Vehicle Control System Research Articles

Numerical investigation on cooling performance and control effect of a novel evaporation-driven cooling unit for Mars extravehicular space suit application

The special environment of Mars brings new challenges to the thermal control of spacecraft on Mars missions. The outstanding advantages of water evaporation cooling technology make it promising for use in the thermal control system of the Mars spacecraft. This paper proposes a novel concept of evaporation cooling hardware that integrates heat collection, heat transfer and heat rejection, namely evaporation-driven cooling unit. The evaporation-driven cooling unit has a simple structure, high evaporation efficiency and the potential to simplify the system topology. Furthermore, an expert hierarchical coordination control strategy applied to the Mars extravehicular space suit cooling loop with an evaporation-driven cooling unit as its heat sink is proposed. The dynamics model of this cooling loop is also constructed using the lumped parameter method. The numerical results have shown that the expert hierarchical coordination control strategy can achieve precise temperature control and reduce the water consumption of the system. The average water consumption rate during extravehicular activity is 0.6975 kg/h when the mean heat load is 572 W (including member metabolic heat and electronic waste heat). Compared to the open-loop case with the same input heat load, a typical extravehicular activity lasting 8 h will save about 11.5 g water if the expert hierarchical coordination control is applied. The proposed evaporation-driven cooling unit will provide more choices for heat rejection of extravehicular space suits. It is expected that the evaporation-driven cooling unit and expert hierarchical coordination control have potential applications in thermal control systems for other space vehicles in near-Earth orbit, on the surface of the Moon and Mars.

An improved seeds scheme in K‐means clustering algorithm for the UAVs control system application

AbstractClustering algorithm is the primary technology used in target clustering and group status analysis which are key features of the Unmanned Aerial Vehicles (UAVs) control system. Due to variable application environment, the stability of the algorithm in the UAVs control system needs to be considered. K‐means clustering is a widely used method in intelligent systems. However, K‐means algorithm is susceptible to the local optimum due to the influence of the initial centroid. For this problem, the predecessors have proposed various effective solutions. These algorithms perform better on real and large‐scale datasets, but they are unable to achieve optimum results with unbalanced datasets. Herein, a simpler and more effective algorithm for seed initialization is proposed, it has a better accuracy rate than the alternative algorithms.Moreover, after running tests multiple times with each algorithm independently, it has the highest stability and the lowest overall volatility. With unbalanced datasets, the proposed algorithm performs significantly better than several other algorithms and therefore can solve the problems that other algorithms have with unbalanced datasets.

A control algorithm to follow the limitations of an electric vehicle motor using the Tm4 Sumo drive as an example

A control algorithm to follow the limitations of an electric vehicle motor using the Tm4 Sumo drive as an example

Adaptive MPC-Based Lateral Path-Tracking Control for Automatic Vehicles

Adaptive MPC-Based Lateral Path-Tracking Control for Automatic Vehicles

Vehicle Sideslip Angle estimation under critical road conditions via nonlinear Kalman filter-based state-dependent Interacting Multiple Model approach

The knowledge of Vehicle Sideslip Angle (VSA) can play an essential role in active safety vehicle control systems. However, due to the high costs of sensing instruments, this information is difficult to be directly measured onboard of series production vehicles, restricting de facto its application in practice. It follows that there is a need for online VSA estimation methods only based on available measurements from low-cost sensors. From this perspective, this work proposes a strategy based on Interacting Multiple Model (IMM) filters, which does not require tyre–road friction coefficient knowledge. By integrating the available onboard sensor data, the IMM estimates relevant information in different driving conditions leveraging a 2-Degrees Of Freedom (DOF) single-track vehicle model embedding a Dugoff tyre representation. Two alternative IMM algorithms based on the Extended (EKF) and Unscented Kalman filter (UKF) are developed. Moreover, while usually the transition probabilities among models in classical IMMs are fixed and set on prior information and/or dedicated analysis, here these conservative hypotheses are relaxed introducing a state-dependent Markov transition matrix based on a novel model switching algorithm. The effectiveness of the new proposed methods is evaluated on extensive non-trivial simulation scenarios through a Monte Carlo analysis exploiting an accurate 15-DOF vehicle model via a purposely designed high-fidelity co-simulation platform embedding the dSPACE software Automotive Simulation Model (ASM). Results provide a meaningful comparative performance analysis between the IMMEKF and IMMUKF solutions, as well as with respect to traditional IMM based on constant probabilities transition matrix, blue in both the EKF and UKF configuration. Finally, the developed IMM-based estimation strategy is tested in two realistic driving scenarios to assess the VSA estimation accuracy in case of abrupt changes in road surface conditions.

ДИНАМИКА УПРАВЛЕНИЯ ДВИЖЕНИЕМ ТРАНСПОРТНОЙ МАШИНЫ НА ЗАТЯЖНЫХ СПУСКАХ

The article provides a rationale for the need to develop an additional braking control system for a transport vehicle to limit the movement speed under the action of rolling forces and to protect the engine from exceeding the permissible speed, “overshooting” the speed. Design options for four systems that dissipate kinetic energy are considered, and the effectiveness of their use is assessed: in periodically switched off clutches in the transmission; in a hydraulic retarder, containing a rotor and a stator, where kinetic energy turns into heat and is dissipated into the environment; in two complex hydrodynamic transmissions, the design of which integrates a torque converter and a hydraulic retarder. In the first version of the system, kinetic energy turns into heat when the transmission clutches are periodically turned on and off; in the second option, energy transformation is carried out in the retarder circulation circle containing the rotor and the stator; in the third embodiment, in an additional circuit of the circulation circle of the working fluid of a hydrodynamic transmission, containing a pump, turbine, reactor wheels and an additionally introduced fourth wheel, which can serve as the stator; in the fourth version, the pump wheel of the torque converter can perform the additional function of the hydraulic retarder rotor, and the hydrodynamic transmission housing can function as the stator.

Magnetorheological Damper Force Prediction using Particle Swarm Optimization and Long Short-Term Memory Model

Smart materials, like magnetorheological (MR) fluid, are gaining attention for their ability to rapidly change properties under magnetic influence, making them useful in vibration control systems for vehicles, medical devices, and civil engineering structures. Common parametric models, such as Bouc-Wen and Bingham, are traditionally employed to model MR damper dynamics behavior. However, the manual tuning of numerous parameters in these models increases complexity and hinders the identification of inverse models, potentially leading to unpredictable optimum target forces. In response to these challenges, this study suggested a non-parametric approach using Long Short-Term Memory (LSTM) models to predict the optimum target force of MR dampers. Unlike parametric models, LSTM models capture dynamic behavior without the need for extensive manual tuning. To optimize the LSTM model, Particle Swarm Optimization (PSO) is employed to fine-tune hyperparameter values, ensuring robust performance. The proposed non-parametric method, specifically the PSO-LSTM model, demonstrates faster processing times compared to traditional parametric approaches. The proposed model produced an accurate damping force prediction with a root mean square error of less than 5%, This novel approach simplifies the modeling process and offers an efficient and precise alternative to traditional parametric methods.

Full-scale simulator to test a control system of an engine-propeller powerplant of a convertible aerial vehicle

The article presents the results of work involved with developing a full-scale simulator for research into determining the structure and parameters of the control system for unmanned aerial copter-type vehicles with a powerplant comprising electric motors with fixed-pitch propellers. The features of the engineering implementation of the simulator, taking into account the prospects for its development in terms of greater maneuverability (pitch, roll and yaw), are presented. The implemented principle of the Simulink integration – a model of the control object, a controller based on the Arduino platform, a gyroscope-accelerometer to organize feedbacks for the purpose of forming algorithms of the automatic and positional (manual) pitch angle control, manual motor revs control is described. The analysis of the full-scale simulation results in terms of the quality of transients and power costs for various settings of the PID-regulator, which provides generating a signal of electric motor revolutions, is presented. It is concluded that it is feasible to create and use an experimental base to justify the use of adaptive control algorithms for unmanned aerial copter-type vehicles with elements of artificial intelligence to ensure the required flying characteristics in a wide range of properties of control objects.

A Hierarchical Prediction Method for Pedestrian Head Injury in Intelligent Vehicle with Combined Active and Passive Safety System.

With the development of intelligent vehicle technology, the probability of road traffic accidents occurring has been effectively reduced to a certain extent. However, there is still insufficient research on head injuries in human vehicle collisions, making it impossible to effectively predict pedestrian head injuries in accidents. To study the efficacy of a combined active and passive safety system on pedestrian head protection through the combined effect of the exterior airbag and the braking control systems of an intelligent vehicle, a "vehicle-pedestrian" interaction system is constructed in this study and is verified by real collision cases. On this basis, a combined active and passive system database is developed to analyze the cross-influence of the engine hood airbag and the vehicle braking curve parameters on pedestrian HIC (head injury criterion). Meanwhile, a hierarchy design strategy for a combined active and passive system is proposed, and a rapid prediction of HIC is achieved via the establishment of a fitting equation for each grading. The results show that the exterior airbag can effectively protect the pedestrian's head, prevent the collision between the pedestrian's head and the vehicle front structure, and reduce the HIC. The braking parameter H2 is significantly correlated with head injury, and when H2 is less than 1.8, the HIC value is less than 1000 in nearly 90% of cases. The hierarchy design strategy and HIC prediction method of the combined active and passive system proposed in this paper can provide a theoretical basis for rapid selection and parameter design.

Analysis of Performance Parameters of Engines with Spark Ignition with Variable Regulations of the Fuel-Injection System, Powered by E100 Fuel

Emission limits imposed on gas-powered vehicles, which have gradually been introduced by the European Commission, as well as the exhaustibility of the planet’s natural resources involve the need to search for alternative solutions and restore renewable fuel markets. In terms of ecology, using biofuels for powering spark-ignition engines has a positive impact on the natural environment. This kind of fuel, however, has a negative effect on the drive unit performance parameters, such as power and torque. This study presents test results of performance parameters of a spark-ignition engine fueled by E100 (pure ethanol). Parameters that have been analyzed are power, torque, exhaust emission components (oxygen, carbon dioxide, hydrocarbons), solid particles included in exhaust gases and emission of noise generated by the drive unit. The vehicle control system software adjustments have also been tested. Twelve regulations have been tested to evaluate performance parameters of a spark-ignition engine powered by E100 fuel. An analysis of the test results has confirmed the advisability of the introduction of control system software regulation. The aim of the work was to develop a model that will help select the appropriate software adjustment for an engine powered by E100 fuel. Due to the introduced emission restrictions, there is an important issue that concerns users of combustion vehicles. The research presented in the manuscript concerns a joint analysis of the engine’s fueling with alternative fuel and the introduction of changes to its software in order to reduce emissions.

A 4-Phase Combined Adhesion Threshold Algorithm for Wheel Slide Protection Systems in Rail Vehicles

The wheel slide protection control system for rail vehicles plays a crucial role in ensuring a consistent braking performance in all operating environments, making it a vital factor in the safety and efficiency of rail transportation. In this paper, a hybrid approach to wheel slide protection control is presented, which combines the rule-based control strategy and the model-based control methods using adhesion force estimation. Model-based control usually relies on mathematical models to characterize the vehicle dynamics, requiring online estimators to be designed or extra sensors to be added for practical application. Rule-based control operates on predefined rules and thresholds and the available data from vehicles in service. A comparative test was conducted between the traditional rule-based control strategy and the proposed combined control strategy using a semi-physical simulation test bench. The performance differences of the control strategies were analyzed from two perspectives: adhesion utilization and air consumption. It was observed that among the traditional 2-phase, 3-phase, 4-phase and the optimized 4-phase combined control method, the combined control strategy has the best adhesion utilization and the traditional 4-phase control strategy has the least air consumption.

Design of lateral dynamic control objectives for multi-wheeled distributed drive electric vehicles

The lateral dynamic control system for electric vehicles aims to improve vehicle performance, making it critical to propose reasonable lateral dynamic control objectives. Therefore, in this study, a novel lateral dynamic control objectives is proposed for multi-wheeled distributed drive electric vehicles. Initially, utilizing the classical two-degrees-of-freedom (2DOF) vehicle model, two initial yaw rate references were established for handling characteristics of manoeuvrability and stability, respectively. The yaw rate references consider the vehicle velocity, steering wheel angle and road friction coefficient limitations. Considering the vital role of the sideslip angle in ensuring vehicle stability, a sophisticated weighting factor was developed based on a detailed analysis of the vehicle dynamics within the sideslip angle-sideslip angle rate phase plane. The weighting factor was determined based on the position of the vehicle state in the phase plane. This factor was adeptly applied to harmonise our dual control objectives, culminating in an integrated control aim that encapsulates the individual benefits of each. Finally, the lateral dynamic control objective was realised using a direct yaw moment control (DYC) system. The effectiveness of the proposed control objective was validated through hardware-in-the-loop (HIL) simulations.

PID controller design with a new method based on proportional gain for cruise control system

In this study, a PID controller is proposed for cruise control systems in vehicles. The cruise control system here is linearized around the equilibrium point. A new simple design method is provided for tuning the parameters of the PID controller. In this method, PID controller is designed based on tuning the optimal proportional gain from the system according to the settling time ts, overshoot Mp. Determining the other parameters of the PID controller by adjusting the optimum ratio gain (kp) in a stable loop that minimizes the settling time and the error rate of the overshoot constitutes the basis of the method. The performance of the controller designed with the method without complex mathematical formulation is compared with the PID performance designed with state space, classical, fuzzy logic, genetic algorithm methods. As a result of numerical simulations, it has been observed that the performance of the PID controller designed with the proposed method is more successful than the other controllers mentioned.

Simultaneous attitude fault detection and control of the six-rotor UAV based on event trigger mechanism

This paper analyzes the problem of simultaneous fault detection and control of the six-rotor unmanned aerial vehicle control system, considering external disturbance, measurement disturbance, and actuator fault. The integral event trigger mechanism is introduced in the control side and sensor side of the system. Based on Lyapunov stability and H∞ control theory, the sufficient conditions to make the fault system asymptotically stable and have certain performance indexes are given by means of linear matrix inequalities; at the same time, the design criteria of event trigger parameters are also given. The linear matrix inequality is decoupled, and the calculation method of gain matrix of simultaneous fault detection and control module is given. The effectiveness of the proposed method is verified by simulation experiments.

Improvement of Traction Force Estimation in Cornering through Neural Network

<div>Accurate estimation of traction force is essential for the development of advanced control systems, particularly in the domain of autonomous driving. This study presents an innovative approach to enhance the estimation of tire–road interaction forces under combined slip conditions, employing a combination of empirical models and neural networks. Initially, the well-known Pacejka formula, or magic formula, was adopted to estimate tire–road interaction forces under pure longitudinal slip conditions. However, it was observed that this formula yielded unsatisfactory results under non-pure slip conditions, such as during curves. To address this challenge, a neural network architecture was developed to predict the estimation error associated with the Pacejka formula. Two distinct neural networks were developed. The first neural network employed, as inputs, both longitudinal slip ratios of the driving wheels and the slip angles of the driving wheels. The second network utilized longitudinal slip ratios of the driving wheels and longitudinal and lateral accelerations of the vehicle as inputs. The training of the neural networks was performed using data from straight-line accelerations, circuit maneuvers, and a sinus steering maneuver. Both neural networks were designed as multi-output networks capable of simultaneously estimating longitudinal force errors for both driving wheels. The estimator was tested by making two laps on the Hockenheim circuit in the opposite direction. The initial root mean square error (RMSE) was substantially reduced using corrective neural networks. These findings affirm the effectiveness of the neural network-based approach in improving traction force estimation under combined slip conditions, overcoming the limitations of the Pacejka formula in cases of non-pure slip, thereby paving new avenues for the implementation of more advanced and secure vehicle control systems.</div>

Дослідження шляхів підвищення оперативності виконання діагностичних операцій електронних систем автотракторної техніки

The purpose of this study is to find ways to reduce production time spent on diagnostic operations during maintenance or repair of electronic control systems (ECS) in automotive vehicles. Existing methods of diagnosing engine control systems in vehicles are sufficiently effective, but in most cases, they require highly skilled personnel, the use of additional complex and expensive equipment, prolonged service cycles, which in turn increases the complexity of maintenance, and also does not provide for prior prediction of faults and system failures. Analysis of scientific publications has shown that to reduce the time for maintenance and repair of electronic systems, it is necessary to predict the residual life of ECS components in vehicles during maintenance, to further reduce the labor intensity of diagnostics and current repairs. In other words, diagnostics performed during maintenance or repair should include predicting the residual life of electronic system components. For the most critical ECS components in vehicles, it is proposed to apply a strategy of replacing them upon reaching a certain mileage, rather than upon failure. This organization method will reduce vehicle downtime in repair and reduce the time to troubleshoot ECS component failures. A method has been proposed for identifying design elements that limit the reliability of the ECS, taking into account the degree of influence of their failures on the engine's technical and economic characteristics and the cost of restoring its performance. The availability of such information is the basis of the ECS maintainability support system. An algorithm has been developed to search for hidden faults in the components of ECS subsystems, which includes predicting the failure of ECS components. This will help reduce the labor intensity of diagnostic operations during maintenance and repair.

Structural integrity of 15-5PH stainless steel flow formed pressure vessel for launch vehicle applications

15-5 PH stainless steel is a martensitic precipitation-hardenable stainless steel (0.07C-15Cr-5Ni-3.5Cu-Fe). Presently, high strength low alloy steel, AFNOR 15CDV6 is used for the fabrication of Thrust Vector Control system of satellite launch vehicle. These pressure vessels are used for storing strontium perchlorate, intended for attitude (pitch and yaw) control of the launch vehicle during first stage. The tanks are fabricated through welding of rolled plates, machined rings, and end domes from forgings. AFNOR 15CDV6 steel is prone for corrosion and requires alumina barrier coating to protect from strontium perchlorate. Therefore, as a superior alternative, 15-5PH steel with improved corrosion resistance, simple heat treatment avoiding distortion was selected for the fabrication of pressure vessel. In order to improve the structural integrity of the pressure vessel, flow forming technology was adopted for realization of cylindrical portion of the tank, which eliminated the long seam weld joint and reduced the number of circumferential weld joints. As a part of the developmental program, the tank was designed, shells were flow formed and welded with end domes and were directly aged. The fabricated tank was subjected to a series of pressure tests followed by final burst test for estimating structural margins and ensuring quality requirements.This paper describes the design of the pressure vessel, process followed for manufacture, qualification program adopted and detailed metallurgical analysis carried out on the burst tested hardware. The 15-5PH pressure vessel has successfully withstood the proof pressures and test results are better than the predicted analysis results, indicating structural integrity of the pressure vessel for flight.

Intuitive and Privacy-Preserving Traffic Light Control System for Autonomous Vehicles

Intuitive and Privacy-Preserving Traffic Light Control System for Autonomous Vehicles

Real-time Adaptive Control of Electric Vehicle Drives using Artificial Neural Networks

This study examines the use of artificial neural networks (ANNs) in real-time adaptive control for electric vehicle (EV) propulsion systems, with the goal of enhancing performance and efficiency. The neural network-based control system is developed and validated using experimental data that includes vehicle speed, battery temperature, battery voltage, and motor temperature. The neural network demonstrates precise control output predictions by effectively adapting to dynamic changes in input parameters, exhibiting a remarkable level of responsiveness to diverse operating settings. The analysis of the experimental findings reveals a strong correlation between the expected and actual control values, confirming the system's dependability and effectiveness in managing torque and voltage instructions for the electric vehicle (EV). The performance indicators, such as mean squared error (MSE), R-squared, mean absolute error (MAE), and root mean squared error (RMSE), demonstrate a small difference between the anticipated and actual values, indicating that the system has a high level of accuracy and predictive capacity. Furthermore, the system displays remarkable responsiveness to changes in velocity, battery temperature, and voltage, showcasing its capacity to adjust to different driving situations while still staying within acceptable levels of fluctuation. This research highlights the capacity of artificial neural networks (ANNs) to facilitate accurate and flexible control systems for electric vehicles (EVs), representing a substantial advancement in improving the performance, efficiency, and adaptability of electric vehicle propulsion in sustainable transportation systems. The neural network-based control system has been proven to be accurate, responsive, and reliable. This highlights its potential to revolutionize future electric vehicle (EV) technologies and contribute to advancements in real-time adaptive control strategies for environmentally friendly transportation systems.

Robust Lane Keeping Control for Tractor With Multi-Unit Trailer Under Parametric Uncertainty

In this paper, a lane-keeping system is proposed for the tractor with two articulated trailers. For Tractor with Multi-Unit Trailer (T-MUT) vehicles, the motions of each unit of the vehicle are quite different from those of ordinary single-unit vehicles. Based on the dynamic model of the T-MUT vehicle, the lane-keeping control system of a T-MUT vehicle is designed with Model Predictive Control (MPC). In order to consider the uncertainty of the weight change at each trailer, a model uncertainty analysis is performed through the transfer functions for each hitch angle. For lane-keeping performance, the geometry constraints on each unit of the T-MUT vehicle are described to prevent the vehicle from lane invasion. Also, by applying the constraint tightening method to the output of the controller, a robust MPC is designed against model uncertainty, such as weight changes at each trailer. The performance of the proposed controller is validated in simulations with various curvature scenarios.