Search Results (272)

For low-speed heavy-load steering of electric forklifts, conventional three-loop proportional–integral (PI) control employs a fixed saturation limit on the position-loop output. Consequently, the maximum allowable speed cannot be adjusted according to load variations. Under light-load conditions, the steering motor speed is excessively constrained, which wastes the available voltage margin. Under heavy-load conditions, the allowable speed may exceed the voltage limit, thereby causing voltage saturation. Moreover, load-torque feedforward compensation is commonly adopted to improve load-carrying capability. However, at medium and high speeds, excessive feedforward action may cause voltage saturation and current-vector offset. This can lead to loss of control of the steering motor. To address these issues, a voltage-limit-constrained dynamic saturation and load-torque feedforward control strategy is proposed for electric forklift steering systems. First, fuzzy PI control is adopted in the position loop. Then, considering the nearly identical direct-axis and quadrature-axis inductances of a surface-mounted permanent magnet synchronous motor (PMSM), the direct-axis current is set to zero. An analytical expression of the maximum safe speed is derived with the quadrature-axis current as the only independent variable. Based on this expression, a dynamic saturation limit is designed for the position-loop output. Finally, a reduced-order disturbance observer (DOB) is utilized to estimate the equivalent load torque in real time. The current feedforward gain is dynamically regulated according to the voltage margin. This compensates for torque limitation caused by speed-loop saturation while preventing voltage saturation. A Simulink simulation platform is developed using a forklift as the case study. The results demonstrate that, compared with the conventional three-loop PI controller, the proposed strategy reduces the no-load 180° step-response time by 30%. Under heavy-load and large-angle steering conditions, the voltage margin is maintained at approximately 10%. Full article

Motoneurons are under strong pressure to maintain stable motor output throughout an individual life, through homeostatic regulation of their electrical properties. Dysregulated spinal motoneuron excitability has long been implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS). Recent work in SOD1^G93A mice suggests that the homeostatic response of motoneurons becomes dysregulated as cellular processes are disrupted by the disease, causing fluctuations in motoneuron electrical properties. Yet, few studies directly test whether ALS motoneurons respond differently than wild-type motoneurons to a common chronic perturbation. Here, we used in vivo electrophysiology to test whether motoneurons from pre-symptomatic SOD1^G93A mice modulate excitability differently than wild-type motoneurons in response to the same homeostatic perturbation: chronic inhibition exerted by the benzodiazepine diazepam. Using linear mixed-effects statistical models, we assessed whether diazepam treatment differentially modulated passive properties, firing behavior, spike properties, and/or synaptic inputs in SOD1^G93A versus wild-type motoneurons. We identified a significant genotype × treatment interaction effect selectively for properties related to passive membrane integration and spike initiation, including membrane time constant, peak input resistance, and recruitment current. In contrast, firing gain, spike waveform characteristics, and synaptic inputs were largely unaffected. These findings indicate that sustained inhibitory perturbation selectively triggered overactive intrinsic compensatory mechanisms in SOD1^G93A motoneurons rather than inducing widespread changes in firing or synaptic transmission. Together, our results provide direct evidence for over-active homeostatic control of motoneuron excitability and support a view of motoneuron dysfunction in ALS as a problem of altered feedback regulation rather than simply hyper- or hypo-excitability. Full article

Background/Objectives: Presynaptic terminals (PTs) in the neuromuscular junction (NMJ) are essential regulators of skeletal muscle function and are responsible for the translation of electrical impulses from motor neurons into muscle contraction. The present exploratory study aimed to compare PT adaptations in spinalis muscle samples from the concave and convex regions of the spine in three cases of advanced scoliosis, which exhibited marked asymmetry in muscle development. Methods: Spinalis muscle sample pairs were retrieved after surgical procedures and subjected to immunofluorescence (IF)-based spatial analysis of PTs, histological assessment of muscle fibers, and expression analyses of inflammatory and neurotrophic proteins. Results: IF images revealed distinct differences in PT parameters between spinalis samples obtained from the corresponding concave and convex sides of spinal deformities. Advanced statistical models revealed a consistent tendency for concave spinalis muscles to develop lower PT numbers, along with decreased expression of relevant components, neurofilament M, and synaptic vesicle glycoprotein 2. Moreover, these impairments were accompanied by increased expression levels of IFN alpha, which has been previously implicated in NMJ disorders, neuropathies, and myopathies. Conclusions: In the concave regions of spinal deformities, continuously compressed spinalis muscles may be particularly susceptible to PT alteration and denervation. However, comprehensive multicenter validation studies are required to better define the relationships among PT alterations, IFN alpha expression, and muscle tissue compression. Full article

Electric motor control may be challenging due to nonlinearity, cross-coupling and current and voltage constraints. Multivariable action may enhance the effectiveness of control on electric motors. This work presents the real-time implementation of a Model Predictive Control (MPC) strategy for current regulation in a low-power synchronous electric motor, on a low-cost microcontroller platform. The experimental setup employs a back-to-back configuration with a DC motor operating as a generator, enabling comparative analysis of the impact of different cost-function formulations on the closed-loop dynamics. Both transient and steady-state capabilities have been investigated through suitable key performance indexes. Full article

The energy consumed by thermal management systems strongly affects the driving range of battery electric vehicles. In this study, we develop an integrated control strategy that couples the Sparrow Search Algorithm (SSA) with Nonlinear Model Predictive Control (NMPC) to simultaneously reduce energy consumption and satisfy cabin comfort and battery safety requirements. We construct a multiloop coupled, heat pump-based integrated thermal management model, including a compressor, heat exchangers, expansion valves, and an electro-thermal battery sub-model. Bench and vehicle-level tests confirm that the model predicts the refrigerant mass flow rate and heating capacity with mean relative errors of 4.76% and 4.30%, respectively. The SSA is used to tune the NMPC weighting parameters offline, minimizing the mean absolute errors of the cabin temperature, battery temperature, and total system energy consumption. The resulting SSA-NMPC strategy is evaluated under NEDC and CLTC-P driving cycles. Under the investigated NEDC-based high-load assessment with representative operating conditions, the proposed strategy limits the cabin temperature overshoot to 0.35 °C and battery temperature fluctuation to 0.26 °C, while achieving a 6.31% energy saving under high-speed cruising. The proposed framework focuses on cabin and battery thermal regulation and considers motor waste heat recovery. These results demonstrate that the SSA-NMPC approach can improve thermal management performance under the investigated operating conditions. Full article

This paper proposes an optimal tracking control framework for a permanent magnet synchronous motor (PMSM) drive integrated with a quasi-Z-source (QZS) inverter for all-electric aircraft applications. Two tracking control strategies are developed: (i) an online adaptive optimal control (OAC) method for tracking motor speed and (ii) a linear quadratic integral (LQI) controller for regulating the peak DC-link voltage (PDV) of the QZS. Due to the nonlinear characteristics, parameter uncertainties, and external disturbances inherent in PMSM systems, achieving accurate speed tracking and stable DC-link voltage (DCV) regulation using a PDV control strategy under varying power flow conditions remains a significant challenge. In this study, the PMSM model is represented as a nonlinear system with strict feedback. Augmented feedforward control signals are incorporated to restructure the conventional cascade control architecture into a novel optimal control framework. Based on this formulation, a saturated adaptive optimal control law is proposed, relying on a near-optimal solution to the Hamilton–Jacobi–Isaacs (HJI) equation. This solution is approximated using an online approximator combined with an integral reinforcement learning technique. Meanwhile, an LQI controller is employed to regulate the PDV and suppress voltage fluctuations in the QZS. Simulation results demonstrate that the proposed approach significantly improves speed tracking accuracy, DCV stability, and disturbance rejection capability while improving the overall performance and reliability of PMSM drive systems. The simulation results demonstrate that the proposed control strategies have strong potential for effective application in all-electric aircraft systems, meeting the requirements of high performance and energy efficiency. Full article

Two-speed transmissions can regulate the motor operating point by changing the transmission ratio of drive systems and are an effective approach to improving both dynamic performance and energy efficiency of battery electric vehicles. However, existing gear shift strategies rarely consider the impact of transmission operating states on shift rationality and system stability, leading to limited adaptability under complex driving conditions. To address this issue, a hierarchical fuzzy evaluation and gear shift strategy matching method based on transmission operating states is proposed. First, three basic strategies are designed. Then, shift frequency and gear duty ratio are introduced to characterize transmission behavior, and a hierarchical decision framework consisting of driving demand evaluation, transmission behavior evaluation, and strategy matching is constructed to enable adaptive selection among different strategies. Furthermore, a fuzzy shift frequency correction strategy is proposed to adjust shift thresholds online, thereby reducing frequent and unnecessary shifting. Finally, simulations are conducted under multiple typical driving cycles based on a vehicle model, and experimental validation is carried out using a high-speed dual motor load test bench. The results demonstrate that the proposed strategy can effectively balance dynamic performance and energy efficiency while reducing unnecessary shifts. Full article

Driven by demanding global emission regulations and the urgent requirements for sustainable mobility, Electric Vehicles (EVs) have emerged as the primary alternative to Internal Combustion Engine (ICE) vehicles. Central to this transition is the electric propulsion system (EPS), a multidisciplinary integration of power electronics, advanced motor drives, and electrochemical energy storage. This paper provides a comprehensive overview of the current landscape of power electronic drives, focusing on the evolution of high-efficiency traction motors and next-generation energy storage systems (ESSs), and advancements in ultra-fast chargers. The analysis explores the vital impact of power converters, evaluating recent breakthroughs in wide-bandgap (WBG) semiconductors and advanced control topologies that enhance energy density and thermal management. Furthermore, the study identifies critical challenges in the design, modulation, and operational reliability of converters under dynamic automotive environments. By synthesizing current research trends and technical bottlenecks, this paper offers insights into the future trajectory of power electronics in achieving high-performance, cost-effective, and carbon-neutral transportation. Full article

Distributed Electric Propulsion aircraft have gained significant attention for advancing green aviation. However, their application is constrained by the limited energy density of batteries, resulting in weight compensation and flight range limitation. Current research on DEP energy management predominantly focuses on thrust allocation during the cruise phase while largely neglecting the energy regeneration potential during the descent phase. Conventional all-motors active energy recovery strategies force the multi-motor array to operate within a low-efficiency region, since the required drag torque is small under low aerodynamic drag conditions. To solve this issue, this paper proposes an energy recovery strategy that dynamically adjusts the number of activated motors during the descent phase of aircraft. The proposed N-Active strategy can adaptively regulate the number of operating motors, shifting motor operating points from the low-efficiency region to the high-efficiency region, which effectively decouples energy regulation within the longitudinal symmetry plane and maximizes energy recovery benefits. In this study, a high-fidelity simulation platform is established, including nonlinear aerodynamic characteristics and propeller windmilling motor efficiency models. Moreover, the optimal performance of the N-Active multi-motor cooperative energy recovery optimization strategy is verified based on the constructed platform. Simulation results demonstrate that compared with the traditional all motors active strategy, the proposed method improves battery state of charge by 11.96% and reduces virtual weight of battery. This method can effectively alleviate the weight compensation effect of distributed electric propulsion aircraft without additional physical weight increment, thereby enhancing the loading capacity of aircraft. Full article

Serotonin (5–HT) in the brain is involved in the regulation of various emotional states and behaviors. Most serotonergic neurons are located in the raphe nuclei. The raphe magnus (RMg) is one of the raphe nuclei and belongs to the caudal raphe complex. The primary goal of our research was to examine the effects of chronic, repeated electrical stimulation of the RMg on rats’ motility over a period of 15 days. During the research, 35 rats were used; 21 rats underwent electrical stimulation of the RMg (RMg-ST), while 14 rats were included in the control group (RMg-Sham). In addition, we aimed to evaluate the effects of electrical stimulation in the RMg-ST group as well as the naïve procedure in the RMg-Sham group on anxiety-related behaviors and spatial memory on selected days 30 min after the end of stimulation. We found that rats in the RMg-ST group were characterized by considerably higher locomotor activity than animals in the RMg-Sham group over a 15-day stimulation period. Stimulated animals were less anxious during the elevated plus maze on the 4th and 5th days of stimulation and demonstrated improved memory performance during the Morris water maze conducted between the 9th and 12th days of stimulation in comparison to the control animals. Furthermore, in both behavioral tests, rats’ motility when subjected to the RMg electrical stimulation was much higher than in control rats. On the last day of the 15-day stimulation period, rats were sacrificed, and their brains were collected. Brain immunofluorescent analysis revealed an increase in the number of 5–HT-positive cells in the RMg-ST group and altered activity of c-Fos-positive cells in selected brain structures connected with locomotion (secondary motor cortex), anxiety (arcuate nucleus of the hypothalamus), and spatial memory (dentate gyrus) after stimulation in comparison to the results in the RMg-Sham group. These findings suggest that locomotion may be strictly dependent on the RMg neuronal projections, and electrical stimulation of the structure influences cognitive behaviors. Full article

This study addresses pressure regulation in an induction-motor-driven centrifugal fan and introduces two complementary novelties: a Softsign-PI controller that shapes the tracking error via a Softsign nonlinearity before PI regulation and a hybrid starfish optimization with a differential evolution (hSFOA-DE) scheme for automatically tuning the controller parameters. The approach is evaluated on an experimentally validated nonlinear fan–motor model and benchmarked against modern metaheuristics—starfish optimization algorithm (SFOA), animated oat optimization (AOO), electric eel foraging optimization (EEFO), differential evolution (DE), particle swarm optimization (PSO)—as well as classical tunings—Murrill-based 2-DOF PID, Tyreus–Luyben PID and Ziegler–Nichols PI. Statistical summaries and boxplots indicate superior central tendency with reduced run-to-run variability; fitness–evolution curves show faster convergence; and time-domain performance metrics confirm improved transient and steady-state behaviour. Objective function comparisons further show the lowest values of both the Zwe-Lee Gaing (ZLG) and integral of absolute error (IAE), supporting advantages in robustness and tracking accuracy of the proposed approach. These gains reduce overshoot and cumulative error, which can lessen throttling losses and actuator duty in fan/pump service, suggesting potential energy and maintenance benefits. Full article

Dual-motor electric drive axles (e-axles) can realize basic torque vectoring through motor-torque allocation. However, without an inter-wheel power-transfer path, they still face structural limitations under motor torque–speed envelopes and severe left–right adhesion asymmetry. To address this issue, this paper proposes a reconfigurable dual-motor e-axle based on fixed-carrier compound planetary gear trains and two cross-axle clutches. By switching between controlled-slip and lock-coupled states, the proposed topology creates a switchable inter-wheel power-transfer path. As a result, it enhances yaw-rate regulation capability under high-adhesion conditions and improves escape capability under severe adhesion asymmetry. A unified kinematic–static analytical framework is established to derive closed-form capability boundaries and compact structural indices for parameter matching. Vehicle-level co-simulation on a representative rear-wheel-drive platform is then carried out for validation. Under severe split-$\mu$ conditions, the peak high-adhesion wheel torque increases from 241.72 to 695.57 N·m, and the escape time decreases from 0.43 to 0.19 s. In a representative high-adhesion step-steer case, the mean yaw-rate tracking error is reduced from 6.75 to 0.20 deg/s, while the mean differential wheel torque reaches 1.83 times that of the baseline mode. The other high-adhesion cases show the same trend. These results verify the vehicle-dynamics significance and engineering feasibility of the proposed architecture. Full article

To address the significant degradation of steering performance in dual-motor steer-by-wire (DMSBW) systems caused by the coupling of communication delays and motor faults, a robust fault-tolerant control strategy is proposed under the dual-motor collaborative driving mode. First, a matrix polytopic model is employed to describe the nonlinearities introduced by delays, establishing a delay-dependent DMSBW system dynamics model. Second, for electrical faults such as internal motor short circuits that cause a sudden drop in rotational speed, an adaptive motor-switching fault-tolerant mechanism is designed based on a smooth monitoring function to achieve rapid fault detection and steering function reconstruction. Furthermore, considering the coupled impact of delays and faults, a robust linear quadratic regulator (LQR) controller with feedforward compensation is designed to enhance system fault tolerance and robustness. Simulation results demonstrate that under steering wheel angle step input with delays, the proposed strategy achieves a rapid response without significant overshoot, and the steady-state tracking error is significantly reduced. In variable-speed single lane change maneuvers with coupled delays and severe motor faults, the peak and root mean square (RMS) errors of the front wheel angle are reduced to 0.0112 rad and 0.0031 rad, respectively. Compared to the delay-compensated nonlinear model predictive control (NMPC) and sliding mode control (SMC) strategies that do not account for delays, the peak error is reduced by 15.79% and 45.37%, while the RMS error decreases by 27.91% and 35.42%, respectively. Additionally, the peak and RMS errors of the sideslip angle and yaw rate are substantially reduced, validating the strategy’s excellent steering fault tolerance, robustness, and vehicle handling stability. Full article

To suppress the severe output torque fluctuations caused by clutch engagement when a hybrid electric vehicle equipped with a dedicated hybrid transmission (DHT) switches from pure electric (E) drive mode to hybrid (H) drive mode, a coordinated control method for power source switching is proposed. First, an adaptive fuzzy proportional-integral (PI) controller regulates the engine speed based on the speed difference between the engine and the P2 motor. Second, an active disturbance rejection control (ADRC) controller is employed for trajectory tracking to eliminate the speed difference across the synchronizer’s friction surfaces. This compensates for clutch torque variations during engine startup and ensures rapid synchronizer engagement. Finally, the torque interruption caused by the decoupling of the engine and P2 motor from the driveline is compensated via feedforward control from the P3 motor. The proposed strategy was validated through MATLAB Simulink simulations and CANape calibration tests. The results indicate that applying the proposed method to E-H mode switching slightly extended the total duration by 0.02 s. However, compared with uncoordinated control, the maximum longitudinal jerk was reduced by 73.8%, and the clutch sliding work decreased by 38.6%. This significantly enhances switching smoothness and prolongs the clutch’s service life. Full article

To ensure reliable lane change behavior in-wheel motor-driven electric vehicles (IWM-EVs) under steer-by-wire (SBW) failure, this paper presents an integrated lateral–longitudinal lane change control strategy based on differential steering. The control framework and relevant models are first established. An upper-layer model predictive control (MPC) controller is then designed to simultaneously achieve lateral path tracking and longitudinal speed regulation, outputting the desired front-wheel steering angle and acceleration. Finally, a model-free adaptive control (MFAC)-based lower-layer lateral controller transforms the desired steering angle into differential driving torques for the front wheels, while a feedforward–feedback lower-layer longitudinal controller (incorporating drive/brake switching and PI control) computes the required driving torque or braking pressure. Co-simulation in Matlab/Simulink R2022b and CarSim R2020 reveals that the MPC controller designed in this study outperforms the LQR-PID controller, reducing the maximum absolute values of lateral error, heading error, front-wheel steering angle, yaw rate and sideslip angle by 42.9%, 50.0%, 7.8%, 2.8% and 10.3%. The proposed hierarchical control strategy outperforms the compared hierarchical controller, reducing the maximum absolute values of the lateral displacement error, heading error and yaw rate by 17.9%, 6.7%, and 33.3%. These results verify that the strategy can improve trajectory tracking accuracy and achieve basic differential steering functionality in specific scenarios. Full article