Vehicle Control System Research Articles

Design of Automotive Collision Avoidance Lower Position Controller Based on GA Optimization

Abstract To improve the control stability of the lower controller in the vehicle collision avoidance system, and solve the problem of traditional fuzzy PID not providing good dynamic performance under large parameter changes, a lower controller for vehicle collision avoidance based on genetic algorithm optimized fuzzy proportional-integral-derivative (PID) control was studied. The vehicle collision avoidance system collects real-time motion information of the preceding vehicle through millimeter-wave radar and calculates the safety distance. The safety distance model and upper controller of the system calculate the required acceleration of the vehicle. Afterwards, the expected acceleration of the vehicle is controlled by the lower controller of fuzzy PID. At the same time, the improved GA is used to adjust the PID parameters, solving the problem of poor robustness of the lower controller. A simulation platform was built to simulate the longitudinal vehicle collision avoidance control system jointly. The simulation results show that the lower controller designed based on this control algorithm has better stability and meets the collision avoidance requirements, providing an important basis for the development of vehicle control systems.

State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF

The prerequisite for intelligent vehicles to achieve autonomous driving and active safety functions is acquiring accurate vehicle state parameters. Traditional Kalman filters often underperform in non-Gaussian noise environments due to their reliance on Gaussian assumptions. This paper presents the IMM-MCCKF filter, which integrates the interacting multiple model theory (IMM) and the maximum correntropy cubature Kalman filter method (MCCKF), for estimating the state parameters of intelligent vehicles. The IMM-MCCKF successfully suppresses non-Gaussian noise by optimizing a nonlinear cost function using the maximum correntropy criteria, allowing it to capture and analyze signal data outliers accurately. The filter designs various state and measurement noise submodels to adapt to the environment dynamically, thus reducing the impact of unknown noise statistical properties. Accurately measuring the velocity of a vehicle and the angle at which its center of mass drifts sideways is of utmost importance for its ability to maneuver, maintain stability, and ensure safety. These parameters are critical for implementing advanced control systems in intelligent vehicles. The study begins by constructing a nonlinear Dugoff tire model and a three-degrees-of-freedom (3DOF) vehicle model. Subsequently, utilizing low-cost vehicle sensor signals, joint simulations are conducted on the CarSim-Simulink platform to estimate vehicle state parameters. The results demonstrate that in terms of estimation accuracy and robustness in non-Gaussian noise scenarios, the proposed IMM-MCCKF filter consistently outperforms the MCCKF and CKF algorithms.

INVESTIGATION OF THE EFFECTIVENESS OF THE ALGORITHM FOR CONTROLLING THE ACCELERATION AND DECELERATION OF A WHEELED VEHICLE BY MEANS OF A STROKE PEDAL

Justification. Since one of the significant costs of operating vehicles is fuel costs, therefore it is very promising to use machines with electric drives, such as battery electric ones, which reduce these costs. The main key property for them is the amount of autonomous mileage on a single charge. To maximize this value, developers are trying to use more advanced, energy-intensive energy sources and reduce energy costs when converting it on the way from the source to the driving wheels. In this circuit, the traction electric drive is the main source of energy loss. Therefore, it is very important not only to use more advanced electric drives, but also to improve control algorithms. To do this, it is necessary to form the setpoints of the requested engine torque using only the stroke pedal, taking into account the speed of movement, other conditions, and vehicle characteristics. The implementation of such a law allows the driver to reduce energy consumption by controlling the car, allowing it to move by inertia (coasting) and maximize the use of regenerative braking with minimal use of the service braking system.The purpose of the work. Investigation of the functioning and efficiency of the algorithm for determining the torque settings of a traction electric drive using only the stroke pedal, using mathematical modeling methods.Materials and methods. The study was performed in the Matlab Simulink software package.Results. The article presents the theoretical foundations of constructing an algorithm for determining the torque setpoint of a traction electric drive, the results of a virtual efficiency study.Conclusion. The practical value of the study lies in the possibility of using the proposed law for determining the setpoint of the traction and regenerative torque of the traction electric drive, the run-out mode for the development of a traction drive control system for transport vehicles in order to increase their energy efficiency.

Real-time estimation of vehicle inertia parameters based on Kalman-bucy filter

Vehicle inertial parameters such as mass and moments of inertia are required for most vehicle dynamic control systems. Due to the wide range variation of these parameters during vehicle operation, accurate estimation of their values in real-time plays an important role in improving the efficiency of vehicle control systems. In this article, the vehicle sprung mass and moments of inertia are estimated in real-time based on a Kalman-Bucy filter algorithm designed for a spatial vibration model of a two-axle truck. This proposed method requires measuring only the vertical, roll, and pitch velocity of the sprung mass and, therefore can reduce the sensor cost significantly. The simulation results for a random roughness road profile according to ISO 8608 class C with step variations in sprung mass and moments of inertia showed that the designed estimator rejected the process and measurement noises and tracked the real vehicle parameters effectively with acceptable errors

Hunting stability control of high-speed bogie based on active yaw damper

Hunting stability is an inherent property of railway vehicles that determines the operational speed. This paper establishes a half-vehicle model with nonlinear wheel/rail equivalent conicity and interactive forces. Additionally, the dynamic performances of vehicle under various levels of wheel wear with passive suspension are compared and analyzed. Importantly, the investigation delves into the hunting stability of the vehicle system, employing both linear and nonlinear control approaches. The results demonstrate a notable reduction in critical speed during the end-worn period with a passive suspension. However, this reduction can be substantially countered through the application of active control, resulting in a significant speed increase. The implementation of stiffness control raises the frequency of limit cycles, whereas damping control serves to diminish it. Notably, an appropriate linear cubic stiffness control effectively mitigates the amplitudes of limit cycles during instances of instability. Moreover, the control strategy derived from the simplified model is extended to enhance the stability of the entire vehicle system. The research findings hold the potential to offer a promising strategy for the active control of high-speed vehicles, particularly during periods of wheel wear.

Solar Lights for Highways with Zone-Wise Speed Control System for Vehicle

The challenge of providing adequate lighting along highways, particularly at critical points such as sharp turns and speed breakers, poses significant safety concerns. Traditional energy sources often fail to meet the demands of continuous lighting, especially in remote areas. To tackle this challenge, we present an automatic lighting system aimed at selectively activating lights during nighttime while also reducing vehicle speed near or at danger zones. Entire system is designed as automatic, human involvement is not required; lights are controlled automatically by sensing the vehicles. Whenever the vehicle enters into the danger zone, lights will be energized and they remain in ON state until the vehicle leaves that area. The system will be activated automatically during dark. In this method precious energy acquired from the solar panel can be saved. During the day time the battery will be charged and this stored energy is utilized in the nights[10]. It is a novel approach to vehicle navigation and safety implementation, and also aimed at automatically sensing the areas/zones like Schools, Hospital Accident, etc and automatically reducing the speed of the vehicle for safety purposes.

Hand Gesture Controlled Smart Car Using Image Recognition

A hand gesture-controlled smart automobile is the newest initiative in the field of human-computer interaction, and it represents the evolution toward more natural and in- tuitive user interfaces. This paper describes how OpenCV and Google’s MediaPipe are intricately coordinated to produce a control strategy that is both agile and responsive. The deployed technology translates dynamic vehicle motion commands from complex human hand gestures using advanced image recognition algorithms. This is the pinnacle of interactive technology meeting real-world locomotion needs: state-of-the-art computer vision and machine learning combined. The suggested system not only demonstrates the viability of non-contact user input for complex tasks like driving, but it also sets the way for future research in the field of autonomous vehicle guidance and control systems. This study highlights how gesture-based interfaces have the power to completely change the way people interact with cars, paving the way for more flexible and human-centered navigation systems.

Adaptive Dynamic Formation Control of Robotic Vehicle Systems Based on Rigid Graph Theory

Adaptive Dynamic Formation Control of Robotic Vehicle Systems Based on Rigid Graph Theory

A Robust Model Order Reduction Scheme for Lithium-Ion Batteries in Control-Oriented Vehicle Models

The role of batteries in electrification of vehicles is eminent; thus, a dynamic model that represents the physics-based phenomena of the battery system at a minimum computational cost is essential in the model-based design of electrified vehicle control systems. Furthermore, robustness of the reduced-order battery model when maintaining the dominant physics-based phenomena governing the dynamic behavior of the battery system is crucial. Characterization of the power signal applied to the lithium-ion battery in the energy management controller of a plug-in hybrid electric vehicle shows that there is a dominant frequency range in the input signal to the battery. This key feature can be considered as a basis to construct a reduced-order model in which the training input is different from the original power signal. The original idea in this paper is to generate the training input by applying a low-pass filter to the white-noise random signal to maintain the same dominant frequency range observed in the original power signal. Response of the reduced-order model, constructed using the proper orthogonal decomposition, compared to the high-fidelity battery model shows promising results; a maximum relative error of 1% was obtained for the battery state of charge while simulation time was reduced by 42.9%.

Lateral Driver‐Automation Driver Authority Decision Considering Safety of the Intended Functionality

AbstractThe driver authority decision for driver‐initiated takeover is closely related to vehicle driving safety. In this paper, a lateral driver‐automation driver authority decision method considering the safety of the intended functionality is proposed to improve vehicle driving safety. Based on systems‐theoretic processes analysis, functional safety analysis of driver initiative takeover is carried out to clarify the safety of the intended functionality issues caused by the unreasonable decision. Then, a method for defining the safe driving area is developed to assess the safety level of the driving area around the ego vehicle. Taking the driver‐vehicle state parameters and vehicle–vehicle state parameters as input, a deep neural network model is constructed to determine the intention of the driver to take over the vehicle. The lateral driver‐automation driver authority decision method is designed to make the driver‐machine take‐over decisions, which considers the safety of the intended functionality. The effectiveness of the proposed method is evaluated via numerical simulation and hardware‐in‐the‐loop experiments. The results show that the designed method not only improves the control ability of the driver to the maximum extent but also integrates the perception ability of the driver into the vehicle control system to improve further vehicle driving safety.

Theoretical and experimental studies of gas preparation system for pressurization and reactive control system of launch vehicle

A new gas preparation system (GasPS-RCS) is proposed to solve two tasks: (A) to heat helium gas for tank pressurization; (B) to prepare gas for the Launch Vehicle (LV) Reactive Control System (RCS) at the LV orientation and stabilization sections of the LV on passive parts of the flight trajectory, to provide conditions for launching the Liquid Rocket Engine (LRE). The system includes a gas generator based on hydrogen peroxide, a separator for water separation, heat exchangers independent of the LRE, and gas-jet nozzles. The proposed new system allowed to reduce the length of pressurizing gas lines and reduce the increased helium gas consumption during the heat exchanger warm-up interval of the LRE during its launch. A special advantage of the proposed system is the possibility of ground testing of heat exchangers without an operating LRE. A mathematical model based on the first law of thermodynamics was used to perform a comparative analysis of GasPS-RCS with traditional pressurization and RCS systems. To validate the mathematical model, the experimental studies of helium pressurizing of a liquid nitrogen tank were conducted. The results show that the deviation of experimental and calculated values for pressure is 1.1% and for temperature up to 2%. According to the results of comparative analysis, the GasPS-RCS is 259 kg lighter for the first stage and 80 kg lighter for the second stage of the LV.

VOICE CONTROL CAR USING MACHINE LEARNING

The "Voice-Controlled Car Using Machine Learning" project aims to create an innovative and user- friendly vehicle control system by integrating machine learning technology. This system provides a hands-free driving experience, making it safer and more convenient for drivers. The project involves developing a smart car prototype equipped with various sensors and microcontrollers. Machine learning algorithms are implemented to recognize voice commands and translate them into specific actions for the vehicle, such as accelerating, braking, turning, and even parking. The core components of the system include a speech recognition module, a control interface, and a real-time data processing unit. The project not only demonstrates the potential of machine learning in the automotive industry but also promotes a more accessible and user-centric approach to vehicular control. Keywords: “Speech Recognition”, command interpretation”, audio processing”, Wake word Detection”.

Monitoring the State of the Operator of the Ergatic System in UAV Control Tasks

An important aspect of the application of unmanned systems is the reliability and safety of controlling these devices. An innovative approach has been proposed to improve the efficiency of the pilot operator and the reliability of the automated control system. It includes the development of an algorithm for determining the pilot’s condition based on heart rate analysis. This algorithm helps to assess the condition of the pilot and his ability to control the drone. Another important element of the proposed approach is the algorithm for selecting the control mode of the automated control system for unmanned aerial vehicles, which takes into account information about the functional state of the pilot operator. This algorithm allows the system to automatically switch between different operating modes depending on the condition of the pilot, ensuring optimal control and minimizing the risks of human error. An integrated approach to improving the reliability of the unmanned aerial vehicle control system allows not only improving the work of the pilot operator, but also ensuring the safer and more efficient operation of automated systems.

When Do Users Prefer Voice Control Systems in Vehicles? A Survey of Chinese Drivers

Voice control systems (VCSs) are becoming increasingly common in modern vehicles, but concerns are still expressed by drivers about adopting them in smart cockpits. Previous research on privacy and recognition accuracy may not fully address users’ needs with regard to VCSs. In addition, studies on the safety impacts of different interaction modalities may not reveal drivers’ preference for interaction modalities, given that driver choice may not be solely based on interaction safety. Thus, to better understand and optimize VCSs design, an online survey was conducted in China to investigate drivers’ choices between VCSs and manual interaction. In total, we analyzed 168 drivers’ preferred interaction modality (i.e., either VCSs or manual interaction) in different scenarios defined by passenger presence (i.e., present versus absent), traffic complexity (i.e., low versus high traffic density), roadway type (i.e., urban road versus highway), and task characteristics (i.e., six in-vehicle interaction tasks). Compared with manual interactions, drivers preferred VCSs when driving alone, driving on highways, and driving in complex traffic. Further, drivers preferred VCSs when in-vehicle tasks were more distracting and time-demanding (e.g., compiling a text message). The preference for interaction modalities could also be affected by the characteristics of drivers: those with lower driving frequencies and higher VCSs familiarity and those who perceived higher VCSs usability tended to choose VCSs as an interaction modality for in-vehicle interaction tasks. These findings offer insights for adaptive interface design and future optimization of VCSs.

Advanced digital twin-enabled fault diagnosis framework for unmanned vehicle systems

The control system of unmanned vehicles must demonstrate strong capability to promptly diagnose and address system faults. Such a capability can improve transportation efficiency, ensure the smooth execution of production tasks, and to a certain extent, mitigate the risk of human casualties. To ensure the upkeep of unmanned vehicles and address the diagnostic requirements of control systems, this study integrates traditional wheeled vehicle control systems with digital twin (DT) technology to establish a framework for control system fault diagnosis and maintenance, with the primary objective of fulfilling the fault diagnosis task. By this framework, a method for detecting faults in unmanned vehicle control systems based on DT technology has been developed. This method involves the design of a data-driven model using multiple sensors and the application of a DT-improved particle filter fault diagnosis algorithm, utilizing a multi-domain model approach. A case study of the proposed method and simulation results are presented to illustrate its feasibility.

Research on an Intelligent Vehicle Trajectory Tracking Method Based on Optimal Control Theory

This study aims to explore an intelligent vehicle trajectory tracking control method based on optimal control theory. Considering the limitations of existing control strategies in dealing with signal delays and communication lags, a control strategy combining an anthropomorphic forward-looking reference path and longitudinal velocity closure is proposed to improve the accuracy and stability of intelligent vehicle trajectory tracking. Firstly, according to the vehicle dynamic error tracking model, a linear quadratic regulator (LQR) transverse controller is designed based on the optimal control principle, and a feedforward control strategy is added to reduce the system steady-state error. Secondly, an anthropomorphic look-ahead prediction model is established to mimic human driving behavior to compensate for the signal lag. The double proportional–integral–derivative (DPID) control algorithm is used to track the longitudinal speed reference value. Finally, a joint simulation is conducted based on MatLab/Simulink2021b and CarSim2019.0 software, and the effectiveness of the control strategy proposed in this paper is verified by constructing a semi-physical experimental platform and carrying out a hardware-in-the-loop test. The simulation and test results show that the control strategy can significantly improve the accuracy and stability of vehicle path tracking, which provides a new idea for future intelligent vehicle control system design.

Neural network-based robust adaptive super-twisting sliding mode fault-tolerant control for a class of tilt tri-rotor UAVs with unmodeled dynamics

Abstract Aiming at alleviating the adverse influence of coupling unmodeled dynamics, actuator faults and external disturbances in the attitude tracking control system of tilt tri-rotor unmanned aerial vehicle (UAVs), a neural network (NN)-based robust adaptive super-twisting sliding mode fault-tolerant control scheme is designed in this paper. Firstly, in order to suppress the unmodeled dynamics coupled with the system states, a dynamic auxiliary signal, exponentially input-to-state practically stability and some special mathematical tools are used. Secondly, benefiting from adaptive control and super-twisting sliding mode control (STSMC), the influence of the unexpected chattering phenomenon of sliding mode control (SMC) and the unknown system parameters can be handled well. Moreover, NNs are employed to estimate and compensate some unknown nonlinear terms decomposed from the system model. Based on a decomposed quadratic Lyapunov function, both the bounded convergence of all signals of the closed-loop system and the stability of the system are proved. Numerical simulations are conducted to demonstrate the effectiveness of the proposed control method for the tilt tri-rotor UAVs.

Design of ABS sliding mode control system for in-wheel motor vehicle based on road identification

Aiming at the problem that the current Anti-lock Braking System (ABS) control algorithm can not make full use of the ground friction to complete the braking when emergency braking on complex roads, an ABS sliding mode control method based on road surface identification is proposed. Combined with the in-wheel motor of in-wheel motor electric vehicle, a coordinated control method of motor regenerative braking and mechanical friction braking is designed. Based on the neural network control method, the road friction coefficient is estimated to realize the identification of typical roads and dynamically obtain the optimal slip ratio of different roads. The ABS sliding mode controler is designed with the optimal slip ratio and the actual slip ratio as input, and the saturation function is used to replace the symbol function in the traditional sliding mode control to weaken the chattering problem, and then the ABS controller is designed. The research results show that compared with the traditional ABS sliding mode controller, the designed ABS neural network sliding mode controller has the following advantages: 1. Through the identification of tire-road friction coefficient by neural network, the real-time performance and road adaptability of ABS controller are improved; 2. When the braking simulation is carried out at a given speed, the braking time is reduced by about 1.03 % and the braking distance is shortened by about 1.01%; 3. The identification of tire-road friction coefficient is realized, which weakens the chattering problem of traditional sliding mode control and improves the stability and robustness of ABS controller.

Collaborative control of path tracking and roll stability in intelligent commercial vehicle: A minimax robust regulator based on Nash game

—Currently, most vehicles are generally equipped with roll stability control system or electronic stability control system to improve stability. However, when encountering emergency obstacle avoidance and similar conditions, roll stability control system may compete with path tracking control system for vehicle control, resulting in the inability to balance path tracking and roll stability. Therefore, a closed-loop feedback Nash game collaborative control method is proposed to solve the collaborative problem between the two control systems. Firstly, a fifth-order vehicle model with coupled yaw-roll dynamics is presented, which was extended to a vehicle-road closed-loop model based on lateral position, yaw angle and road preview information. Secondly, the path tracking control system and roll stability control system are regarded as two agents in the dynamic game control process. On this basis, a Nash game collaborative control strategy is designed by fully considering the control interaction between two game agents. Finally, considering the influence of uncertain disturbances, a finite-time minimax robust control strategy is proposed by using minimax optimization theory. Simulation and hardware in loop experiments show that the minimax robust regulator based on Nash game controller can effectively improve the path tracking and roll stability performance of vehicles. Moreover, the regulator improves the robustness of the system when it is subjected to bounded uncertain lateral disturbances.

Electronic anti-theft protection for vehicles of people with special needs

The article presents an electronic anti-theft protection subsystem that was incorporated into the vehicle control system dedicated to people with special communication needs. The aim of the work was to design and test an electronic subsystem allowing for the acquisition and quick analysis of fingerprint images, checking whether they belong to the vehicle owner and generating a signal allowing the vehicle to be started. The following hypothesis was put forward: it is possible to use artificial intelligence for accurate, automatic classification of fingerprint images acquired in a system with a single-chip microcomputer in order to identify a person. The research niche includes the use of biometrics in electronic anti-theft security adapted to people with special needs. This solution reduces the risk of theft and unauthorized use of special vehicles tailored to the owners' individual needs. The use of a commercial algorithm offered by the sensor manufacturer or the use of artificial neural networks for the classification of fingerprints was considered. The results of research on the accuracy of fingerprint recognition obtained from the developed subsystem are presented. The experiments performed based on the NASNetLarge artificial neural network model confirmed the possibility of achieving recognition accuracy for the test set of 99.99%. Additionally, practical aspects of the applicability of the presented device in an electric vehicle control system supporting the movement of people with physical and/or intellectual disabilities were discussed. The presented solution, after minor modifications, can be used in access security systems for protected rooms, as well as vehicles and military equipment.