Search Results (2,870)

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

Underactuated robotic systems are appealing for industrial use due to their reduced actuator number, which lowers energy consumption and system complexity. Underactuated systems are, however, often affected by residual vibrations. This paper addresses the challenge of generating energy-optimal trajectories while imposing theoretical null residual (and yet practical low) vibration in underactuated systems. The trajectory planning problem is cast as a constrained optimal control problem (OCP) for a two-degree-of-freedom revolute–revolute planar manipulator. The proposed method produces energy-efficient motion while limiting residual vibrations under motor torque limitations. Experiments compare the proposed trajectories to input shaping techniques (ZV, ZVD, NZV, NZVD). Results show energy savings that range from 12% to 69% with comparable and negligible residual oscillations. 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

Model predictive control (MPC) has become a popular method in motor control due to its high adaptability to multivariate control. However, one issue for this control system is constructing a reasonable cost function (CF) and obtaining appropriate weighting factors (WFs) within it. This paper addresses the issue of effectively reducing torque ripple and current harmonic content in permanent magnet synchronous motors (PMSM). Within the three-vector model predictive current control (TV-MPCC) strategy for PMSM, a new CF including current error and switching frequency terms is constructed. Combined with the technique for order preference by similarity to ideal solution (TOPSIS), the optimal control vector is obtained. Compared with traditional methods, this method reduces the complexity of adjusting WFs in the CF. Simulation results show that the motor’s torque ripple and current harmonic content are effectively reduced. Both the steady state and dynamic performance of the PMSM are also improved by means of the proposed multi-objective MPC for current error and switching frequency. 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

Conventional space vector pulse width modulation (CSVPWM) with the fixed switching frequency generates significant sideband harmonics in the three-phase voltage. Discrete random switching frequency SVPWM (DRSF-SVPWM) methods have been widely applied in motor control systems for the suppression of tone harmonic energy. To further reduce the amplitude of the high-frequency harmonic with a limited switching frequency variation range, this paper proposes a time-division subbands beta distribution random SVPWM (TSBDR-SVPWM) method. The overall frequency band of the switching frequency is equally divided into N subbands, and each fundamental cycle of the line voltage is segmented into 2*(N-1) equal time intervals. Additionally, within each time segment, the switching frequency is randomly selected from the corresponding subband and follows the optimal discrete beta distribution. The switching frequency harmonic energy in the line voltage spectrum spreads across multiple frequency subbands and discrete frequency components, thereby forming a more uniform power spectrum of the line voltage. Both simulation and experimental results validate that, compared with CSVPWM, the sideband harmonic amplitude is reduced by more than 8.5 dB across the entire range of speed and torque conditions in the TSBDR-SVPWM. Furthermore, with the same variation range of the switching frequency, the proposed method achieves the lowest switching frequency harmonic amplitude and flattest line voltage spectrum compared with several state-of-the-art random modulation methods. Full article

This paper proposes a hybrid methodology for one-step-ahead torsional torque estimation in an electric drive with an elastic connection. The approach integrates Nonlinear Autoregressive Neural Networks with Exogenous Inputs (NARX NNs) and model-based feedback. The NARX model uses real-time and historical motor speed and torque signals as inputs while leveraging physics-derived torsional torque as a feedback input to refine estimation accuracy and robustness. While model-based methods provide insight into system dynamics, they lack predictive capability—an essential feature for proactive control. Conversely, standalone NARX NNs often suffer from error accumulation and overfitting. The proposed hybrid architecture synergises the adaptive learning of NARX NNs with the fidelity of physics-based feedback, enabling proactive vibration damping. The method was implemented and evaluated on a two-mass drive system using an IP controller and additional torsional torque feedback. Results demonstrate high accuracy and reliability in one-step-ahead torsional torque estimation, enabling effective proactive vibration damping. MATLAB 2024a/Simulink and dSPACE 1103 were used for simulation and hardware-in-the-loop testing. Full article

This paper presents an online efficiency optimization strategy for a six-phase induction generator (6PIG) operating in both healthy and faulty modes for micro-hydropower applications. The proposed method is based on an extended Loss Model Control (LMC) approach, in which the direct axis stator current Id is dynamically optimized in real time to minimize the total electrical losses. Unlike conventional LMC strategies, this method explicitly incorporates switching losses into the loss model, along with stator and rotor copper losses and iron losses. The optimization problem is solved using a numerical minimization routine, allowing the control system to adapt continuously to variations in torque requests. The proposed approach is validated under both healthy and faulty configurations of the 6PIG. It is implemented and tested through simulation in MATLAB/Simulink^® and experimentally validated on a 24 kW squirrel cage six-phase induction generator (SC6PIG). The results are compared in terms of power losses, energy saving, and efficiency. Full article

Currently, the dismantling of aluminum stranded conductors remains predominantly manual due to their structural complexity. To enhance the efficiency and reduce the labor intensity for dismantling aluminum stranded conductors, this study presents an innovative torque-driven dismantling method validated through dynamic simulation analysis. To demonstrate the proposed method, a modular prototype machine that includes four main functional modules (transmission, untwisting, separation, and shearing) was developed. Experimental results from the prototype dismantling machine demonstrated that when processing JL/G3A-500/65 conductors (Sichuan Star Cable Co., Ltd., Leshan, China) under the following operational parameters—0.5 rad/s rotational speed, 10 cm extension length, 2400 N clamping force, and 40 N·m torque application—the system achieved a single-layer dismantling efficiency exceeding 98%. This represents a significant improvement in operational speed compared to traditional manual methods. The developed machine achieved collaborative control of axial feed, reverse untwisting, and automatic shearing, elevating the untwisting qualification rate to 95%. This solution provides an efficient and safe approach to conductor inspection, demonstrating substantial engineering application value. Full article

Compound steering technology has been extensively adopted in military logistics and related applications, owing to its superior maneuverability and enhanced stability compared to conventional systems. To enhance the steering efficiency and dynamic response of distributed-drive unmanned platforms under low driving torque conditions, this study investigates their unique compound steering system. Specifically, a compound steering dynamics model is established, and a hierarchical stability control strategy, along with a model predictive control-based trajectory tracking algorithm, are innovatively proposed. First, a compound steering platform dynamics model is established by combining the Ackermann steering and skid yaw moment methods. Then, a trajectory tracking controller is designed using model predictive control algorithm. Finally, the additional yaw moment is calculated based on the lateral velocity error and yaw rate error, with stability control allocation performed using a fuzzy control algorithm. Comparative hardware-in-the-loop experiments are conducted for compound steering, Ackermann steering, and skid steering. The experimental results show that the compound steering technology enables unmanned platforms to achieve trajectory tracking tasks with a lower torque, faster speed, and higher efficiency. Full article

The optimization and control of the wellbore trajectory is one of the important technologies to improve drilling efficiency, reduce drilling cost, and ensure drilling safety in the process of modern oil and gas exploration and development. In this paper, a multi-objective wellbore trajectory optimization mathematical model is established, which takes into account the five factors of wellbore trajectory length, friction, torque, trajectory complexity, and target accuracy. A DR-NSGA-III-MGA algorithm (dynamic reference NSGA-III with multi-granularity adaptation) is proposed. By introducing multi-granularity reference vector generation and an information entropy-guided search direction adaptation mechanism, the performance of the algorithm in the complex target space is improved, and the three-stage wellbore trajectory is optimized. Simulation experiments show that the DR-NSGA-III-MGA algorithm is stable in a variety of complex problems, while maintaining good convergence, and has good generalization ability and practical application value. Full article

Using ultra-short dental implants is a promising alternative to extensive bone grafting procedures for patients with atrophic posterior mandibles and vertical bone loss. However, the amount of insertion torque (IT) applied during implant placement significantly influences stress distribution in the peri-implant bone, which affects implant stability and long-term success. Materials and Methods: This study used finite element analysis (FEA) to examine how different insertion torques (35 N·cm and 75 N·cm) affect stress distribution in cortical and trabecular bone types D2 and D4 surrounding ultra-short implants. Von Mises equivalent stress values were compared with ultimate bone strength thresholds to evaluate the potential for microdamage during insertion. Results: The findings demonstrate that increasing IT from 35 N·cm to 75 N·cm led to a significant increase in peri-implant bone stress. Specifically, cortical bone stress in D4 bone increased from approximately 79 MPa to 142 MPa with higher IT, exceeding physiological limits and elevating the risk of microfractures and bone necrosis. In contrast, lower IT values kept stress within safe limits, ensuring optimal primary stability without damaging the bone. These results underscore the need to strike a balance between achieving sufficient implant stability and avoiding mechanical trauma to the surrounding bone. Conclusions: Accurate control of insertion torque during the placement of ultra-short dental implants is crucial to minimize bone damage and promote optimal osseointegration. Excessive torque, especially in low-density bone, can compromise implant success by inducing excessive stress, thereby increasing the risk of early failure. Full article

This study explored the effects of adding a newly developed type of polydextrose on the appearance, sensory score, and textural parameters of steamed bread and the microstructure of dough, as well as the pasting, thermal, and thermal mechanical properties of high-gluten wheat flours. The results revealed that, compared with a control sample, 3–10% of polydextrose addition significantly increased the hardness, adhesiveness, gumminess, and chewiness of steamed bread, but other textural parameters like springiness, cohesiveness, and resilience remained basically the same. Further, in contrast to the control sample, 3–10% polydextrose addition significantly reduced the specific volume and width/height ratio of steamed bread but increased the brightness index, yellowish color, and color difference; improved the internal structure; and maintained the other sensory parameters and total score. Polydextrose addition decreased the peak, trough, final, breakdown, and setback viscosity of the pasting of wheat flour suspension solutions but increased the pasting temperature. Polydextrose additions significantly reduced the enthalpy of gelatinization and the aging rate of flour paste but increased the peak temperature of gelatinization. A Mixolab revealed that, with increases in the amount of added polydextrose, the dough’s development time and heating rate increased, but the proteins weakened, and the peak torque of gelatinization, starch breakdown, and starch setback torque all decreased. Polydextrose additions increased the crystalline regions of starch, the interaction between proteins and starch, and the β-sheet percentage of wheat dough without yeast and of steamed bread. The amorphous regions of starch were increased in dough through adding polydextrose, but they were decreased in steamed bread. Further, 3–10%of polydextrose addition decreased the random coils, α-helixes, and β-turns in dough, but the 3–7% polydextrose addition maintained or increased these conformations in steamed bread, while 10% polydextrose decreased them. In unfermented dough, as a hydrogel, the 5–7% polydextrose addition resulted in the formation of a continuous three-dimensional network structure with certain adhesiveness and elasticity, with increases in the porosity and gas-holding capacity of the product. Moreover, the 10% polydextrose addition further increased the viscosity, freshness, and looseness of the dough, with smaller and more numerous holes and indistinct boundaries between starch granules. These results indicate that the 3–10% polydextrose addition increases the chewiness and freshness of steamed bread by improving the gluten network structure. This study will promote the addition of polydextrose in steamed bread to improve shelf-life and dietary fiber contents. Full article

Realizing path tracking is crucial for improving the accuracy and efficiency of unmanned vehicle operations. In this paper, a path tracking hierarchical control method based on DDPG-PP is proposed to improve the path tracking accuracy of tracked unmanned vehicles. Constrained by the objective of minimizing path tracking error, with the upper controller, we adopted the DDPG method to construct an adaptive look-ahead distance optimizer in which the look-ahead distance was dynamically adjusted in real-time using a reinforcement learning strategy. Meanwhile, reinforcement learning training was carried out with randomly generated paths to improve the model’s generalization ability. Based on the optimal look-ahead distance output from the upper layer, the lower layer realizes precise closed-loop control of torque, required for steering, based on the PP method. Simulation results show that the path tracking accuracy of the proposed method is better than that of the LQR and PP methods. The proposed method reduces the average tracking error by 94.0% and 79.2% and the average heading error by 80.4% and 65.0% under complex paths compared to the LQR and PP methods, respectively. Full article

In air-conditioning compressors operating under ultra-low temperature conditions, both the rotational speed and load torque are at high levels, demanding pump motors that offer high efficiency and high power at high speeds. Electrically excited synchronous motors (EESMs) satisfy these operational requirements by leveraging their inherent wide-speed field-weakening capability and superior high-speed performance characteristics. Current research on EESM primarily targets electric vehicle applications, with a high-efficiency design focused on medium and low speeds. Excitation design under constant-power–speed extension remains insufficiently explored. To address it, this paper proposes an EESM design methodology optimized for high-speed efficiency and constant-power excitation control. Key EESM parameters are determined through a dynamic phasor diagram, and design methods for turn number, split ratio, and other parameters are proposed to extend the high-efficiency region into the high-speed range. Additionally, a power output modulation strategy in the field-weakening region is introduced, enabling dynamic high-power regulation at high speed through excitation adjustment. Compared to similarly sized PMSMs, the proposed EESM exhibits consistently superior efficiency beyond 10,000 rpm, delivering 19% and 49% higher power output at 12,000 rpm and 14,000 rpm, respectively, relative to conventional pump-drive PMSMs. Experimental validation via a prototype confirms excellent high-speed efficiency and sustained constant-power performance, in alignment with the design targets. Full article