Vehicle Control Research Articles

Flow Field Analysis and Development of a Prediction Model Based on Deep Learning

The velocity of ocean currents significantly affects the trajectory prediction of ocean drifters and the safe navigation of intelligent vessels. Currently, most ocean current predictions focus on time-based forecasts at specific fixed points. In this study, deep learning based on the flow field prediction model (CNNs–MHA–BiLSTMs) is proposed, which predicts the changes in ocean currents by learning from historical flow fields. Unlike conventional models that focus on single-point current velocity data, the CNNs–MHA–BiLSTMs model focuses on the ocean surface current information within a specific area. The CNNs–MHA–BiLSTMs model integrates multiple convolutional neural networks (CNNs) in parallel, multi-head attention (MHA), and bidirectional long short-term memory networks (BiLSTMs). The model demonstrated exceptional modelling capabilities in handling spatiotemporal features. The proposed model was validated by comparing its predictions with those predicted by the MIKE21 flow model of the ocean area within proximity to Dalian Port (which used a commercial numerical model), as well as those predicted by other deep learning algorithms. The results showed that the model offers significant advantages and efficiency in simulating and predicting ocean surface currents. Moreover, the accuracy of regional flow field prediction improved with an increase in the number of sampling points used for training. The proposed CNNs–MHA–BiLSTMs model can provide theoretical support for maritime search and rescue, the control or path planning of Unmanned Surface Vehicles (USVs), as well as protecting offshore structures in the future.

Carvedilol suppresses coronary artery spasm in A-kinase anchoring protein 150 knockout mouse

Abstract Backgrounds A-kinase anchoring proteins (AKAPs) act as scaffold to regulate activation of protein kinases and subsequent downstream signaling. AKAP 150 interacts with protein kinase A and protein kinase C, and has a critical role in regulating L-type calcium channel activity. We previously reported enhanced phospholipase C activity as an important mechanism of coronary spastic angina (CSA), known as Prinzmetal’s angina. Coronary artery spasm was induced by intravenous ergometrine injection in mouse model of vascular smooth muscle cell (VSMC)-specific enhanced phospholipase C activity concomitantly with enhanced intracellular Ca2+ concentration ([Ca2+]i). Then, we hypothesized that coronary artery spasm would be inhibited by AKAP 150 knockout, however, coronary artery spasm was unexpectedly induced in AKAP 150 knockout (AKAP-KO) mice without affecting [Ca2+]i, suggesting that AKAP-KO mouse is an unique model of CSA independent of [Ca2+]i. Previous report showed that AKAP-KO mice have a high Ca2+/calmodulin-dependent protein kinase (CaMK) II activity, and carvedilol, a non-selective beta-adrenergic receptor antagonist, inhibits CaMK II activity. Aim We tested the hypothesis that carvedilol could prevent coronary artery spasm via inhibiting CaMK II activity, which is a key molecule to regulate VSMC contraction, in AKAP-KO mice. Methods and Results We pretreated 14-18-weeks-old AKAP-KO mice with intraperitoneal injection of carvedilol (2.5mg/kg), propranolol (20mg/kg), or vehicle (control). Coronary artery spasm, evaluated by ST-segment elevation on lead II of body surface electrocardiogram, was induced by intravenous ergometrine injection (30mg/kg) in all controls (5/5, 100%) and all propranolol-treated mice (5/5, 100%), whereas it tended to be inhibited in carvedilol-treated mice (3/6, 50%) (p=0.08 vs control). In isolated VSMCs obtained from the aorta of AKAO-KO mice, the changes in [Ca2+]i from baseline in response to acetylcholine (ACh, 100μM) did not differ among the groups of pretreatment with carvedilol (10μM), propranolol (10μM), or vehicle, indicating that coronary artery spasm in AKAP-KO mice was induced by Ca2+-independent mechanism. In response to ACh, the phosphorylation of CaMK II was tended to be decreased (p=0.07), and the phosphorylation of myosin light chain was decreased (p<0.05) in VSMCs treated with carvedilol compared with control. Conclusions Carvedilol may inhibit coronary artery spasm probably via inhibiting CaMK II activity in AKAP-KO mice. Our results may provide a new paradigm into the pathogenesis of CSA as a model of enhanced Ca2+ sensitivity and important clinical implications for the mechanism of calcium channel antagonist-refractory coronary artery spasm.

Research on the role of multi-sensor system information fusion in improving hardware control accuracy of intelligent system

Abstract Multi-sensor management and control technology generally constructs a reasonable objective function to solve the optimal control command set to control a limited number of sensors to obtain higher quality measurement information, thus obtaining better target tracking performance. In the process of multi-sensor information fusion, there is not only the problem of information redundancy but also obvious time delay. A sensor fusion algorithm combined with global optimization algorithm is innovatively proposed. According to the key frames saved in the previous steps, feature points in local maps, sensor information, and loop information, a global optimization algorithm based on graph optimization model is constructed to optimize the position and pose of intelligent hardware system and the position of spatial feature points. Moreover, this work studies and experiments on multi-sensor fusion simultaneous localization and mapping (SLAM) comprehensively and systematically, and the experimental results show that the algorithm proposed in this work is superior to common open-source SLAM algorithm in positioning accuracy and mapping effect under special circumstances. Therefore, the method proposed in this work can be applied to intelligent driving of vehicles, vision-assisted movement of robots and intelligent control of unmanned aerial vehicles, thus effectively improving the hardware control accuracy of intelligent systems.

Innovative Feature Analysis of Electric Vehicle Technology, Charging Infrastructure, Power management, and Control Methods

Lowering the dependence on fossil fuels and reducing pollution from greenhouse gas (GHG) emissions is incredibly achievable through electric vehicle (EVs) technology. EV technology is an innovation that uses electricity, rather than fossil fuels, to power and refuel (recharge) vehicles. The adoption and development of EVs should lead to a decline in future demand for fossil fuels, which are finite in supply and exhaustible. Inherent challenges in EV technology, such as inadequate supply of critical minerals, power grid overload, battery technology constraints, extended charging durations, insufficient charging infrastructures, high initial costs, and limited driving range, must be addressed. The technology of charging infrastructures cannot be over-emphasized in EV technology. EV technology, charging infrastructures, vis-à-vis the impact of their integration into the grid is investigated. Effective control strategies and power management systems (PMSs) are required to optimize energy use to improve EVs' efficiency and lifetime. This research uses comprehensive analysis methods to assess various control strategies, PMSs, and their effects on EV integration into the grid.

Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Abstract This paper addresses the critical need to quantify vehicle trajectory uncertainty in autonomous driving under environmental variability. We focus on predicting the posterior distribution of vehicle trajectories over a fixed horizon, given an initial state and a sequence of actions. We propose and compare three approaches: a probabilistic seq2seq model based on stochastic variational Gaussian processes, sequential Monte Carlo simulation with a single-step Gaussian process model, and a hybrid model that leverages the strengths of both methods. Each approach incorporates a baseline vehicle kinematics model to enhance stability and convergence. We evaluate these methods using a dataset generated from the CARLA simulator, assessing both point error metrics and probabilistic prediction metrics. This research introduces novel approaches to quantifying vehicle trajectory uncertainty through various uncertainty quantification techniques, with the goal of improving the safety and reliability of autonomous vehicle control systems.

Combined Restraint Stress and Metal Exposure Paradigms in Rats: Unravelling Behavioural and Neurochemical Perturbations.

Accumulation of heavy metals (Mn and Ni) and prolonged exposure to stress are associated with adverse health outcomes. Various studies have shown the impacts of stress and metal exposures on brain function. However, no study has examined the effects of co-exposure to stress, Mn, and Ni on the brain. This study addresses this gap by evaluating oxidative and glial responses, apoptotic activity, as well as cognitive processes in a rat model. Adult Wistar rats were exposed to vehicle (control), restraint stress, 25mg/kg of manganese (Mn) or nickel (Ni), or combined restraint stress plus Mn or Ni. Following treatment, rats were subjected to several behavioural paradigms to assess cognitive function. Enzyme activity, as well as ATPase levels, were evaluated. Thereafter, an immunohistochemical procedure was utilised to evaluate neurochemical markers of glial function, myelination, oxidative stress, and apoptosis in the hippocampus, prefrontal cortex (PFC), and striatum. Results showed that stress and metal exposure increased oxidative stress markers and reduced antioxidant levels. Further, combined stress and metal exposure reduced various forms of learning and memory ability in rats. In addition, there were alterations in Iba1 activity and Nrf2 levels, reduced Olig2 and myelin basic protein (MBP) levels, and increased caspase-3 expression. These neurotoxic outcomes were mostly exacerbated by co-exposure to stress and metals. Overall, our findings establish that stress and metal exposures impaired cognitive performance, induced oxidative stress and apoptosis, and led to demyelination effects which were worsened by combined stress and metal exposure.

Tree structures may be the leadership structures employed by group motions exhibiting hierarchy

Collective behaviors leading to various fascinating movement patterns are believed to be the product of complex interplay among individuals. Previous studies have identified two types of leadership structures in pigeon flocks, i.e., hierarchical networks and reciprocal relationships. However, both of these leadership structures are predicated based on data analysis and lack substantial empirical evidence. Additionally, it is difficult to delineate a direct correspondence between leadership structures and trajectory data for pigeon flocks because birds cannot report their leadership structure. Herein, based on experiments involving volunteers, we found that tree structures may serve as the leadership structures employed by group motions exhibiting hierarchy. In the tree structure, each follower follows its only leader during collective motions, and the single top leader determines the direction of collective motions. By employing the tree structure, we introduced a model for describing collective motions. A method for obtaining the leadership structure through experimental trajectory data was proposed. This strategy was used to analyze flight trajectory data from a pigeon flock and elucidate the pigeons' leadership relationships. Our model can simulate the collective behavior of pigeon flocks, thus accurately replicating the findings of previous experimental studies. The results of this study provide insights regarding the leadership structure in pigeon flocks and have implications for artificial collective systems, e.g., autonomous formation control of multiple unmanned aerial vehicles or unmanned surface vehicles. Published by the American Physical Society 2024

Adaptive multimodal control of trans-media vehicle based on deep reinforcement learning

To solve the problem that the control system is prone to instability due to the sudden change of physical characteristics, strong interference, and nonlinear in the process of multimodal movement of trans-media vehicle, an adaptive control method combining the Deep Deterministic Policy Gradient (DDPG) and traditional Proportional-Integral-Derivative (PID) controller is proposed in this paper. In this approach, the upper-level DDPG controller continuously monitors the vehicle's state and environmental conditions, dynamically adjusting the PID parameters in real-time. The lower-level PID controller then utilizes these updated parameters to modulate the output thrust of the vehicle's motors, thereby achieving excellent control over the vehicle's entire movement. Firstly, according to the hydrodynamic analysis, the kinematics and dynamics mathematical model of the self-designed trans-media vehicle is constructed. This model includes the multi-stage motion modal process of aerial flight, underwater navigation, and cross-media motion, which is suitable for the simulation and verification of the control method. Then, an adaptive controller called RL-PID combining DDPG and PID is built, so that PID can adjust parameters in real-time according to the changes in the external environment. Finally, after theoretical stability proof, a comparison study is performed across three approaches, namely the novel RL-PID, Fuzzy PID, and PID. The experimental results illustrate the superiority of the proposed approach over the competing ones and the generalization of the proposed approach under different interference.

Impact of Senescent Cell-Derived Extracellular Vesicles on Innate Immune Cell Function.

Extracellular vesicles (EVs) are components of the senescence-associated secretory phenotype (SASP) that influence cellular functions via their cargo. Here, the interaction between EVs derived from senescent (SEVs) and non-senescent (N-SEVs) fibroblasts and the immune system is investigated. Via endocytosis, SEVs are phagocytosed by monocytes, neutrophils, and B cells. Studies with the monocytic THP-1 cell line find that pretreatment with SEVs results in a 32% (p < 0.0001) and 66% (p < 0.0001) increase in lipopolysaccharide (LPS)-induced tumor necrosis factor-alpha (TNF-α) production when compared to vehicle control or N-SEVs respectively. Interestingly, relative to vehicle control, THP-1 cells exposed to N-SEVs exhibit a 20% decrease in TNF-α secretion (p < 0.05). RNA sequencing reveals significant differences in gene expression in THP-1 cells treated with SEVs or N-SEVs, with vesicle-mediated transport and cell cycle regulation pathways featuring predominantly with N-SEV treatment, while pathways relating to SLITS/ROBO signaling, cell metabolism, and cell cycle regulation are enriched in THP-1 cells treated with SEVs. Proteomic analysis also reveals significant differences between SEV and N-SEV cargo. These results demonstrate that phagocytes and B cells uptake SEVs and drive monocytes toward a more proinflammatory phenotype upon LPS stimulation. SEVs may therefore contribute to the more proinflammatory immune response seen with aging.

Distributed finite-time sliding mode containment control for multi-unmanned surface vehicles with input and output quantization

To solve the containment control problem of multi-unmanned surface vehicles (USVs) with input and output quantization communication, we propose a distributed containment method using sliding mode control (SMC) technology. First, with only the quantized value of position information being available, a super-twisting algorithm (STA)-based observer is used to recover the unmeasured velocities of the follower USVs in a finite time. Second, based on the STA-based observer, a distributed SMC strategy is developed to guide the follower USVs closely into the convex hull formed by the leader USVs under quantized communication. Furthermore, the stability analysis is given to guarantee that sliding variables and containment position errors can converge to specified ranges related to the quantization parameters and the parameters of the STA-based observer. Finally, simulation results demonstrate the feasibility of the designed SMC approach.

Coordinated torque vectoring and direct yaw control based on Kalman filter control allocation for a four in-wheel electric vehicle

This paper presents a novel hierarchical control architecture designed for a four in-wheel electric vehicle (EV), integrating longitudinal and lateral control strategies to optimize performance and lateral stability. On one hand, a high-level longitudinal controller is synthesized to track a desired speed profile and generate a total torque. On the other hand, a direct yaw control (DYC) strategy is employed for steerability and stability control to generate a yaw moment. To manage the total torque as well as the yaw moment, a control allocation layer is employed. Subsequently, the formulation of the control allocation problem is presented, incorporating a gain-scheduling Daisy chaining Kalman filter (DCKF) control allocation to deal with actuator dynamics and fault tolerance. Results demonstrate enhanced performance in normal and emergency driving. Quick adaptability in case of emergency is achieved without overshooting or undershooting, with a smooth response.

Energy Management System and Control of Plug-in Hybrid Electric Vehicle Charging Stations in a Grid-Connected Microgrid

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system.

Efficient Nonlinear Model Predictive Path Tracking Control for Autonomous Vehicle: Investigating the Effects of Vehicle Dynamics Stiffness

Motion control is one of the three core modules of autonomous driving, and nonlinear model predictive control (NMPC) has recently attracted widespread attention in the field of motion control. Vehicle dynamics equations, as a widely used model, have a significant impact on the solution efficiency of NMPC due to their stiffness. This paper first theoretically analyzes the limitations on the discretized time step caused by the stiffness of the vehicle dynamics model equations when using existing common numerical methods to solve NMPC, thereby revealing the reasons for the low computational efficiency of NMPC. Then, an A-stable controller based on the finite element orthogonal collocation method is proposed, which greatly expands the stable domain range of the numerical solution process of NMPC, thus achieving the purpose of relaxing the discretized time step restrictions and improving the real-time performance of NMPC. Finally, through CarSim 8.0/Simulink 2021a co-simulation, it is verified that the vehicle dynamics model equations are with great stiffness when the vehicle speed is low, and the proposed controller can enhance the real-time performance of NMPC. As the vehicle speed increases, the stiffness of the vehicle dynamics model equation decreases. In addition to the superior capability in addressing the integration stability issues arising from the stiffness nature of the vehicle dynamics equations, the proposed NMPC controller also demonstrates higher accuracy across a broad range of vehicle speeds.

Microglia ameliorate delirium-like phenotypes in a murine model of acute ventilator-induced lung injury

BackgroundDelirium affects 50–85% of patients on mechanical ventilation and is associated with increased mortality, prolonged hospitalization, and a three-fold higher risk of dementia. Microglia, the resident immune cells of the brain, exhibit both neuroprotective and neurotoxic functions; however, their effects in mechanical ventilation-induced acute lung injury (VILI) are unknown. We hypothesize that in a model of short-term VILI, microglia play a neuroprotective role to ameliorate delirium-like phenotypes.MethodsMicroglia depletion (n = 18) was accomplished using an orally administered colony stimulating factor 1 receptor inhibitor, while controls received a vehicle diet (n = 18). We then compared extent of neuronal injury in the frontal cortex and hippocampus using cleaved caspase-3 (CC3) and multiple delirium-like behaviors in microglia depleted and non-microglia depleted male mice (C57BL/6 J aged 4–9 months) following VILI. Delirium-like behaviors were evaluated using the Open Field, Elevated Plus Maze, and Y-maze assays. We subsequently evaluated whether repopulation of microglia (n = 14 repopulation, 14 vehicle) restored the phenotypes.ResultsFrontal/hippocampal neuronal CC3 levels were significantly higher in microglia depleted VILI mice compared to vehicle-treated VILI controls (p < 0.01, p < 0.01, respectively). These structural changes were accompanied by worse delirium-like behaviors in microglia depleted VILI mice compared to vehicle controls. Specifically, microglia depleted VILI mice demonstrated: (1) significantly increased time in the periphery of the Open Field (p = 0.01), (2) significantly increased coefficient of variation (p = 0.02), (3) trend towards reduced time in the open arms of the Elevated Plus Maze (p = 0.09), and (4) significantly decreased spontaneous alternations on Y-maze (p < 0.01). There was a significant inverse correlation between frontal CC3 and percent spontaneous alternations (R2 = 0.51, p < 0.01). Microglia repopulation showed a near-complete return to vehicle levels of delirium like-behaviors.ConclusionsThis study demonstrates that microglia depletion exacerbates structural and functional delirium-like phenotypes after VILI, while subsequent repopulation of microglia restores these phenotypes. These findings suggest a neuroprotective role for microglia in ameliorating neuronal and functional delirium-like phenotypes and call for consideration of interventions that leverage endogenous microglia physiology to mitigate delirium.

Multi-Agent Reinforcement Learning for Smart Community Energy Management

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. LSD-MADDPG modifies the conventional MADDPG framework by limiting data sharing during centralized training to only discretized strategic information. During execution, it relies solely on local information, eliminating post-training data exchange. This approach addresses critical challenges commonly faced by EMS solutions serving dynamic, increasing-scale communities, such as communication delays, single-point failures, scalability, and nonstationary environments. By leveraging and sharing only strategic information among agents, LSD-MADDPG optimizes decision-making while enhancing training efficiency and safeguarding data privacy—a critical concern in the community EMS. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening the community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings by ensuring fair alignment between individual buildings’ energy management actions and community-wide goals, highlighting its potential for advancing future smart community energy management.

Fixed-time second-order sliding mode output feedback trajectory tracking control for an autonomous surface vehicle with model uncertainties and prescribed performance

This paper investigates the fixed-time trajectory tracking control problem of autonomous surface vehicle system with asymmetric performance constraints. The external disturbances, model uncertainties and the difficulty of velocity measurement are all taken into account in the controller design. Asymmetric performance constraints are firstly studied to improve the transient and steady-state performance of the tracking error system. Next, a fixed-time extended state observer is developed to estimate the unknown velocity and external disturbances. Both the fixed-time first-order and the second-order PID-type sliding surfaces are designed. Further, two continuous output feedback controllers are built to achieve high control accuracy and low energy consumption and to achieve fixed-time stability of the closed-loop system. Finally, abundant simulations for CyberShip II demonstrate the effectiveness of the proposed algorithms.

A novel power quality-improved high-step-up-gain Luo converter-powered BLDC motor drive with model reference adaptive controller for electric vehicles

A novel power quality-improved high-step-up-gain Luo converter-powered BLDC motor drive with model reference adaptive controller for electric vehicles

Assessment of Situational Complexities and Vehicle Control Difficulties for Profiling Driving Skills

Assessment of Situational Complexities and Vehicle Control Difficulties for Profiling Driving Skills

Long-Term Efficacy and Tolerability of an Emollient Containing Glycerol and Paraffin for Moderate-to-Severe Uremic Xerosis: A Randomized Phase3 Study.

There is an unmet need for effective topical therapies for patients with uremic xerosis and chronic kidney disease-associated pruritus (CKD-aP). The long-term efficacy and tolerability of an emollient containing glycerol 15% and paraffin 10% (V0034CR) was evaluated in a phase3 study. In this randomized, double-blind, two-parallel group, vehicle-controlled study, patients with moderate-to-severe uremic xerosis were randomized to once-daily application of V0034CR or vehicle control for 28days (periodI). This was followed by a treatment-free period of ≤ 21days (periodII), then all patients received open-label treatment with V0034CR for ≥ 84days (periodIII). Outcomes included treatment response at the end of periodI (El Gammal's xerosis severity score), instrumental measures of scaling (D-Squame technique), time to relapse during periodII, rate of recurrence during periodIII, pruritus severity over time, patient acceptability, and adverse events (AEs). The intent-to-treat population comprised 235 patients randomized to V0034CR (n = 118) or vehicle control (n = 117) during periodI. Treatment response at the end of periodI was achieved by 71 patients (60.2%) in the V0034CR group versus 48 (41.0%) with vehicle control (p = 0.0041). This coincided with greater reductions in the total surface area of squames (p = 0.001 vs vehicle control). Xerosis relapsed progressively without treatment in periodII; however, remission was durable under maintenance therapy in periodIII. Improvements in pruritus severity were comparable between V0034CR and vehicle control, suggesting that the antipruritic effect of V0034CR was mainly exerted by its oil-in-water emulsion base. V0034CR had high patient acceptability and was well tolerated; the most common treatment-related AEs were irritation or erythema (2.1%), exacerbated pruritus (1.3%), and vesicles at the application site (0.9%). These data support the use of V0034CR, with its hydrating and occlusive properties, for the long-term management of patients with moderate-to-severe uremic xerosis and CKD-aP. ClinicalTrials.gov identifier NCT01084148; EudraCT number 2006-002201-31.

Integrated Control of Four-Wheel Steering, Electronic Stability Control and Active Aerodynamic Control for Electric Vehicles

Integrated Control of Four-Wheel Steering, Electronic Stability Control and Active Aerodynamic Control for Electric Vehicles