^® linear guides for precise motion. The outcomes of this study provide a foundation for the further development of a low-cost, effective sonography training system. Full article

Assistive technologies, particularly multi-fingered robotic hands (MFRHs), are critical for enhancing the quality of life for individuals with upper-limb disabilities. However, achieving precise and stable control of such systems remains a significant challenge. This study proposes an Improved Grey Wolf Optimization (IGWO)-tuned Linear Quadratic Regulator (LQR) to enhance the control performance of an MFRH. The MFRH was modeled using Denavit–Hartenberg kinematics and Euler–Lagrange dynamics, with micro-DC motors selected based on computed torque requirements. The LQR controller, optimized via IGWO to systematically determine weighting matrices, was benchmarked against PID and PID-PSO controllers under diverse input scenarios. For step input, the IGWO-LQR achieved a settling time of 0.018 s with zero overshoot for Joint 1, outperforming PID (settling time: 0.0721 s; overshoot: 6.58%) and PID-PSO (settling time: 0.042 s; overshoot: 2.1%). Similar improvements were observed across all joints, with Joint 3 recording an IAE of 0.001334 for IGWO-LQR versus 0.004695 for PID. Evaluations under square-wave, sine, and sigmoid inputs further validated the controller’s robustness, with IGWO-LQR consistently delivering minimal tracking errors and rapid stabilization. These results demonstrate that the IGWO-LQR framework significantly enhances precision and dynamic response. Full article

With the increasing capacity of wind turbines, key components including the rotor diameter, tower height, and tower radius expand correspondingly. This heightened inertia extends the response time of pitch actuators during rapid wind speed variations occurring above the rated wind speed. Consequently, wind turbines encounter significant output power oscillations and complex structural loading challenges. To address these issues, this paper proposes a novel pitch control strategy combining an effective wind speed estimation with the inverse system method. The developed control system aims to stabilize the power output and rotational speed despite wind speed fluctuations. Central to this approach is the estimation of the aerodynamic rotor torque using an extended Kalman filter (EKF) applied to the drive train model. The estimated torque is then utilized to compute the effective wind speed at the rotor plane via a differential method. Leveraging this wind speed estimate, the inverse system technique transforms the nonlinear wind turbine dynamics into a linearized, decoupled pseudo-linear system. This linearization facilitates the design of a more agile pitch controller. Simulation outcomes demonstrate that the proposed strategy markedly enhances the pitch response speed, diminishes output power oscillations, and alleviates structural loads, notably at the tower base. These improvements bolster operational safety and stability under the above-rated wind speed conditions. Full article

We address the problem of humanoid locomotion in 3D environments consisting of planar regions with arbitrary inclination and elevation, such as staircases, ramps, and multi-floor layouts. The proposed framework combines an offline randomized footstep planner with an online control pipeline that includes a model predictive controller for gait generation and a whole-body controller for computing robot torque commands. The planner efficiently explores the environment and returns the highest-quality plan it can find within a user-specified time budget, while the control layer ensures dynamic balance and adequate ground friction. The complete framework was evaluated via dynamic simulation in MuJoCo, placing the JVRC1 humanoid in four scenarios of varying complexity. Full article

This study introduces an innovative set of guide vanes that increase the efficiency of Vertical Axis Wind Turbines (VAWT) using winds generated by vehicles traveling on highways. The increase in efficiency is based on enhancing the airflow interaction as the vehicle moves past the turbine. Initial Computational Fluid Dynamics (CFD) simulations with two guide vanes setups demonstrated a 56.81% increase in power output under wind generated by passenger vehicles. Further design enhancements, incorporating three guide vanes with optimized geometries, led to a 242% improvement in power generation. Additional simulations evaluated the performance under wind conditions generated by larger vehicles, such as buses. The three guide vanes configuration yielded a 102% increase in energy capture efficiency in these scenarios. The findings suggest that vehicle-induced winds—typically an untapped energy source—can be effectively harvested using tailored turbine system designs. By integrating passive flow control strategies such as guide vanes, VAWTs can operate more efficiently in highway environments. This research highlights a novel pathway for enhancing renewable energy systems and supports broader efforts toward sustainable energy development through the utilization of unconventional wind sources. This performance enhancement is primarily due to the aerodynamic redirection of airflow toward the advancing blade and away from the returning blade, reducing drag and improving torque generation. Full article

To address the limitations of traditional single-motor bionic ornithopters in terms of environmental adaptability and lift capacity, this study proposes a dual-motor independently driven system utilizing a cross-shaft single-gear crank mechanism to achieve adjustable flap speed and wing frequency, thereby enabling asymmetric flapping for enhanced environmental adaptability. The design integrates a two-stage reduction gear group to optimize torque transmission and an S1223 high-lift airfoil to improve aerodynamic efficiency. Multiphysics simulations combining computational fluid dynamics (CFD) and finite element analysis (FEA) demonstrate that, under flapping frequencies of 1–3.45 Hz and wind speeds of 1.2–3 m/s, the optimized model achieves 50% and 60% improvements in lift and thrust coefficients, respectively, compared to the baseline. Concurrently, peak stress in critical components (e.g., cam disks and wing rods) is reduced by 37% to 41 MPa, with significantly improved stress uniformity. These results validate the dual-motor system’s capability to dynamically adapt to turbulent airflow through the precise control of wing kinematics, offering innovative solutions for applications such as aerial inspection and precision agriculture. Full article

Switched Reluctance Machines (SRMs) are gaining increasing traction within the industrial sector, primarily due to their inherently simple and robust structure. Nevertheless, SRMs are characterized by two major drawbacks—high torque ripple and strong radial forces—both of which render them less suitable for applications requiring smooth operation, such as Electric Vehicles (EVs). To address these limitations, researchers and designers focus on optimizing these critical performance metrics during the design phase. In recent years, the concept of System-Level Design Optimization (SLDOM) has been introduced and applied to SRM drive systems, where both the machine and the controller are simultaneously considered within the optimization framework. This integrated approach has shown significant improvements in mitigating the aforementioned issues. This paper aims to review the existing literature concerning the SLDOM applied to SRMs, highlighting the key methodologies and findings from studies conducted in recent years. Despite its promising outcomes, the adoption of SLDOM remains limited due to its high computational cost and complexity. In response to these challenges, the paper discusses complementary techniques used to enhance the optimization process, such as search space and computational time reduction strategies, along with the associated challenges and potential solutions. Finally, two critical directions for future research are identified, which are expected to influence the development of the SLDOM and its application to SRMs in the coming years. Full article

The inherent static instability of bicycles poses significant safety risks, driving research into active stabilization systems within the broader field of autonomous vehicle control. This study proposes a Variable-Speed Control Moment Gyroscope (VSCMG) system for bicycle attitude stabilization, aiming to enhance rider safety and system endurance by addressing the high power consumption of traditional Single-Gimbal CMG (SGCMG) systems. A single-axis balance model was developed, employing a proportional–derivative (PD) controller to compute the total torque demand, combined with least-squares-based power-constrained optimization and a center-of-mass alignment algorithm to achieve stable control. Experimental validation was conducted on a simplified single-axis balancing setup, designed as an abstracted bicycle model for verification purposes, equipped with two VSCMG units. This setup demonstrated the rapid stabilization of a 15.5° tilt to near 0°, with significantly reduced steady-state power consumption compared to SGCMG systems, and an effective mitigation of external disturbances at 4000 RPM, though oscillations increased at 1500 RPM. The VSCMG system achieves a balance between stability and energy efficiency through dynamic flywheel speed adjustment, and future research can enhance disturbance rejection capabilities by varying the speed, offering a viable approach for long-endurance autonomous bicycles. Full article

This article discusses speed control methods for electric motor drives with elastic mechanical coupling causing torsional vibrations, which negatively affect the operation of the system. Model Predictive Control (MPC) is often presented as an effective solution; however, it is notoriously difficult to implement in real-time due to the high computational complexity of the controller. In this paper, a simplified predictive control approach in the form of Analytical MPC (aMPC) is proposed for the speed control of a two-mass motor drive. In contrast to conventional MPC, which requires complex online optimisation, aMPC derives an explicit control law analytically under simplifying assumptions, greatly reducing the computational load. The effect of the controller parameters on the drive performance is investigated and a multi-objective performance function for automatic tuning is proposed. The aMPC structure is compared with conventional State Feedback Control (SFC), including a system robustness test of both approaches. Based on simulation studies and experimental verification, the proposed structure is shown to ensure high dynamics in drive control, with smoother torque control and superior robustness for higher-load inertia ratios than SFC. Full article

In the context of the new energy hybrid ship propulsion system (NE-HSPS), the parameters of the rotor speed, torque, and current of the permanent magnet synchronous motor (PMSM) are susceptible to environmental variations and unmodeled disturbances. Conventional nonlinear controllers (e.g., backstepping, PI, and sliding mode) encounter challenges related to response speed, interference immunity, and vibration jitter. These challenges stem from the inherent uncertainties in perturbations and the limitations of the traditional nonlinear controllers. In this paper, a novel Adaptive Finite-Time Command-Filtered Backstepping Controller (AFTCFBC) is proposed, featuring a faster response time and the elimination of overshoot. The proposed controller is a significant advancement in the field, addressing the computational complexity of backstepping control and reducing the maximum steady-state error of the control output. The novel controller incorporates a Nonlinear Finite-Time Command Filter (NFTCF) adapted to the variation in motor speed. Secondly, a novel Nonlinear Sliding Mode Observer (NSMO) is proposed based on the designed nonlinear sliding mode gain function (φ(S_w)) to estimate the load disturbance of the electric propulsion system. The Uncertainty Parameter-Adaptive law (UPAL) is designed based on Lyapunov theory to improve the robust performance of the system. The construction of a simulation model of a hybrid ship PMSM under four distinct working conditions, including constant speed and constant torque, the lifting and lowering of speed, loading and unloading, and white noise interference, is presented. The results of this study demonstrate a significant reduction in speed-tracking overshoot to zero, a substantial decrease in integral squared error by 90.15%, and a notable improvement in response time by 18.6%. Full article

Numerous voltage vectors exist in a neutral-point-clamped (NPC) three-level inverter. Traditional three-level model predictive control incurs a heavy online computational burden. This paper proposes a model predictive torque control strategy for NPC three-level inverters with permanent magnet synchronous motor systems. First, the relationship among the stator flux linkage vector position, the torque–flux linkage increment, and the stator flux linkage variation is analyzed. Then, the candidate voltage vector sector is determined, and the candidate voltage vectors are selected from it. Meanwhile, the direction of the load current flowing to the neutral point and the voltage difference between the upper and lower capacitors are evaluated. As a result, redundant small vectors are effectively selected, reducing the number of candidate voltage vectors to six and avoiding the computation of all possible vectors. The experimental results from an NPC three-level inverter–permanent magnet synchronous motor system verify that this strategy significantly reduces the computational complexity and provides excellent dynamic and steady-state performance. Full article