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Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers. [ABSTRACT FROM AUTHOR]

This study explores the effectiveness of vertical force control in in-wheel motors (IWMs) to enhance ride comfort in electric vehicles (EVs). A dynamic vehicle model and a proportional ride-blending controller were used to reduce vertical vibrations of the sprung mass. By converting the state-space model into a transfer function, the system's frequency response was evaluated using road profiles generated according to ISO 8608 standards and converted into Power Spectral Density (PSD) inputs. The frequency-weighted acceleration ( a w ) was calculated based on ISO 2631 standards to measure ride comfort improvements. The results showed that increasing the proportional gain ( K p ) effectively reduced the frequency-weighted acceleration and the RMS of the vertical acceleration of the sprung mass. However, the proportional gain could not be increased indefinitely due to the torque limitations of the IWMs. Optimal proportional gains for various road profiles demonstrated significant improvements in ride comfort. This study concludes that advanced suspension technologies, including the proportional ride-blending controller, can effectively mitigate the challenges of increased unsprung mass in IWM vehicles, thereby enhancing ride quality and vehicle dynamics. [ABSTRACT FROM AUTHOR]

The systems that are currently installed in autonomous vehicles are not designed to avoid obstacles on the road by manoeuvring at the limits of the vehicle and the road surface – the goal of the research presented was to develop such system. For these purposes, a universal test track based on ISO 3888-1 and ISO 3888-2 standards was adopted, which can be used to represent any situation in which there is an obstacle on the road in front of the vehicle that needs to be avoided. An analysis of the curves for generating the paths through the test track was carried out, on the basis of which the Bézier curves were chosen. In addition to them, the results of simulations in Adams Car software were used to generate the paths, which can be considered equivalent to real-world trials with a professional driver behind the wheel. A control model was developed for longitudinal and transverse control of the vehicle based on PID controllers, with the selection of optimal parameters – the ones that define PID controllers, and the others that define other characteristics of the model. The model proved to be successful in guiding the vehicle through the test track at all speeds at which manoeuvres are possible. Bézier curves are shown to be a better choice at lower speeds, while paths based on Adams Car simulations are a better choice at higher speeds. [ABSTRACT FROM AUTHOR]

This article addresses a common issue in the design of battery electric vehicles (BEVs) by introducing a comprehensive methodology for the modeling and simulation of BEVs, referred to as the "PerfECT Design Tool". The primary objective of this study is to provide engineers and researchers with a robust and streamlined approach for the early stages of electric vehicle (EV) design, offering valuable insights into the performance, energy consumption, current flow, and thermal behavior of these advanced automotive systems. Recognizing the complex nature of contemporary EVs, the study highlights the need for efficient design tools that facilitate decision-making during the conceptual phases of development. The PerfECT Design Tool is presented as a multi-level framework, divided into four logically sequential modules: Performance, Energy, Currents, and Temperature. These modules are underpinned by sound theoretical foundations and are implemented using a combination of MATLAB/Simulink and the vehicle dynamics software VI-CRT. The research culminates in the validation of the model through a series of experimental maneuvers conducted with a Tesla Model 3, establishing its accuracy in representing the mechanical, electrical, and thermal behavior of BEVs. The study's main findings underscore the viability of the design tool as an asset in the initial phases of BEV design. Beyond its primary application, the tool holds promise for broader utilization, including the development of active control systems, advanced driver assistance systems (ADAS), and solutions for autonomous driving within the domain of electric vehicles. [ABSTRACT FROM AUTHOR]

Relocating the electric motor to a position inside the wheel offers a variety of benefits, such as reduced overall vehicle weight, more efficient production of vehicles, more flexible design and better road handling because of the accurate torque control on each wheel. However, the motor also increases the unsprung mass, which can impair the ride comfort of the vehicle. In this paper, we propose and investigate a control strategy using the vertical component of the driving force provided by an in-wheel motor for better ride comfort and handling dynamics. In the next stage, a mathematical model of the vehicle with coupled longitudinal, lateral, and vertical dynamics was introduced. The parameters of the vehicle were measured and collected from an actual car equipped with in-wheel motors. To analyze the performance of the controller, a model of the vehicle was tested in a series of simulations. The straight-line acceleration test indicated an improvement to the ride comfort of 31.8% in terms of the RMS vertical vibrations of the sprung mass. The control is achieved without any deterioration of the tire load and the vehicle's longitudinal dynamics. The stable lateral dynamics of the controlled vehicle was confirmed using a sine-sweep steering excitation, conducted on a perfectly smooth road surface with a constant longitudinal acceleration. [ABSTRACT FROM AUTHOR]

Featured Application: The methodology presented in this paper can be applied to the design and evaluation of torque vectoring systems in hybrid electric vehicles. Comparison of different controllers is helpful in making design choices in these applications. In today's automotive industry, electrification is a major trend. In-wheel electric motors are among the most promising technologies yet to be fully developed. Indeed, the presence of multiple in-wheel motors acting as independent actuators allows for the implementation of innovative active systems and control strategies. This paper analyzes different design possibilities for a torque vectoring system applied to an originally compact front-wheel drive hybrid electric vehicle with one internal combustion engine for the front axle and two added electric motors integrated in the wheels of the rear axle. A 14 degrees of freedom vehicle model is present o accurately reproduce the nonlinearities of vehicle dynamic phenomena and exploited to obtain high-fidelity numerical simulation results. Different control methods are compared, a PID, an LQR, and four different sliding mode control strategies. All controllers achieve sufficiently good results in terms of lateral dynamics compared with the basic hybrid version. The various aspects and features of the different strategies are analyzed and discussed. Chattering reduction strategies are developed to improve the performance of sliding mode controllers. For a complete overview, control systems are compared using a performance factor that weighs control accuracy and effort in different driving maneuvers, i.e., ramp and step steering maneuvers performed under quite different conditions ranging up to the limits. [ABSTRACT FROM AUTHOR]

This paper presents a state-of-the-art estimation technique by cross-combining a number n of filters for high-precision, reliable and robust vehicle sideslip angle state estimation, over a full range of vehicle operations irrespective of the driving mission and disruptions that may occur in the system. A machine-learning algorithm based on decision trees connects several filters together to switch between them according to the driving context, ensuring the best possible state estimate for relatively small and large sideslip angle values. In conjunction with the above-mentioned aspects, a seamless transition between different vehicle models is attained by observing the key parameters characterizing the lateral motion of the vehicle. The tests conducted using a prototype vehicle on a snow-covered track confirm the effectiveness and reliability of the proposed approach. [ABSTRACT FROM AUTHOR]

The automotive industry is experiencing increasing competition, and vehicle development is becoming increasingly complex. Manufacturers must therefore be able to rapidly compare the outcomes of experimental tests carried out under different conditions. Robust simulation tools that can adjust for external factors have the potential to save a significant amount of time. In this regard, the purpose of this paper is to propose a method for evaluating the effect of asphalt temperature on tire and vehicle lateral dynamic performance, based on empirical data. Because rubber is a viscoelastic material, its properties are heavily influenced by the operating conditions. Therefore, a corrective algorithm must be created to enable the transfer of results obtained from tests carried out under different asphalt temperature conditions to a reference temperature of 25 °C. This article presents an analytical model that accurately describes this phenomenon, as well as the methods employed to generalize and optimize the model. Generalizability represents a crucial aspect of this research, as the model must be widely applicable across several vehicle categories while requiring minimal data to perform the corrections effectively. Finally, the analytical compensatory tool was incorporated into a MATLAB bicycle model to update the numerical transfer function measurements that describe the vehicle's dynamic behavior during experimental maneuvers. These results indicate that modest data is needed to achieve good levels of accuracy, making the model and vehicle dynamics implementation promising. [ABSTRACT FROM AUTHOR]

Minimum lap-time planning (MLTP) is a well-established problem in the race car industry to provide guidelines for drivers and optimize the vehicle's setup. In this paper, we tackle the 3D nature of the problem in its full extension, making no simplifying assumptions on the mechanics of the system. We propose a multibody vehicle model, described by rigorous dynamical equations. To effectively handle the resulting complexity, we devised an efficient direct dynamics computational method based on Featherstone's articulated-body algorithm (ABA). To solve the MLTP, we employed a direct-collocation technique, discretizing the problem so that all information of the 3D track is pre-processed and directly embedded into the discrete problem. This discretization approach turns out to be perfectly compatible with our vehicle model, leading to a solution in accessible computational time frames. The high level of detail of the model makes the proposed approach most useful for in-depth vehicle dynamics analyses on complex tracks. To substantiate the analysis, we provide a comparison with the results obtained by a double-track model on the Nürburgring Nordschleife circuit. Consistently with the average trend defined by the double track, the proposed model features a more dynamically rich behavior, realistically capturing the higher-order effects elicited by the sharp corners and the highly variable slope of the track. [ABSTRACT FROM AUTHOR]

Heavy vehicles exiting (or entering) a tunnel at high speed under a strong crosswind is a particularly critical condition since the aerodynamic load changes drastically, greatly affecting the lateral stability of the vehicle. Often, active control systems (active suspensions, active front steering, etc.) and infrastructure elements (e.g., wind fences) are proposed to reduce the induced risks. To help the design of these devices, the present paper investigates the response of the vehicle–driver system in the case of a high-sided lorry exiting a tunnel under crosswind, by using Driver-In-the-Loop simulations. The study was performed using the dynamic driving simulator of Politecnico di Milano and 28 test drivers. Vehicle and aerodynamic models have been developed to reproduce the phenomenon in a highly immersive environment. During the tests, several combinations of vehicle and wind speed were considered. The effect of vehicle loading condition (Empty and Laden) was also investigated. The performed tests allowed us to gain information about the sequence of the driver's actions and associated delays, which may induce lane deviation or, in the worst case, rollover. It was found that lane invasion may happen for ratios of lateral aerodynamic force over vehicle weight force bigger than 0.1, while rollover could happen for ratios bigger than 0.3. Moreover, it was found that the driver's response typically happens with a delay of ∼0.25 s with respect to the onset of the crosswind stimulus. [ABSTRACT FROM AUTHOR]

To improve the real-time performance of the estimation method of road friction coefficient (RFC) based on moving horizon estimation (MHE), a hierarchical estimation method for RFC combining single-step MHE (S-MHE) and inverse tire model (ITM) based on lateral vehicle dynamics is proposed in this study. Firstly, a hierarchical estimation framework is designed to decouple vehicle and tire systems. Secondly, the S-MHE estimator is designed based on the nonlinear vehicle model to estimate the lateral tire force. Thirdly, the ITM is deduced based on the Pacejka model, and the estimator for the RFC based on the ITM is designed. Finally, the estimation accuracy, convergence speed, and real-time performance of the proposed method and the traditional MHE method are compared and discussed through different tests based on CarSim and Simulink. The results show that compared with the traditional MHE method, the proposed method reduces the average computation time to about 0.125 s and improves the real-time performance by more than 30% while ensuring the estimation accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Vehicle mass is an important parameter for motion control of intelligent vehicles, but is hard to directly measure using normal sensors. Therefore, accurate estimation of vehicle mass becomes crucial. In this paper, a vehicle mass estimation method based on fusion of machine learning and vehicle dynamic model is introduced. In machine learning method, a feedforward neural network (FFNN) is used to learn the relationship between vehicle mass and other state parameters, namely longitudinal speed and acceleration, driving or braking torque, and wheel angular speed. In dynamics-based method, recursive least square (RLS) with forgetting factor based on vehicle dynamic model is used to estimate the vehicle mass. According to the reliability of each method under different conditions, these two methods are fused using fuzzy logic. Simulation tests under New European Driving Cycle (NEDC) condition are carried out. The simulation results show that the estimation accuracy of the fusion method is around 97%, and that the fusion method performs better stability and robustness compared with each single method. [ABSTRACT FROM AUTHOR]

Due to the uncertainty of vehicle model parameters, modeling errors and external disturbances, the performance of path tracking control system is poor, especially under high velocity and large curvature extreme conditions. To address this issue, this paper presents a novel gain-scheduled robust control strategy based on linear parameter varying system with model predictive control (MPC) and H∞. Firstly, fully considering the influence of the time-varying characteristics of vehicle velocity and tire cornering stiffness on the path tracking system model, a novel linear parameter varying system model is built for path tracking control of autonomous vehicle. Then, a path tracking robust controller is designed based on gain-scheduled approach, and the linear matrix inequality (LMI) is applied to solve optimization problem, in which MPC and H∞ robust control theory are applied to the controller design process. Finally, the simulation experiments have verified that the proposed novel robust control strategy can improve the path tracking accuracy and ensure the vehicle lateral and roll stability, especially under high velocity and large curvature extreme conditions. Meanwhile, the proposed robust controller shows superiority to suppress the parameters uncertainty, modeling error and external disturbance. [ABSTRACT FROM AUTHOR]

In this paper, a linear time-varying model predictive controller (LTV-MPC) is proposed for automated vehicle path-following applications. In the field of path following, the application of nonlinear MPCs is becoming more common; however, the major disadvantage of this algorithm is the high computational cost. During this research, the authors propose two methods to reduce the nonlinear terms: one is a novel method to define the path-following problem by transforming the path according to the actual state of the vehicle, while the other one is the application of a successive linearization technique to generate the state–space representation of the vehicle used for state prediction by the MPC. Furthermore, the dynamic effect of the steering system is examined as well by modeling the steering dynamics with a first-order lag. Using the proposed method, the necessary segment of the predefined path is transformed, the linearized model of the vehicle is calculated, and the optimal steering control vector is calculated for a finite horizon at every timestep. The longitudinal dynamics of the vehicle are controlled separately from the lateral dynamics by a PI cruise controller. The performance of the controller is evaluated and the effect of the steering model is examined as well. [ABSTRACT FROM AUTHOR]

This article deals with the control chassis dynamics of a light electric vehicle. In the frame of Global Chassis Control, it proposes a hierarchical control approach focuses on compensating the chassis dynamics against driver disturbances. It is based on a supervisory structure consists of two main levels, a global controller for regulating chassis variables and a local controller to each suspension system. Both controllers are designed using a fractional-order robust controller, namely CRONE controller, to provide an optimized solution for the performance-robustness tradeoff. The main objective is to improve ride comfort for passengers while respecting road holding and handling criteria. In contrast to other studies, random road disturbances are considered to investigate the limits of the proposed approach. Analysis in frequency and time domain has been done to evaluate the performance and robustness of the designed controller. Simulation results based on a full nonlinear 14 degree of freedom vehicle model show that the proposed strategy can effectively improve the ride quality in the field of interest where a significant performance in driver disturbance rejection is reported. [ABSTRACT FROM AUTHOR]

This article focuses on the 3-D guidance strategy for a reusable launch vehicle (RLV) during terminal area energy management (TAEM) phase. Based on sliding-mode and shrinking-horizon techniques, the proposed scheme consists of trajectory generation and correction mechanisms, which can enhance the guidance precision and robustness against disturbances. The RLV guidance model, in the form of a set of highly nonlinear differential equations in the time domain, is recast as an altitude-domain model. By this means, the main characteristics of TAEM gliding motion are extracted. The altitude-domain model is thereby used for trajectory generation. A sliding surface and a guidance law are proposed. Hybrid TAEM constraints can be fully satisfied when the proposed guidance law drives the altitude-domain vehicle model to the designated altitude. Using the proposed guidance law as the input of the altitude-domain model, a constrained TAEM trajectory is generated, leading to TAEM guidance commands simultaneously. The commands are utilized to drive the time-domain model to the terminal target. In an attempt to compensate for model uncertainties and initial deviations, the guidance commands are modified periodically by the shrinking-horizon correction mechanism according to current states. Simulations on different scenarios are provided to demonstrate the performance of the proposed guidance strategy. [ABSTRACT FROM AUTHOR]

Longitudinal velocity, lateral velocity, and front wheel steering angle are crucial states for vehicle active safety features. However, direct measurement of these variables requires expensive measurement instruments and can be easily affected by vehicle nonlinear dynamics, outliers and noise pollution. Moreover, unknown inputs bring great challenges to their accurate estimation. Therefore, this paper develops a novel robust unscented M-estimation-based filter (RUMF) for vehicle state estimation with unknown driver steering torque. The nonlinear system model is constructed based on the vehicle dynamics model. Unscented transformation (UT) and statistical linearization are implemented to transform the nonlinear process and measurement function into a linear-like regression form with data redundancy to achieve outlier suppression. Then, an M-estimation-based iterated algorithm is designed to address process uncertainty and innovation and observation outliers for robust vehicle state estimation. The iteratively reweighted least-squares (IRLS) method based on the M-estimation methodology is utilized for unknown input estimation. Simulations and experimental results compared with extended Kalman filter (EKF) and particle filter (PF) have verified the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Automated driving functions are expected to increase both the safety and ride comfort of future vehicles. Ensuring their functional safety and optimizing their performance requires thorough testing. Costs and duration of tests can be reduced if more tests can be performed numerically in a feasible simulation framework. This simulation setup must include all subsystems of the autonomous vehicle, which significantly interact with the system under test. In this paper, a modular model chain is presented, which is developed for testing systems with an impact on vehicle motion. It includes trajectory planning, motion control, and a model of the vehicle dynamics in a closed loop. Each subsystem can easily be exchanged to adapt the model chain with respect to the simulation objectives. As a use case, the testing of an active suspension control system is discussed. It is designed directly for use in autonomous cars and uses inputs from the vehicle motion planning subsystem for planning the suspension actuator motion. Using the presented closed-loop model chain, the effect of different actuator control strategies on ride comfort is compared, such as curve tilting. Furthermore, the impact of the active suspension system on lateral vehicle motion is shown. [ABSTRACT FROM AUTHOR]

The goal of this work is the development of a general nonlinear single-track model for the cases of curved flat or banked road paths and the identification of the fundamental "handling bricks" or vehicle "DNA", extending an existing theory for flat road tracks. The handling bricks are related to specific design parameters of the vehicle. It is confirmed (both theoretically and numerically), that two structurally different vehicles, with the same handling bricks, exhibit very similar or identical handling behavior. The current nonlinear model accepts general steering input, and arbitrary axle characteristic functions and can be used for general (flat or banked) road tracks. If necessary, various types of constraints can be easily enforced, linking the dynamic parameters of the model. For example, given a complex steering input, the vehicle's (center of gravity) velocity and (or) the vehicle heading, can be related to the direction of the banked road path. On the other hand, the road path can be given independently of the steering data to check the deviation between the intended and the obtained road paths. An implicit nonlinear Crank-Nicolson time marching technique (with additional internal iterations at each time step), has been developed and implemented for the set of (nonlinear) ordinary differential equations (in a homemade VBA framework). The numerical results, using magic formula-based axle characteristic functions, confirm the robustness of the model formulation. • Development of a general nonlinear single-track model. • Identification of the fundamental "handling bricks" or vehicle "DNA". • Applications in curved flat or banked road paths. • General steering input and arbitrary axle characteristic functions. • Development of a novel implicit nonlinear Crank-Nicolson time marching technique. [ABSTRACT FROM AUTHOR]

A novel longitudinal, lateral, and vertical integrated stability control system is proposed for distributed electric vehicle in this paper. The integrated stability control system is based on model predictive control and takes advantage of its multi-objective optimization feature to takes manipulability, stability and comfort requirements into consideration at the same time, and realize the optimal longitudinal $/$ lateral $/$ vertical forces distribution with constraints overall considered. The advantage of the proposed integrated control system is that it can solve the control objective conflict of the cooperative motion control subsystems and the interference of the actuators compared to distributed control system. Finally, the simulation and comparison analysis are given in several different driving conditions under Carsim and Matlab $/$ Simulink environment. Results show that an improvement of over 80 $\%$ in the pitch angle and roll angle is realized compared to the simple control strategy. [ABSTRACT FROM AUTHOR]

This paper presents a novel estimation and identification approach for lateral vehicle dynamics. The algorithm leverages on a Linear Fraction Transform (LFT) reformulation of vehicle and tyre models, allowing for a simple and computationally efficient inclusion of complex and nonlinear dynamic models, like, for example, two-wheels, four-wheels or single-track as vehicle model, and Pacejka, brush or Fiala as tyre model. As a result, this technique can be easily adopted in the development of an online identification system, able to run on a standard embedded device, implementing a flexible identification procedure that can handle different driving conditions, up to the limits of handling, different vehicle modelling approaches, and different input measurements. Experimental results demonstrate the effectiveness of the proposal, either in a persistent excitation and in a non-persistent excitation scenario. [ABSTRACT FROM AUTHOR]

This research presents a data-driven Neural Network (NN)-based Virtual Sensor (VS) that estimates vehicles' Unsprung Mass (UM) vertical velocity in real-time. UM vertical velocity is an input parameter used to control a vehicle's semi-active suspension. The extensive simulation-based dataset covering 95 scenarios was created and used to obtain training, validation and testing data for Deep Neural Network (DNN). The simulations have been performed with an experimentally validated full vehicle model using software for advanced vehicle dynamics simulation. VS was developed and tested, taking into account the Root Mean Square (RMS) of Sprung Mass (SM) acceleration as a comfort metric. The RMS was calculated for two cases: using actual UM velocity and estimations from the VS as input to the suspension controller. The comparison shows that RMS change is less than the difference threshold that vehicle occupants could perceive. The achieved result indicates the great potential of using the proposed VS in place of the physical sensor in vehicles. [ABSTRACT FROM AUTHOR]

Path planning is a critical part for improving the driving safety and driver comfort of autonomous vehicles (AVs), especially in complex maneuvering conditions. In addition, different drivers have different preferences for AVs, thus, how to provide personalized trajectories for different drivers is a vital issue for AVs. The collision-free path planning problem in conditions with large road curvatures is investigated in this paper, with the consideration of environmental safety constraints, drivers’ comfort, vehicle actuator constraints, etc. Firstly, a Driver-Vehicle-Road (DVR) system is established based on the combination of the kinematic vehicle model and the two-point visual preview driver model, such that the driver's individual handling characteristics can be considered in the controller. The kinematic vehicle model is modified to have the similar understeering characteristics with those of the nonlinear full car models, and then the proposed DVR system can satisfy different groups of drivers and cars. Secondly, for environmental constraints, a new artificial potential field (APF) method is proposed, which can form a banana-shaped 3-D dangerous imaginary mountain and a lane boundary cliff suitable for arbitrary curvature roads to generate a collision-free evasive path. Finally, the Linear-Time-Varying (LTV) model predictive control (MPC) method is adopted to design the path planner. The CarSim-Simulink joint simulation illustrates that with the proposed planner, the host vehicle is capable of avoiding obstacles with a safer and more comfortable maneuver on large curvature roads. And the proposed path planner can provide individually safe trajectories for different drivers with good maneuverability. [ABSTRACT FROM AUTHOR]

In this work, a novel single-level adaptive trajectory planner and tracking controller is developed for off-road autonomous vehicles operating on deformable terrains. Trajectory planning and tracking algorithms often rely on a simplified vehicle model to predict future vehicle states based upon control inputs, hence requiring accurate modeling and parameterization. On off-road deformable terrains this is a challenging task due to unknown terrain parameters and the complex interactions at tire-terrain interfaces, which pose issues in continuous differentiability, operating conditions, and computational time. To address these difficulties, in this paper, a neural network deformable terrain terramechanics model and its implementation within a terrain adaptive model predictive control algorithm is presented to improve vehicle safety and performance through more accurate prediction of the plant response. It is shown in simulations that the neural network is able to predict the lateral tire forces accurately and efficiently compared to the Soil Contact Model as a state-of-the-art model and is able to yield accurate bicycle model predictions. It is demonstrated that the implementation of the neural network within model predictive control can outperform both a baseline Pacejka-based and a rapidly exploring random tree controller by improving performance and allowing for more severe maneuvers to be completed that otherwise lead to failure when terrain deformations are not explicitly taken into account. The improved performance achieved through estimating terrain parameters online in an adaptive controller is highlighted against the nonadaptive realization. Finally, it is shown the algorithm is conducive to real-time implementation. [ABSTRACT FROM AUTHOR]

Energy storage devices such as batteries and supercapacitors are arousing a growing interest in many areas of industrial applications since they allow improving the dynamic response and efficiency. In this sense, the availability of electric models of batteries and supercapacitors is necessary for the correct sizing, control, and energy management of power systems. In order to identify the parameters of the equivalent electric model, the identification current profile is often a pulse of charge or discharge. Subsequently, these models are validated with a current verification profile. However, the verification profile compared to the identification profile is more dynamic in amplitude and frequency, producing validation errors due to the identification signal's limited bandwidth. This paper proposes a novel Pulse – Pseudo Random Binary Sequence (Pulse – PRBS) identification current profile, with similar spectral characteristics to the verification profile, in order to improve the modeling accuracy. The parameters of a Lithium-ion battery and a supercapacitor are obtained with this new identification current profile and the Optimization Toolbox of Matlab, using the electrical models in Simulink or Simscape. Finally, the models are validated with the Urban Driving Cycle ECE-15 and the Hybrid Pulse Power Characterization (HPPC) for electric vehicle applications. [ABSTRACT FROM AUTHOR]

In vehicle trajectory prediction, the difficulty in modeling the interaction relationship between vehicles lies in constructing the interaction structure between the vehicles in the traffic scene. Majority of existing models only focus on the interaction between the historical trajectory of the vehicle and the surrounding vehicles in the spatial domain, and do not pay attention to the interaction between the vehicle and the non-Euclidean correlation structure (graph structure) that exists in the environment. In order to overcome the deficiencies in the existing models, this paper proposes the Environment-Attention Network model (EA-Net) to obtain the full interactive information between the vehicle and its driving environment. In the proposed model, a new type of parallel structure consisting of Graph Attention network (GAT) and Convolutional social pooling containing Squeeze-and-Extraction mechanism (SE-CS), is constructed as the environmental feature extraction module and embedded in LSTM encoder-decoder. This structure solves the limitation of the dimension and structure influence when constructing the interaction model between the vehicle and the surrounding environment, making the extracted feature information comprehensive and effective. The prediction accuracy of the model with the RMSE loss function is tested on two public datasets– NGSIM and highD, and compared with several state-of-the-art trajectory prediction algorithm models. The results show that the prediction accuracy of the proposed Environment-Attention Network in the two datasets is more than 20% higher than that of the single structure model, which indicates that the proposed model proposed has superior performance and better adaptability to different traffic environments compared with the existing models. [ABSTRACT FROM AUTHOR]

Motion planning by considering it as an optimal problem is an effective and widely applicable method. Its comprehensive performance greatly depends on the vehicle dynamics model, which is highly coupled and nonlinear, especially under the dynamical scenarios and causes much more consumption of computation resources for the numerical optimization. To increase the real time performance of the motion planner designed by nonlinear model predictive control (NMPC), a unified and simplified vehicle dynamics model (SDM) is presented to make a balance between the accuracy and complexity for dynamical driving scenarios. Based on the statistical analysis results of naturalistic driving conditions, a unified nonlinear vehicle dynamics model is set up, which considers the tyre cornering characteristic and is also applicable to conditions with large turning angle. After the validation of this coupled dynamics model (CDM) by comparisons with other widely used models under a variety of conditions, the coupling effect is analyzed according to the transfer functions, which are obtained by linearizing CDM at equilibrium points. Furthermore, SDM is derived by ignoring the weak part of the coupling effect. The accuracy of SDM is validated by several comparative studies with other models and it is further applied to design a motion planner by NMPC to validate its contribution on the performance improvement under dynamical driving conditions. [ABSTRACT FROM AUTHOR]

This article is motivated by an interesting recent result on antilock braking system (ABS) design using two-time scale (TTS)-based estimation. In this article, the basic method of TTS is extended to estimate not only the disturbances but also their derivatives, improving the accuracy of estimation by an order of magnitude. Further, the problem of ABS is extended to an eight-DOF vehicle model to include the effects of load transfer, steering dynamics, and actuator dynamics. Since analysis showed that the use of basic TTS to ABS design results in a poor transient in the estimation error dynamics, this article proposes a new formulation of the TTS method specifically for ABS. Two new ABS-specific sliding surfaces are proposed to compensate the actuator dynamics where the second sliding surface is proposed as an improvement over the first one. The stability, and convergence of estimation, and tracking errors are analyzed. The proposed methods are validated on MATLAB/Simulink, and CarSim platforms for a D-class sedan car. The results are also validated on an experimental ABS platform in the laboratory. [ABSTRACT FROM AUTHOR]

The complex slip characteristics between the tracks and the terrain make it difficult to build an accurate model for intelligent tracked vehicles. Both the path planning and lateral control, however, are highly dependent on the accurate tracked vehicle model. To overcome the issue, a slip model based on the instantaneous centers of rotation (ICRs) of tracks and a dual-layer adaptive unscented Kalman filter (DAUKF) are used to estimate the ICR locations in real-time without requiring prior knowledge of terrain parameters. First, the historical trajectory information is used to estimate ICR locations by the upper-layer AUKF estimator preliminarily. The ICR locations estimated in the upper-layer and the current vehicle state are then imported into the model to predict the future trajectory which can be used to estimate the offset as compensation of preliminarily estimated ICR locations by the lower-layer AUKF estimator. The proposed DAUKF is verified by simulations on MATLAB/Simulink. In order to further verify the effectiveness and practicability of the algorithm, a large number of experiments under different terrains and road conditions are implemented on the electric-drive tracked vehicle. The simulation and experimental results illustrate that the proposed DAUKF can estimate the ICR locations efficiently and accurately, which can improve the accuracy of the tracked vehicle model compared with those of extended Kalman filter (EKF), UKF, and AUKF. [ABSTRACT FROM AUTHOR]

Accurate predictions of future pedestrian trajectory could prevent a considerable number of traffic injuries and improve pedestrian safety. It involves multiple sources of information and real-time interactions, e.g., vehicle speed and ego-motion, pedestrian intention and historical locations. Existing methods directly apply a simple concatenation operation to combine multiple cues while their dynamics over time are less studied. In this paper, we propose a novel Long Short-Term Memory (LSTM), namely, to incorporate multiple sources of information from pedestrians and vehicles adaptively. Different from LSTM, our considers mutual interactions and explores intrinsic relations among multiple cues. First, we introduce extra memory cells to improve the transferability of LSTMs in modeling future variations. These extra memory cells include a speed cell to explicitly model vehicle speed dynamics, an intention cell to dynamically analyze pedestrian crossing intentions and a correlation cell to exploit correlations among temporal frames. These three individual cells uncover the future movement of vehicles, pedestrians and global scenes. Second, we propose a gated shifting operation to learn the movement of pedestrians. The intention of crossing the road or not would significantly affect pedestrian’s spatial locations. To this end, global scene dynamics and pedestrian intention information are leveraged to model the spatial shifts. Third, we integrate the speed variations to the output gate and dynamically reweight the output channels via the scaling of vehicle speed. The movement of the vehicle would alter the scale of the predicted pedestrian bounding box: as the vehicle gets closer to the pedestrian, the bounding box is enlarging. Our rescaling process captures the relative movement and updates the size of pedestrian bounding boxes accordingly. Experiments conducted on three pedestrian trajectory forecasting benchmarks show that our achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a small-scale hardware-in-the-loop test environment for lateral vehicle dynamics controllers. The test rig consists of a conveyor belt and a 1:10 scale model vehicle. The vehicle is anchored to the frame of the conveyor belt using a special fixture, which constrains only the longitudinal displacement of the car. Therefore, the longitudinal velocity of the vehicle is provided by the conveyor belt, while the steering is generated by the computational unit, where various control methods can be implemented. The test rig is equipped with sensors that provide accurate measurements of the position and orientation of the car, which can be used as feedback in the control algorithms. The paper includes a case study, where the analytical stability analysis of a lane-keeping controller is verified with experiments on the test rig. The proposed test environment provides a compact, cost effective and versatile framework for the testing of various steering control methods in a running vehicle, while maintaining the benefits of a controlled laboratory environment. The experimental setup can also be used for educational and demonstrational purposes. [ABSTRACT FROM AUTHOR]

In this article, we aim to investigate the path following control problem for four-wheel-independent-drive electric vehicles with consideration of modeling errors and complex driving scenarios. In order to guarantee operational safety and robustness, a supertwisting second-order sliding mode (SOSM) control strategy is employed to suppress the heavy chattering issue existing in the traditional sliding mode control (SMC). To degrade the lumped disturbance, a nonlinear disturbance observer (NDOB) technique is proposed to estimate the disturbance and compensate the modeling error. Then, a composite control strategy, which combines the SMC algorithm and NDOB technique is proposed based on stability analysis of the corresponding closed-loop system. Finally, hardware-in-the-loop experimental results show that the supertwisting SOSM control algorithm with the NDOB technique can achieve better tracking performance and suppress the chattering phenomenon. [ABSTRACT FROM AUTHOR]

This paper investigates the robust adaptive neural control of nonminimum phase hypersonic flight vehicle using composite learning. To overcome the nonminimum phase behavior, the output redefinition is employed and the attitude subsystem is transformed to the internal subsystem and the input–output subsystem. For the input–output subsystem, the adaptive neural control works together with the robust control to follow the reference command of pitch angle derived from the internal subsystem. Furthermore, the sliding mode control is constructed in a similar way. For the update of the neural weights, the composite learning is constructed using the prediction error. The stability of the closed-loop system is analyzed via the Lyapunov approach and the ultimately uniform boundedness of the tracking errors can be guaranteed. The effectiveness of the methodology is illustrated by the simulation results. [ABSTRACT FROM AUTHOR]

In this paper, the selected phenomena related to a motor vehicle's motion have been considered based on a computer simulation in MSC Adams/Car. The vehicle's model performed a turning maneuver with the steering wheel release under different road conditions. All simulations have been performed based on the sports two-seater vehicle's model, at the initial speed of 70 km/h on the flat and randomly uneven road. This enabled us to observe the selected phenomena along the road long enough to relate them to different aspects of road traffic safety in unusual situations. For uneven road, the same profiles were assumed for the left and the right wheel of the vehicle, with two coefficient values determining the maximum height of these irregularities. [ABSTRACT FROM AUTHOR]

In a typical traffic scenario, autonomous vehicles are required to share the road with other road participants, e.g., human driven vehicles, pedestrians, etc. To successfully navigate the traffic, an adaptive robust level-k framework can be used by the autonomous agents to categorize the agents based on their depth of strategic thought and act accordingly. However, mismatch between the vehicle dynamics and its predictions, and improper classification of the agents can lead to undesirable behavior or collision. Robust approaches can handle the uncertainties, however, might result in a conservative behavior of the autonomous vehicle. This paper proposes an adaptive robust decision making strategy for autonomous vehicles to handle model mismatches in the prediction model while utilizing the confidence of the driver behavior to obtain less conservative actions. The effectiveness of the proposed approach is validated for a lane change maneuver in a highway driving scenario. [ABSTRACT FROM AUTHOR]

Featured Application: The presented method can be used as a real-time trajectory following algorithm for autonomous vehicles using prediction based on lookahead information. Reinforcement learning-based approaches are widely studied in the literature for solving different control tasks for Connected and Autonomous Vehicles, from which this paper deals with the problem of lateral control of a dynamic nonlinear vehicle model, performing the task of lane-keeping. In this area, the appropriate formulation of the goals and environment information is crucial, for which the research outlines the importance of lookahead information, enabling to accomplish maneuvers with complex trajectories. Another critical part is the real-time manner of the problem. On the one hand, optimization or search based methods, such as the presented Monte Carlo Tree Search method, can solve the problem with the trade-off of high numerical complexity. On the other hand, single Reinforcement Learning agents struggle to learn these tasks with high performance, though they have the advantage that after the training process, they can operate in a real-time manner. Two planning agent structures are proposed in the paper to resolve this duality, where the machine learning agents aid the tree search algorithm. As a result, the combined solution provides high performance and low computational needs. [ABSTRACT FROM AUTHOR]

A microscopic fuel consumption model with respect to comprehensive fuel consumption key impact factors that can reflect dynamic engine operations and various real-world driving conditions is essential for fuel efficient model-based vehicle and powertrain control (MB-VPC). Existing microscopic fuel consumption models are mostly steady state models with limited vehicle parameters as dynamic variables. Data-driven solutions can make the model comprehensive by introducing broad impact factors. However, without the required accessible online computational capability and fast model adaptation mechanisms, those data-driven solutions cannot quickly adapt to unseen driving conditions and engine condition changes based on real-world vehicle data to support MB-VPC. In this paper, a connected vehicle-based data mining (CV-DM) framework is proposed to achieve online adaptive dynamic fuel consumption modeling through knowledge sharing over CVs and the CV remote data center. Based on the CV-DM framework, CV-supported Distributed Meta-regression (CV-DMR) algorithms are developed to realize a fast few-shot adaptation with limited training data. Extensive proof-of-concept experiments are conducted with steady-state and transient vehicle engine data. Compared to the baseline physical model and the existing non-adaptive grey-box model, prediction accuracy is improved by 47%–85% and 38%–80% respectively with only limited training data needed for the subject vehicle. Accordingly, a fuel savings of up to 9.4% can be achieved owing to the improvement of prediction accuracy. [ABSTRACT FROM AUTHOR]

In this article, the problem of control of antilock braking systems under significant uncertainties and unknown relationship between tire-road friction coefficient and wheel slip ratio for a two-axle vehicle model is considered. Two methods based on a disturbance observer and sliding mode control are proposed. In the first method, a conventional sliding surface is used while in the second method a new nonlinear sliding surface is proposed as an improvement over the first method. The performance under the proposed methods is assessed analytically and by MATLAB simulation under different road conditions. The proposed methods are validated further on CarSim platform. [ABSTRACT FROM AUTHOR]

An optimized control approach for the distributed cooperative vehicular platoon was proposed in this paper, considering actuator delay and nonideal communication condition. A hierarchical structure is used to model the vehicular platoon, where the desired acceleration is optimized by CACC controller and the longitudinal tracking controller is proposed to follow it. In the design process of CACC controller, a parameter space approach and linear quadratic regulator (LQR) method are applied to guarantee the string stability and optimized performance. The influence of communication time delay and dropout rate is revealed by the changing of string stable region. Analytical results are verified by numerical simulations of a six-vehicle platoon where the leading vehicle follows the FTP-75 drive cycle speed profile. Furthermore, a Hardware-in-the-Loop (HIL) test is carried out with Carsim to simulate a high-fidelity vehicle dynamic model and dedicated short range communication (DSRC) modems as hardware in the loop as well as the control unit to achieve the inter-vehicle communication. The results show that the designed CACC platooning controller exhibits favorable performance in highway driving scenarios. The platooning vehicles display excellent car-following behavior with high accuracy, driving comfort and safety. [ABSTRACT FROM AUTHOR]

This work considers the problem of position and position-uncertainty estimation for atonomous vehicles during power black-out, where it cannot be assumed that any position data is accessible. To tackle this problem, the position estimation will instead be performed using power separated and independent measurement devices, including one inertial 6 Degrees of Freedom (DOF) measurement unit, four angular wheel speed sensors and one pinion angle sensor. The measurement unit's sensors are initially characterized in order to understand conceptual limitations of the inertial navigation and also to be used in a filtering process. Measurement models are then fused together with vehicle dynamics process models using the architecture of an Extended Kalman Filter (EKF). Two different EKF filter concepts are developed to estimate the vehicle position during a safe stop; one simpler filter for smooth manoeuvres and a complex filter for aggressive manoeuvres. Both filter designs are tested and evaluated with data gathered from an experimental vehicle for selected manoeuvres of developed safe-stop scenarios. The experimental results from a set of use-case manoeuvres show a trend where the size of the position estimation errors significantly grows above an initial vehicle speed of 70 km/h. This paper contributes to develop vehicle dynamics models for the purpose of a blind safe stop. [ABSTRACT FROM AUTHOR]

Cooperative adaptive cruise control (CACC) technique enhances the coordination of vehicles on road. Through CACC controllers, the platoon can exhibit promising traffic performance. In this paper, we study the control synthesis for two types of CACC systems, one of which uses acceleration feedforward while the other uses control feedforward. Under the predecessor-following topology, a general state-space model of vehicle platoon is established and the CACC characteristics including stabilization and string stability are realized by a set of constraints. To relax computation, complete quadratic Lyapunov-Krasovskii functionals are adopted and the CACC controllers are solved by linear matrix inequality tools. The proposed method provides a computationally efficient way to realize CACC design, and comparative experiments with existing work demonstrate the advantages. [ABSTRACT FROM AUTHOR]

This paper investigates the trajectory tracking control problem for autonomous underwater vehicles whose initial starting position differs substantially from that specified by the desired trajectory. This scenario is very likely to cause serious chattering in the control output, especially in the early stages, when the large tracking error is the input to the controller. A novel trajectory tracking control strategy is developed based on fuzzy re-planning of a local desired trajectory. At each time instant, a local desired trajectory is reconstructed based on the AUV's current position and that specified by the original desired trajectory at a future time. Also the control effort is computed based on the local desired trajectory, rather than the original one. Moreover, the interval between each time instant is determined by a new single-input fuzzy model, where the input is determined by the distance between AUV's current and desired trajectories and the change in the distance. Finally, the effectiveness of the new control strategy is verified by simulation-based case studies using an actual vehicle model as a necessary step prior to experimental validation. [ABSTRACT FROM AUTHOR]

This paper presents an integrated platoon control framework for heterogeneous vehicles on curved roads with varying slopes, aerodynamic drag, and wireless communication delays. First, a linear heterogeneous vehicle platoon model with decoupled longitudinal and lateral dynamics is built based on nonlinear feedback linearization and arc-length parametric transformation of an arbitrary curved road. The external disturbances are also considered, including the up-down road slopes, wind and equivalent time-delays caused by the wireless communication delay and discrete data in onboard sensors. Second, to achieve stable platoon control on a curved road, the platoon uses the state-feedback control method in the longitudinal direction while each vehicle tracks the centerline of the curved road to realize lateral control. Longitudinally, an $ H_\infty$ platoon controller is designed based on the Lyapunov-Krasovskii functional to ensure the inner-vehicle and string stability of the platoon, which suffers from parameter uncertainty, and external disturbances. Additionally, the $ \mathcal {L}_2$ string stability is defined to guarantee that the perturbations do not grow unbounded as they propagate through the platoon. Simulation results indicate that the proposed robust controller achieves proper platoon intervehicle spacing in both the longitudinal and lateral directions. [ABSTRACT FROM AUTHOR]

This paper studies a control architecture for vehicle lateral dynamics based on the execution of optimal trajectories via feedforward inverse model control. The focus here is on assessing the robustness of this arrangement when the vehicle real forward dynamics is not identically cancelled by the inverse model due to model approximations and parameter uncertainties. The trajectories that are considered are analytic solutions of the minimum square jerk optimal control problem for a simplified kinematic vehicle model in curvilinear coordinates. Various hypotheses are made concerning the mismatch between the inverse model and the actual forward dynamics of the vehicle. Closed-loop stability analysis shows that the studied control scheme guarantees asymptotic stability of both the nominal and the perturbed kinematic model with significant robustness margins. In addition to the theoretical robustness analysis, the same control scheme is validated in simulation using the industry-standard software for virtual vehicle testing IPG CarMaker. [ABSTRACT FROM AUTHOR]

The platoon control for vehicles can efficiently reduce traffic pressure. Current control schemes are able to achieve stable platoon control, but cannot guarantee collision avoidance, connectivity preservation and convergence time at the same time. In this paper, we propose a novel control scheme that can simultaneously guarantee the safe distance and communication connectivity, which is called flexible safe distance constraint, and also can guarantee the specified convergence time. A dynamic gain based distributed control law is first designed for the vehicle with single-integral model. Using the prescribed transient and steady-state performance control approach, the flexible safe distance constraint can be satisfied. Using the time transformation method and Lyapunov stability theory, the closed-loop systems under the proposed protocols can achieve stable platoon within the specified time. Compared with some existing results, the novelty of this work is that the proposed approaches allow a vehicle to converge with the platoon within any preset time without dependence on the initial conditions or system parameters. Another contribution of this work is to propose specified-time platoon control protocols for the vehicle with double-integral model under a flexible safe distance constraint. Furthermore, in order to get closer to the actual system, the controller is extended to the triple-integral system, and some special cases are also discussed. Finally, some simulations are presented to show the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

This study deals with a new integrated control sys-tem using active torque vectoring (ATV) and electronic stability control (ESC) for enhancement of vehicle directional stability and steerability. In this system, the stabilizing yaw moment is calculated using a novel constrained nonlinear controller with both input and state constraints. The proposed controller is designed using the prediction of continuous nonlinear vehicle model. It guarantees the vehicle directional stability by restricting the side slip angle in the admissible range when the yaw rate tracks its desired response for improved steerability. The calculated yaw moment is generated as driving and braking forces by integrated ATV and ESC systems. According to the integration policy, the required yaw moment is initially generated by ATV in the rear wheels to keep the vehicle driving performance. Because of limitation of ATV, if more yaw moment is required, the ESC system applies asymmetric braking pressure to front wheels to compensate the scarcity of ATV. By the proposed strategy, the small reduction of vehicle velocity is achieved and the vehicle drivability performance is maintained. In order to determine the full capacity of ATV, the active differential dynamics is considered in this paper. Simulation studies are conducted to evaluate the efficiency of proposed constrained controller in comparing with the unconstrained controller and a conventional nonlinear model predictive controller (NMPC) developed in the recent papers using 14-DOF vehicle model. By analyzing the results, it is clear that the proposed controller is much faster and easy to solution and implementation. [ABSTRACT FROM AUTHOR]

Robust estimation of vehicle sideslip angle is essential for stability control applications. However, the direct measurement of sideslip angle is expensive for production vehicles. This paper presents a novel sideslip estimation algorithm which relies only on sensors available on passenger and commercial vehicles. The proposed method uses both kinematics and dynamics vehicle models to construct extended Kalman filter observers. The estimate relies on the results provided from the dynamics model observer where the tire cornering stiffness parameters are updated using the information provided from the kinematics model observer. The stability property of the proposed algorithm is discussed and proven. Finally, multiple experimental tests are conducted to verify its performance in practice. The results show that the proposed approach provides smooth and accurate sideslip angle estimation. In addition, our novel algorithm provides reliable estimates of bank angles and lateral acceleration sensor bias. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach is presented to solve the trajectory tracking problem for autonomous vehicles. This approach is based on the use of a cascade control where the external loop solves the position control using a novel Takagi Sugeno‐Model Predictive Control (TS-MPC) approach and the internal loop is in charge of the dynamic control of the vehicle using a Takagi Sugeno‐Linear Quadratic Regulator technique designed via Linear Matrix Inequalities (TS-LMI-LQR). Both techniques use a TS representation of the kinematic and dynamic models of the vehicle. In addition, a novel Takagi–Sugeno estimator‐Moving Horizon Estimator‐Unknown Input Observer (TS-MHE-UIO) is presented. This method estimates the dynamic states of the vehicle optimally as well as the force of friction acting on the vehicle that is used to reduce the control efforts. The innovative contribution of the TS-MPC and TS-MHE-UIO techniques is that using the TS model formulation of the vehicle allows us to solve the nonlinear problem as if it were linear, reducing computation times by 10–20 times. To demonstrate the potential of the TS-MPC, we propose a comparison between three methods of solving the kinematic control problem: Using the nonlinear MPC formulation (NL-MPC) with compensated friction force, the TS-MPC approach with compensated friction force, and TS-MPC without compensated friction force. [ABSTRACT FROM AUTHOR]

Due to the strong nonlinearity, coupling characteristics, external disturbance, and complex driving conditions, it is difficult to establish an accurate mathematical model for the autonomous ground vehicle (AGV). This requires the AGV path following controller to have strong robustness. In this paper, a robust AGV path following control strategy that is based on nonsingular terminal sliding mode (NTSM) and active disturbance rejection control (ADRC) is presented. First, the complex path following problem is simplified to a simple yaw angle tracking problem by constructing a desired yaw angle function that satisfies that the displacement deviation of AGV converges to zero when the actual yaw angle approaches the desired yaw angle. Second, an NTSM-ADRC controller is designed for the system, which uses the extended state observer to estimate and compensate the unmodeled dynamics and unknown external perturbations of the system in real time. In order to improve response characteristics of the controller, the nonlinear error feedback control law is designed by combining the NTSM and exponential approximation law. In contrast to the existing work, the improved controller can use the simple two-degree-of-freedom linear vehicle dynamic model to provide good performance in a range of driving conditions. Finally, the CarSim–Simulink simulation results of typical conditions show that the proposed control strategy can make the AGV follow the reference path quickly and accurately while ensuring the stability of the vehicle and has strong robustness. [ABSTRACT FROM AUTHOR]