Vehicle Motion Control Research Articles

Optimized Super-Twisting Sliding Mode Control with Parameter Estimation for Autonomous Vehicle Longitudinal Motion on Downhill Road

Longitudinal motion control is a critical aspect of autonomous vehicle area, requiring a well-designed controller to ensure optimal system performance, safety, and comfort under varying driving conditions. Previous control methods often face challenges such as chattering effects, and model uncertainties. To address such challenges, in this paper, we propose the application of an optimized super-twisting sliding mode control (OST-SMC) for the longitudinal motion control of autonomous vehicles. The motivation is to enhance the robustness and efficiency of the control system while minimizing the chattering problem. The proposed system’s mathematical modeling and control design are presented in detail with stability analyzed using Lyapunov theory. To enhance the controller’s performance, uncertain parameters are optimized using the gradient descent method, a linear regression-based technique. The OST-SMC algorithm shows enhanced robustness against disturbances and parameter uncertainties compared to conventional sliding mode controllers. Simulations in MATLAB/Simulink and CarMaker validate the proposed method, demonstrating strong performance even on downhill roads. The OST-SMC reduces chattering more effectively than traditional SMCs, achieving smooth tracking and consistent robustness under varying road conditions.

Improved Deep Reinforcement Learning for Efficient Motion Control of Autonomous Vehicle with Domain-Centralized Electronic and Electrical Architecture

Improved Deep Reinforcement Learning for Efficient Motion Control of Autonomous Vehicle with Domain-Centralized Electronic and Electrical Architecture

АВТОНОМНОЕ НАВЕДЕНИЕ БПЛА С ИСПОЛЬЗОВАНИЕМ КОМПЬЮТЕРНОГО ЗРЕНИЯ: ПРОБЛЕМА ТОЧНОГО УПРАВЛЕНИЯ РУЛЯМИ

the development of a mathematical model of autonomous guidance for unmanned aerial vehicles based on computer vision is presented. The model uses a nonlinear response function with dynamic variation of the nonlinearity coefficient depending on the distance to the target, which improves the accuracy and stability of unmanned aerial vehicle guidance given different flight conditions and target characteristics. The problem of video frame aspect ratio mismatch is also considered and methods for normalizing the coordinates of the square on the screen to ensure uniform and predicable unmanned aerial vehicle response are proposed. As a result, an improved adaptive mathematical model is developed that provides more efficient motion control for the unmanned aerial vehicle, allowing it to accurately track the target and keep it in the center of the frame under different flight conditions.

Autonomous Vehicle Motion Control Considering Path Preview with Adaptive Tire Cornering Stiffness Under High-Speed Conditions

The field of autonomous vehicle technology has experienced remarkable growth. A pivotal trend in this development is the enhancement of tracking performance and stability under high-speed conditions. Model predictive control (MPC), as a prevalent motion control method, necessitates an extended prediction horizon as vehicle speed increases and will lead to heightened online computational demands. To address this, a path preview strategy is integrated into the MPC framework that temporarily freezes the vehicle state within the prediction horizon. This approach assumes that the vehicle state will remain consistent for a specified preview distance and duration, effectively extending the prediction horizon for the MPC controller. In addition, a stability controller is designed to maintain handling stability under high-speed conditions, in which a square-root cubature Kalman filter (SRCKF) estimator is employed to predict tire forces to facilitate the cornering stiffness estimation of vehicle tires. The double lane change maneuver under high-speed conditions is conducted through the Carsim/Simulink co-simulation. The outcomes demonstrate that the SRCKF estimator could provide a reasonably accurate estimation of lateral tire forces throughout the whole traveling process and facilitates the stability controller to guarantee the handling stability. On the premise of ensuring handling stability, integrating the preview strategy could nearly double the prediction horizon for MPC, resulting in the limited increase of online computation burden brought while maintaining path tracking accuracy.

Motion Control of a Low-Cost Underwater Vehicle with Three-Position Cross Rudder

Underwater vehicles are widely employed as platforms for marine monitoring and operations, with rudders playing a crucial role in controlling their movements. Traditional underwater vehicles utilize servos to steer rudders, which results in high production and maintenance costs. To reduce the manufacturing costs of small underwater vehicles, this paper proposes a design featuring a three-position cross rudder driven by a binary-controlled electromagnet. Additionally, a novel virtual rudder angle controller is developed, which utilizes PWM frequency conversion to perform rudder control, ensuring motion performance comparable to that of conventional underwater vehicles. By establishing kinematic and dynamic models of a typical underwater vehicle (REMUS100), a corresponding virtual rudder angle control strategy is introduced. Based on the binary-controlled three-position cross rudder and virtual rudder angle controller, the effectiveness of the motion control method is verified by numerical simulation. The results confirm that the proposed virtual rudder angle controller successfully executes turning and spatial path-tracking tasks. The binary-controlled three-position cross rudder, together with the virtual rudder angle controller, offers a cost-effective solution for the practical application of small unmanned underwater vehicles, particularly for disposable underwater vehicles that do not require recovery.

Research on lie group dynamics modeling and trajectory-tracking optimal control of intelligent ground vehicle

Abstract Aiming at the multi-objective integrated coordination problem of trajectory tracking control accuracy and vehicle stability, a Lie-group-based dynamics modeling and trajectory-tracking optimal control method of intelligent ground vehicle is proposed in this paper. Based on the theory of Lie group and Lie algebra, the kinematic relationship of intelligent ground vehicle was established, and the integral variational dynamic equation expression was established using the variational method and Lagrange principle. A vehicle performance index function that balances energy optimization and efficiency optimization was established by combining the two-degree-of-freedom dynamic equation and stability constraints of intelligent ground vehicle. On this basis, an optimal control method for trajectory tracking of intelligent ground vehicle is proposed based on Legendre interpolation polynomial discretization method and Gauss pseudospectral method. The comparative results show that the proposed optimal control method can balance vehicle motion control accuracy, real-time performance, and optimization solution efficiency, thereby ensuring the lateral stability control effect of vehicle while achieving trajectory tracking accuracy.

Rotorcraft in-ground effect models in axial and forward flight

This paper presents in-ground effect (IGE) models that incorporate the rotor advance ratio and vehicle's climbing velocity to predict the IGE thrust variation in axial (vertical) and forward flight for rotor-based aerial vehicles, such as quadcopter uncrewed aerial vehicles (UAVs). Extensive experiments were conducted to validate each and every component of the new IGE models. Discrepancies between the analytical and experimental results in forward flight were found to be consistent with the two characteristic flow regimes of recirculation and ground vortex. Specifically, an additional increase in thrust was observed at a low advance ratio (μ<0.04) in the extreme ground-effect regime (z/R<0.5). The results of this study can be leveraged to develop new vehicle motion control and path planning algorithms for flying near obstacles.

Vehicle Motion Control for Overactuated Vehicles to Enhance Controllability and Path Tracking

The motion control of vehicles poses distinct challenges for both vehicle stability and path tracking, especially under critical environmental and driving conditions. Overactuated vehicles can effectively utilize the available tyre–road friction potential by leveraging additional actuators, thus enhancing their stability and controllability even in challenging scenarios. This paper introduces a novel modular upstream control architecture for overactuated vehicles, integrating a fast and robust linear time-varying model predictive path and speed tracking controller with a model following approach and nonlinear control allocation to form a holistic vehicle motion controller. The architecture decouples the path and speed tracking task from the actuator allocation, where torque vectoring and rear-wheel steering are applied to achieve linear understeer reference vehicle behavior. It allows for the use of a simpler path tracking controller, enabling long preview horizons and enhanced computational efficiency. Nonlinearities, such as the mutual influence of lateral and longitudinal tyre forces, are accounted for within the control allocation. The simulation results demonstrate that the proposed control architecture and overactuation improve vehicle stability in critical driving conditions and reduce path tracking errors compared to a dual-motor vehicle.

Motion control of autonomous underwater vehicle based on physics-informed offline reinforcement learning

Online reinforcement learning (RL) methods for autonomous underwater vehicles (AUV) are time-consuming and unsafe due to the need for real-world interaction. Offline RL methods can improve efficiency and safety by training with dynamic models, but an accurate model for AUV is difficult to obtain due to its highly nonlinear dynamics. These limit the application of RL methods in AUV control. To solve this issue, we propose physics-informed model-based conservative offline policy optimization (PICOPO). It offers the advantages of small dataset, strong generalizability and high safety by combining the physics-informed dynamic modelling method and the offline RL technique. First, the PICOPO constructs a physics-informed model based on a small offline dataset to serve as the digital twins (DT) of the actual AUV. This DT can forecast the long-term motion states of AUV with high-precision. The RL-based controller is then trained offline within this DT, eliminating the need for real-world interaction and allowing direct deployment to the AUV without fine-tuning. In this paper, simulations and field tests are carried out to evaluate the proposed method. Our results demonstrate that PICOPO achieves accurate motion control with just 2000 samples and enables zero-shot sim-to-real transfer, showcasing strong generalizability across various motion control tasks.

Electronic brake system-based hierarchical motion control of commercial vehicle for autonomous obstacle avoidance

As a high-level autonomous driving technology, efficient and safe automatic obstacle avoidance on highway is one of the critical and ongoing topics that requires in-depth research for commercial vehicles. Electronic control air pressure brake system, owing to its fast response and active braking ability, is an effective active-chassis technology to function path tracking and stability control in such condition. Yaw motion stability control problem of autonomous driving commercial vehicle based on active braking control with electronic air brake system for automatic obstacle avoidance is investigated. First, a hierarchical yaw motion dynamics control architecture is introduced after vehicle and brake system modeling. The quintic polynomial curve-based trajectory planning method and a Model Predictive Control based trajectory tracking control strategy are formulated. Second, to evade the possible stability losing issue in this circumstance, Cubature Kalman Filter (CKF) is utilized to estimate the vehicle motion states, and a fuzzy PID control-based differential brake stability control strategy is designed for the electronic brake system composed of front-and-rear two sets of dual-channel pressure regulator and ABS solenoid valve. Finally, the proposed hierarchical yaw stability control strategy for a commercial vehicle in typical obstacle avoidance conditions is comparatively verified based on the joint simulation tools. After the yaw stability control, the peak value of the lateral displacement error of trajectory tracking can be reduced by 44.7%. The yaw stability control strategy based on fuzzy PID control can reduce the yaw rate by 46%–57% and the sideslip angle by 60%–73% under different conditions. The results show that the proposed control strategy can effectively improve the yaw stability of the vehicle while avoiding obstacles at high speed.

Optimization of Power Control for Autonomous Hybrid Electric Vehicles with Flexible Power Demand

Abstract Technology advancement for on-road vehicles has gained significant momentum in the past decades, particularly in the field of vehicle automation and powertrain electrification. The optimization of powertrain controls for autonomous vehicles typically involves a separated consideration of the vehicle's external dynamics and powertrain dynamics, with one key aspect often overlooked. This aspect, known as flexible power demand, recognizes that the powertrain control system does not necessarily have to precisely match the power requested by the vehicle motion controller at all times. Leveraging this feature can lead to control designs achieving improved fuel economy by adding an extra degree of freedom to the powertrain control while maintaining safety and drive comfort. The present research investigates the use of an Approximate Dynamic Programming (ADP) approach to develop a powertrain controller, which takes into account the flexibility in power demand within the ADP framework. The concept of reachable sets is incorporated into the ADP framework to ensure safety, improve ride comfort, and enhance the accuracy of the optimization solution. The formulation is based on an autonomous hybrid electric vehicle (HEV), while the methodology can also be applied to other types of vehicles. It is also found that necessary customization of the ADP algorithm is needed for this particular control problem to prevent convergence issues. Finally, a case study is presented to evaluate the effectiveness of flexible power demand, as addressed by the ADP method. The experiment demonstrates a 14.1% improvement in fuel economy compared to a scenario without flexible power demand.

The study of forced oscillations in the non-linear system of an individual traction drive

BACKGROUND: The processes taking place in the ‘traction electric drive – wheel – road’ system during acceleration and braking cause increased dynamic loads on drive components, which may lead to a breakdown. Therefore, it is important to control the drive in a way to minimize and to suppress the given processes. To make it possible, the control system is to be equipped with a resistance torque observer at the electric motor shaft. In addition, in any system, there is a heighten interest in study of arise of resonances, which come with abrupt increase of oscillations amplitudes. Therefore, the features of oscillation phenomena in the given non-linear system are to be studied. AIM: Identification of the peculiarities of oscillatory processes, resonance phenomena in the systems of electromechanical drive of vehicles, which are nonlinear technical systems. METHODS: The study of features of the oscillating processes and the study of capabilities of arise of resonant phenomena were conducted using analysis of the differential equations system describing the operation of the non-linear system. RESULTS: The features of the oscillating phenomena in non-linear systems of interaction between an elastic wheel and road as well as capabilities of arise of resonant phenomena were considered. It is defined that arise of the resonant phenomena in the considered systems is not possible due to breakdown of them. The behavior of modes of interaction between an elastic wheel and road during intensive acceleration and braking was analyzed. The abrupt shock behavior of change rate of wheel torque and current consumed by a drive as well as features of lowering of them when using the self-oscillating phenomena suppression were found. CONCLUSION: The practical value of the study lies in ability of using the proposed conclusions at development of units of a traction electric drive and at synthesis of vehicle motion control systems.

Research on dynamic constraint mechanism and motion control of intelligent vehicles based on preview distance optimization under complex road conditions

Aiming at the coupling and conflict issues between intelligent vehicle dynamics characteristics and path tracking system under complex road conditions, this paper investigates the dynamics constraint mechanism and motion control of intelligent vehicles, and proposes an intelligent vehicle lateral–vertical cooperative control method based on optimized preview distance. To begin with, an intelligent vehicle path tracking control system based on expected yaw velocity has been designed by establishing a three-degree-of-freedom dynamic model for intelligent vehicle. Then, we analyzed the mechanism by which changes in vehicle speed, road curvature, and preview distance affect the accuracy of vehicle path tracking and handling stability. Considering the “human-vehicle-road” system in intelligent transportation systems, critical values for collision and instability were set. Furthermore, we designed a proactive optimization method for preview distance under different working conditions, using an optimization algorithm to improve path tracking accuracy while ensuring vehicle stability, based on the lateral displacement deviation and lateral orientation deviation representing the accuracy of path tracking, as well as the lateral acceleration representing handling stability. Finally, hardware-in-the-loop platform test was conducted. The simulation and test results show that the optimized path tracking algorithm reduces lateral deviation to as low as 0.05 m, and the stability constraint control in the algorithm can be triggered promptly even under extreme conditions.

An improved hierarchical deep reinforcement learning algorithm for multi-intelligent vehicle lane change

Lane change is a pivotal technology in autonomous driving, playing a significant role in improving traffic conditions. The control task of lane change becomes exceptionally complex for coordination of multiple intelligent vehicles, especially in unpredictable situations. To address this issue, the paper proposes a novel multi-intelligent vehicle lane change method (MVLC) based on Deep Reinforcement Learning (DRL). The MVLC features a hierarchical framework that includes a lower-layer lane change controller and an upper-layer reinforcement learning decision-making method. In the lower layer, a lane change controller is designed to execute motion control for a single vehicle, consisting of a lateral controller utilizing dueling double deep Q-network to make lane change decision and a longitudinal controller that employs Intelligent Driver Model (IDM) to follow the preceding vehicle. In the upper layer, a model-free DRL method is used for simultaneous control of multiple intelligent vehicles. To learn the optimal policy, a comprehensive reward function is designed. Finally, experiments are conducted in mixed traffic to valid the effectiveness of the proposed method. Results show that MVLC achieves secure and timely lane changes for multiple vehicles. Therefore, the method is expected to have significant potential for improving overall traffic efficiency and mitigating traffic congestion.

Improved Taillight Detection Model for Intelligent Vehicle Lane-Change Decision-Making Based on YOLOv8

With the rapid advancement of autonomous driving technology, the recognition of vehicle lane-changing can provide effective environmental parameters for vehicle motion planning, decision-making and control, and has become a key task for intelligent vehicles. In this paper, an improved method for vehicle taillight detection and intent recognition based on YOLOv8 (You Only Look Once version 8) is proposed. Firstly, the CARAFE (Context-Aware Reassembly Operator) module is introduced to address fine perception issues of small targets, enhancing taillight detection accuracy. Secondly, the TriAtt (Triplet Attention Mechanism) module is employed to improve the model’s focus on key features, particularly in the identification of positive samples, thereby increasing model robustness. Finally, by optimizing the EfficientP2Head (a small object auxiliary head based on depth-wise separable convolutions) module, the detection capability for small targets is further strengthened while maintaining the model’s practicality and lightweight characteristics. Upon evaluation, the enhanced algorithm demonstrates impressive results, achieving a precision rate of 93.27%, a recall rate of 79.86%, and a mean average precision (mAP) of 85.48%, which shows that the proposed method could effectively achieve taillight detection.

Vehicle dynamics analysis and adaptive control scheme design under coupled slope for trajectory tracking of four-wheel independent drive autonomous vehicles

Trajectory tracking is the core function of autonomous vehicle motion control. On variable road slopes, vehicles will encounter extra forces due to gravity, distinct from driving on flat roads. These forces will cause significant deviations in trajectory tracking and even instability, especially for four-wheel independent drive autonomous vehicles (4WIDAVs) with closely coupled longitudinal-lateral dynamics. To address above issues, first, a modified dynamics model considering coupled slopes is built, and the slope effect on vehicle motion and stability is analysed. Secondly, treating coupled slopes as augmented states, a square root cubature Kalman filter (SRCKF) is designed for real-time slope estimation. Then, a slope-adaptive model separate predictive trajectory tracking control strategy is proposed, decoupling the slope-adaptive tracking system into longitudinal and lateral subsystems. Predictive control is performed separately to ensure real-time performance, and prediction information is exchanged between subsystems to preserve dynamics coupled characteristics, enhancing tracking accuracy. Finally, the effectiveness of the proposed scheme is validated by simulations and real-vehicle experiment under the varying slope condition. Results show that the proposed scheme improves tracking accuracy by 17.59% and reduces computation time by 55.02% compared to existing scheme in simulations, and in experiment, the tracking error is reduced by 17.89%.

Coordination control strategy of motion stability and tire slip for electric vehicle driven by in-wheel-motors

The Direct Yaw Moment Control (DYC) is a widely utilized technique for electric vehicles equipped with In-Wheel Motors (IWMs) to ensure yaw stability. However, most existing DYC studies calculate the additional control yaw moment solely based on the torque differences among the four IWMs, disregarding the nonlinear tire characteristics and wheel rotational movements. When road conditions are poor or torque changes are intense, the tire may slip, preventing it from generating the required longitudinal tire force for the desired direct yaw moment. To address this challenge, this study introduces a coordination control strategy for motion stability and tire slip in electric vehicles (EV) driven by IWMs. To enhance control performance, the nonlinear tire characteristics and wheel rotational movements are integrated into the modeling process when calculating tire forces. Additionally, a state and parameter coupling estimation algorithm is utilized to obtain feedback on tire parameters and vehicle motion states. Using these estimation results as a foundation, a coordination strategy is developed to achieve reference motion state tracking and tire slip ratio control. The Model Predictive Control (MPC) algorithm is employed to solve the control problem with constraints on both control inputs and outputs, and the estimation results are utilized to update the control model as well as provide states feedback. Simulations and experiments are conducted to validate the feasibility and effectiveness of the proposed control strategy. The results demonstrate that our proposed control strategy effectively improves vehicle stability by coordinating vehicle motion and tire slip control.

A reinforcement learning based autonomous vehicle control in diverse daytime and weather scenarios

Autonomous driving holds significant promise for substantially reducing road fatalities. Unlike traditional machine learning methods that have conventionally been applied to enhance the motion control of Autonomous Vehicles (AVs), recent attention has shifted toward the utilization of Deep Learning (DL) and Deep Reinforcement Learning (DRL) techniques. These advanced approaches have the potential to greatly improve AV vehicle control and empower vehicles to learn from their surroundings. However, the majority of existing research has concentrated on straightforward scenarios, often neglecting the intricate challenges posed by vulnerable road users such as pedestrians, cyclists, and motorcyclists, as well as the influence of varying weather conditions. In this study, we propose a novel model founded on DRL, specifically leveraging Deep-Q Networks (DQN), to effectively manage AVs in complex scenarios characterized by heavy traffic, diverse road users, and diverse weather conditions. Our approach involves training the model in diverse weather conditions, encompassing clear daytime and nighttime as well as challenging weather conditions like heavy rainfall during both the day and sunset. Through this comprehensive training, the AV becomes proficient in navigating safely through intersections and reaching its destination without any accidents. To rigorously evaluate and validate our proposed approach, extensive testing was conducted employing the CARLA simulator. The simulation results unequivocally demonstrate that our model not only reduces travel delays but also minimizes the occurrence of collisions, marking a significant step forward in achieving safer and more efficient autonomous driving.

Optimization Design of Automotive Rear Tails for Enhancing the Downforce and Grip Effect during High Speed

Vehicle motion control and safety have always been key research directions in the field of automotive engineering. However, the traditional rear tail design has limitations in providing downforce, which cannot adequately meet the needs of high-speed driving. In this study, a computational fluid dynamics (CFD) simulation technique combined with an optimization algorithm was used to systematically investigate the influence of rear tail shape and layout on vehicle downforce. By introducing a parametric rear fin design into the CFD model and applying a multi-objective optimization algorithm, various design options can be systematically explored to improve the downforce performance. It was found that with the optimized tail fin design, the car was able to achieve higher downforce and provide better grip and stability during high speed. Specifically, downforce gains of up to 20% were observed, leading to significant improvements in vehicle handling and stability compared to conventional designs. The results of this research are not only of great significance to the field of automotive engineering but also of substantial help to improve the motion performance and safety of vehicles. optimizing the rear tail design can effectively improve the grip of the car in the process of high speed, provide a safer and more stable driving experience for the driver, and is also expected to play an important role in motorsport and other fields.

Personalized Path-Tracking Approach Based on Reference Vector Field for Four-Wheel Driving and Steering Wire-Controlled Chassis

It is essential and forward-thinking to investigate the personalized use of four-wheel driving and steering wire-controlled unmanned chassis. This paper introduces a personalized path-tracking approach designed to adapt the vehicle’s control system to human-like characteristics, enhancing the fit and maximizing the potential of the chassis’ multi-directional driving and steering capabilities. By modifying the classic vehicle motion controller design, this approach aligns with individual driving habits, significantly improving upon traditional path-tracking control methods that rely solely on reference vector fields. First, the classic reference vector field’s logic was expanded upon, and it is shown that a personalized upgrade is feasible. Then, driving behavior data from multiple drivers were collected using a driving simulator. The fuzzy c-means clustering method was used to categorize drivers based on typical states that match vehicle path-tracking performance. Additionally, the random forest algorithm was used as the method for recognizing driving style. Subsequently, a personalized path-tracking control strategy based on the reference vector field was developed and a distributed execution architecture for four-wheel driving and steering wire-controlled unmanned chassis was established. Finally, the proposed personalized path-tracking approach was validated using a driving simulator. The results of the experimental tests demonstrated that the personalized path-tracking control approach not only fits well with various driving styles but also delivers high accuracy in driving style identification, making it highly suitable for application in four-wheel driving and steering wire-controlled chassis.