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A precise vehicle dynamics model is critical for simulation and algorithm testing. Neural networks have been widely used to build high-fidelity vehicle dynamics models due to the excellent learning ability. However, data starvation is a common phenomenon in neural networks. With limited data, it is difficult for neural networks to achieve precise predictions. To address these problems, a hybrid model combining physics and dual attention neural networks is developed to model vehicle lateral dynamics. First, due to the interpretability of the physical model, linear lateral dynamics model is regarded as a prior model. However, due to the imperfect prior knowledge, there are residuals between the prior and the actual vehicle dynamics. Therefore, neural networks are used to characterize the residuals to achieve recalibration of vehicle dynamics model. Modeling vehicle residual dynamics with neural networks is a time series forecasting problem. The GRU with a dual attention mechanism and adaptive initial hidden states (DA-AGRU) is designed to capture spatial and temporal correlations in the vehicle dynamics data. In particular, considering the unique auto regressive structure of the hybrid model, a spatial attention mechanism with a feature fusion module is designed, so as to globally compute the weights of different channel features. The dataset used to train and validate the model is recorded from a vehicle platform, and the experimental results show that the proposed hybrid model can accurately predict vehicle dynamics states in a data-scarce environment. [ABSTRACT FROM AUTHOR]

The automobile industry usually ignores the height of the path and uses planar vehicle models to implement automatic vehicle control. In addition, existing literature mostly concerns level terrain or homogeneous road surfaces for estimating vehicle dynamics. However, ground vehicles utilized in forestry, such as forwarders, operate on uneven terrain. The vehicle models built on level terrain assumptions are inadequate to capture the rolling or pitching dynamics of such machines as rollover of such vehicles is a potential risk. Therefore, knowledge about the height profile of the path is crucial for automating such off-road operations and avoiding rollover. We propose the use of a six-degrees-of-freedom (6-DOF) dynamic vehicle model to solve the autonomous forwarder problem. An adaptive linear tire model is used in the 6-DOF model assuming the vehicle operates in a primary handling regime. The force models are modified to include the three-dimensional (3D) map information. The calibration procedures, identifying actuator dynamics, and quantifying sensor delays are also represented. The proposed vehicle modeling contributed to realizing the continuous-discrete extended Kalman filter (CDEKF), which takes into account the 3D path during filtering and fixed-lag smoothing. Polaris (an all-terrain electric car) is used as a case study to experimentally validate the vehicle modeling and performance of the state estimator. Three types of grounds are selected — an asphalt track, a concrete track with a high elevation gradient, and a gravel track inside a forest. Stable state estimates are obtained using CDEKF and sparse 3D maps of terrains despite discontinuities in satellite navigation data inside the forest. The height estimation results are obtained with sufficient accuracy when compared to ground truth obtained by aerial 3D mapping. Finally, the proposed model's applicability for predictive control is demonstrated by utilizing the state estimates to predict future states considering (3D) terrain. • A 6-DOF dynamic vehicle model is used for autonomous driving in terrain. • The force models are modified considering 3D terrain. • CDEKF with fixed-lag smoothing is used for online state estimation. • State estimation is demonstrated in RTK-GNSS denied areas. • The 3D form of the future path is used in state predictions using the dynamic model. [ABSTRACT FROM AUTHOR]

To enhance vehicle stability and safety, the four-wheel independent steer-by-wire (4WISBW) system has garnered significant attention. However, the characteristics of corner modules, including model parameters uncertainty and disturbance torque, directly contribute to the deterioration of dynamic response in tracking control. And the differences in the characteristics leading to reduced synchronization performance in the 4WISBW system and hindering effective coordination. To enhance the synchronization and tracking control performance of the 4WISBW system, a novel control strategy, coupled with the fictitious master-generalized mean deviation coupling structure (FMGMDCS), is proposed. Firstly, the corner module dynamic model and the vehicle dynamic model are established. Subsequently, the impact of the differences in the characteristics on the system's tracking and synchronization control is analyzed. Next, the FMGMDCS and the angle synchronization controller based on a new reaching law sliding mode control (NRLSMC) are proposed to compensate for synchronization errors in the 4WISBW system caused by the differences in the characteristics of corner modules. Finally, a radial basis function neural network fast terminal sliding mode control (RBF-FTSMC) steering angle tracking controller is designed to enhance the tracking performance of corner modules. Simulation and experimental results indicate that the proposed control strategy can effectively solve the synchronization problem of the 4WISBW system and improve the system's tracking performance. [ABSTRACT FROM AUTHOR]

• A navigation accuracy compensation algorithm for INS is proposed for low-cost USVs. • An event-triggering mechanism is introduced, and the thrust models of the USVs are adopted to design the event trigger. • According to different events, various weights can be assigned to various integrators and PF. • Field experiments are conducted to verify the navigation performance of our algorithm. Integrated navigation is often used for navigation of unmanned surface vehicles (USVs). For a class of cost-sensitive USVs, it is desirable to compose integrated navigation by low-cost inertial navigation systems (INS) and global positioning systems (GPS). In the cases of GPS signal failure or interference, only INS onboard can be used for navigation, which often leads to the navigation accuracy deteriorating and may fail to meet application requirements. Therefore, a navigation accuracy compensation algorithm for low-cost USVs is proposed. An event-triggering mechanism is introduced, and the thrust models of the USVs are adopted to design the event trigger. According to different events, various weights can be assigned to the integrator based on INS, and measurement-based linear velocity V INS and heading angle ψ I N S can be obtained. Moreover, based on dynamic models, the model-based linear velocity V m and heading angle ψ m also can be calculated. Meanwhile, the event trigger is combined with the Particle Filter (PF) algorithm to accurately estimate the linear velocity V ^ P F and heading angle ψ ^ PF for navigation. Finally, field experiments are conducted and the differential GPS (DGPS) is used as the benchmark for comparison. The results show that our algorithm can improve navigation performance for the low-cost USVs. [ABSTRACT FROM AUTHOR]

Advanced driver assistant systems in vehicles, such as anti-lock brake system, electronic stability program, and traction control, heavily rely on the traction force of the wheels, which is determined by the wheels' longitudinal slip ratio. A common method used to describe this dependency is the well-known Pacejka (magic) formula. The Pacejka (magic) formula suggests the existence of a unique optimal slip ratio, denoted as λ opt , for which the maximum traction force is achieved. Although the λ opt value slightly varies with changes in the tire-to-road interface, most studies tend to neglect the variation of λ opt due to its relatively low impact on the introduced error. Instead, most studies focus on estimating the maximum friction coefficient μ max. In this paper, we address this oversimplification and propose an estimation method for λ opt. This paper introduces two novel approaches for real-time estimation of the slip ratio λ opt based on wheel dynamics. Both approaches were derived using a nonlinear twin-track model implemented in MATLAB & Simulink. The first approach uses recursive least squares, whereas the second approach employs the Unscented Kalman filter algorithm. In addition, a traction force estimator is presented because both estimators rely on either a measurement or an estimation of the traction force. To validate the performance of the proposed estimators, a high-fidelity twin-track vehicle model was initially employed. Finally, real-world experiments are presented in which the proposed algorithms are validated using an RC subscale platform. • Realtime estimation of the wheel optimal slip ratio of vehicles. • Kalman Filter-based estimator for optimal slip ratio estimation. • Recursive least squares-based estimator for optimal slip ratio estimation. • Comparison of both proposed methods on demonstration platform. [ABSTRACT FROM AUTHOR]

• Based on the variable wheelbase characteristics, this study examines the impact of wheelbase changes on the response characteristics of sideslip angle and heading angle relative to the front wheel angle. • A control strategy for adaptive heading tracking is formulated by adjusting the wheelbase under different working conditions. • Considering the influence of noise interference and time delay, a robust active disturbance rejection control (r-ADRC) method is proposed by combining ADRC with a robust term. • A torque distribution method for tire load rate is utilized, and the real-time vertical load of each wheel is calculated, taking into account the change in wheelbase. • The theory of wheelbase-changing and heading tracking strategy are verified through co-simulation of the UGV and experiments of the scaled prototype. In order to fully exploit the variable wheelbase characteristics of the unmanned ground vehicle (UGV) with variable configuration, this paper proposes an adaptive heading tracking control strategy based on wheelbase changes. This strategy involves adjusting the wheelbase according to different working conditions to optimize various driving performance aspects. Firstly, the impact of changing wheelbase on the response characteristics of sideslip angle and heading angle relative to the front-wheel steering angle is analyzed by using a lateral vehicle dynamic model. The configurations where the 2nd axle moves forward by 0.5 m, remains unchanged, and moves backward by 0.5 m are defined as the 'stable configuration', 'balanced configuration', and 'maneuverable configuration', respectively. Considering time lag effects of hydraulic systems and external random noise interference, a robust-active disturbance rejection control (r-ADRC) method is developed by incorporating a robust term into active disturbance rejection control (ADRC) in order to achieve desired front-wheel steering angle. Additionally, a torque distribution method based on the tire load rate and the real-time vertical load of each wheel is applied to ensure uniform tire wear and enhance longitudinal driving stability. Finally, simulations under various typical working conditions as well as scaled prototype experiments are conducted, and both the theory behind the changing wheelbases and the effectiveness of proposed control strategy are verified. According to different vehicle configurations and mission requirements, the maneuverability and stability of the ground unmanned vehicle are ensured respectively. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper investigates the event-triggered model predictive control (MPC) for the series–series (SS) resonant inductive coupling power transfer (ICPT) system in electric vehicles (EVs). Different from most existing literature in ICPT systems, a data-driven modeling approach based on input–output data is proposed to describe the system dynamics and achieve constant voltage output in the presence of load variations. In the traditional MPC control strategies, the optimal control input should be calculated at each time instant to achieve the desired output voltage, which causes great computational burden. To tackle this issue, an event-triggered MPC mechanism is therefore developed to effectively alleviate the computational burden, which will generate the optimal control input only when the norm of the state error exceeds a predefined threshold. The effectiveness and reliability of the proposed event-triggered MPC control strategy are successfully verified by the experimental results. [ABSTRACT FROM AUTHOR]

Sensor fault estimation in an automotive application is crucial for the safe execution and availability of software functions controlling the vehicle. Especially, in situations at the grip limits of the tires the safety of the passengers is most critical and a nonlinear tire model is required for accurate predictions. However, the influence of a nonlinear tire model for sensor fault estimation in an automotive application is not sufficiently analyzed in the literature. In this paper, the influence of a nonlinear tire model on sensor fault estimation during highly dynamic cornering maneuvers is demonstrated. A two-stage Extended Kalman Filter (TSEKF) based on a simple state transformation is used for the estimation of additive and multiplicative faults in longitudinal/lateral acceleration and yaw rate. The proposed estimation algorithm was implemented in real-time on a dSPACE MicroAutoBox II and experimental results with multiple measurements of an Audi S8 (D5) are used for validation of the results. The estimation performance during the measurements, using a linear and a nonlinear tire model, is analyzed and the influence of the state transformation on the accuracy of the filter is investigated. Using a simple nonlinear tire model the state estimation performance under nonlinear operating conditions can be improved by up to 79%, thereby reducing false positive detections significantly. Furthermore, the results show that for the considered system without the use of a state transformation, i.e., neglecting the state-fault cross-covariance, degrades the estimation performance by less than 1%. • Estimation of additive and multiplicative sensor faults for an IMU. • A simple state transformation for two-staged joint state and fault estimation. • Real-time experimental validation at high velocities and lateral accelerations. • Use of a nonlinear tire model improving RMSE up to 79%. • Independent state and fault estimation for reusability with different vehicle models. [ABSTRACT FROM AUTHOR]

In this work, the coupled control of the lateral and the longitudinal dynamics of an autonomous vehicle is addressed. As a first step, a multi-body modeling technique is used to develop a four wheeled vehicle planar model. This technique considers the vehicle as a robot consisting of articulated bodies. The geometric description of the vehicle system is derived using the modified Denavit–Hartenberg parameterization and then the dynamic model of the vehicle is computed by applying a recursive method used in robotics, namely Euler–Lagrange based Algorithm. The validation of the developed vehicle model was then conducted using an automotive simulator, the Scaner-Studio simulator. The developed vehicle model is then used to derive coupled control laws for the lateral and the longitudinal vehicle dynamics. Two coupled controllers are proposed: In the first controller, the control is realized using Lyapunov control techniques while in the second one an Immersion and Invariance with sliding mode approach is used. Both of the controllers aim to ensure a robust tracking of the reference trajectory and the desired speed while taking into account the strong coupling between the lateral and the longitudinal vehicle dynamics. In fact, the coupled controller is a key step for the vehicle safety handling, especially in coupled maneuvers such as lane-change maneuvers, obstacle avoidance maneuvers and combined maneuvers in critical driving situations. The developed controllers were validated in simulation under Matlab/Simulink using experimental data. Subsequently, an experimental validation of the proposed controllers was conducted using a robotized vehicle (Renault-ZOE) present in the Heudiasyc laboratory within the Equipex Robotex project. [ABSTRACT FROM AUTHOR]

Under the constraints of time and the corresponding vehicle location, trajectory tracking control of full self-driving vehicles on ramps requires higher speed to overcome the increase in the driving distance caused by the road slope. Two-dimensional trajectory tracking methods are not applicable to roads with various gradients in reality. Here we discuss a series of studies on tridimensional vector path abstracting and trajectory tracking, collectively designing a network physical model with meaningful nodes for describing real roads and a trajectory tracking controller suitable for ramps. Almost all kinds of paths can be characterized by several simple polynomials, which are then used as the path demand in trajectory tracking control. A vehicle kinematics model considering its vertical location and slope angle attitude is formulated, based on which, model predictive control algorithm is used for the trajectory tracking controller design by regulating the vehicle speed and front wheel steering angle. The developed approach is downloaded into an intelligent control unit and tested in the real world by using a self-driving test vehicle to fully realize the practical application. • A path abstracting method is designed by tridimensional vectors for self-driving. • A network physical model with meaningful nodes is developed for path abstracting. • A tridimensional vehicle kinematics model for trajectory tracking is formulated. [ABSTRACT FROM AUTHOR]

In the heating, ventilation, and air-conditioning (HVAC) system of electric vehicles (EVs), an electric heater is often used for a reheating process that warms up the chilled evaporator outlet air for thermal comfort before it is supplied to the cabin. The usage of the electric heater can significantly reduce the driving range of an EV. Therefore, optimal control of the HVAC system with consideration of the reheating process becomes essential for an increased driving range. In addition, considering the primary role of cabin climate control, it is desirable to intelligently consider the passengers' thermal comfort in climate control design. In this paper, thermal comfort-conscious eco-climate control (TCC-ECC) based on model predictive control (MPC) is proposed to enhance the energy efficiency of the HVAC operation while ensuring passengers' thermal comfort. The MPC design uses a reduced-order HVAC system model based on an ideal vapor-compression cycle. For the integration of thermal comfort into the MPC, a new approach is proposed to obtain an approximate solution of a predictive mean vote (PMV)-based thermal comfort model, which aims to balance computational efficiency and prediction accuracy. With the proposed TCC-ECC, a parametric study is conducted to analyze the impact of weighting factors on energy consumption and thermal comfort under two different thermal load conditions. Then, the performance of the tuned TCC-ECC is evaluated in comparison with a rule-based (RB) controller and the baseline eco-climate control (ECC) without considering thermal comfort. In the performance evaluation, the proposed TCC-ECC demonstrates that with the inclusion of thermal comfort it performs better in terms of energy efficiency and thermal comfort than manually adjusting a target cabin temperature depending on the environmental thermal load. The energy consumption of the proposed TCC-ECC is 22.5% and 35.24% less than that of the RB controller at an ambient temperature of 24 °C and 35 °C, respectively, and 14.5% and 18.5% less than the baseline ECC at the same conditions, respectively. • Eco friendly climate control in an EV should consider both cooling and reheating. • Reheat process in an EV may consume significant energy. • Approximated solution for a PMV-based modified thermal comfort model. • Better energy efficiency by including thermal comfort in MPC-based climate control. [ABSTRACT FROM AUTHOR]

Wheel center loads are usually required to be measured for the durability assessment of vehicle key components, and thus play an important role in the reliability design of vehicles. The current method of obtaining these loads is to install sophisticated and expensive sensors on prototype vehicles to conduct direct measurement. However, it is economically infeasible to equip many prototype vehicles simultaneously for road testing. The paper aims to design and implement edge-computing based soft sensors to estimate these wheel center loads in real time. A set of Finite Impulse Response (FIR) models are trained offline under multiple working conditions, and a Maximum Likelihood Estimation (MLE) classifier is used to determine which working condition the vehicle currently is under so that the wheel center loads can be estimated accurately. The soft sensors are implemented with an edge computing paradigm, and the whole process of data collection, pre-processing, calculation, and upload runs automatically on edge devices. Experimental results show that the estimate from the proposed method fits the measurement well, and the edge-computing based soft sensors meet the real-time requirements for wheel center load estimation. • A data-driven soft sensor is designed and developed to estimate wheel center loads. • An edge computing paradigm is proposed to deploy and manage soft sensors efficiently. • Real road test shows the high performance and efficiency of the soft sensors. [ABSTRACT FROM AUTHOR]

This paper presents the development of a robust distributed and cooperative control algorithm for formation tracking by teams of vehicles modeled as double integrators, and exemplifies its use through the application to multirotor vehicles. To achieve coordination between the vehicles in a distributed manner, in the sense that only local information is either exchanged or perceived, consensus-based protocols are considered. Consensus protocols for agents modeled with up to three integrators are presented, and the third-order protocol is analyzed under any time-invariant sensing or communication topology. The analysis seeks to determine exact bounds on the coupling gains of the protocol that lead to convergence. To that end, an extension of the Routh–Hurwitz criterion to polynomials with complex coefficients is used, leading to novel necessary and sufficient conditions for convergence. Moreover, these novel bounds turn out to be a generalization of the ones described in the literature for second-order consensus, as the latter can be recovered as a special case. The effect of disturbances acting upon the agents is also analyzed and related to their ability to achieve robust consensus in the sense that bounded disturbances do not lead to instability. Further results are derived for the case of constant disturbances, a special case that is particularly relevant for formation control. The third-order consensus protocol is then explored to incorporate integral action in a formation tracking controller used for double integrator vehicles and thereby enable constant disturbance rejection. Finally, experiments resulting from the application of the proposed control algorithms to multirotor vehicles are presented, in order to validate the analysis and demonstrate the usefulness of this approach. [Display omitted] • Constant disturbance rejection is key for distributed formation control. • Third-order consensus is used to introduce integral action to the controller. • Novel necessary and sufficient conditions for convergence are derived. • The Routh–Hurwitz criterion for polynomials with complex coefficients is used. • The usefulness of the proposed controller is validated using multirotors. [ABSTRACT FROM AUTHOR]

In order to achieve better trajectory tracking of intelligent vehicle on different road surfaces, which include underling the weather of rain and snow, an adaptive tire cornering stiffness strategy (ACS) and a trajectory tracking autonomous steering control strategy were proposed in this paper. Firstly, the tire–road friction coefficient estimator was designed to obtain the tire–road friction coefficient based on Bi-LSTM neural network, then combining the tire cornering stiffness coefficient by fuzzy controller to obtain the online tire cornering stiffness. Secondly, considering the uncertainty of tire cornering stiffness, a trajectory tracking upper-level controller based on model predictive control (MPC) algorithm was proposed to calculate the optimal front wheel angle. Thirdly, in order to better adapt to various road conditions, a dynamic equivalent stiffness model is proposed to replace the self-aligning torque model during vehicle steering, and a lower-level controller for pinion gear target position tracking was designed based on the sliding mode control (SMC) algorithm. In addition, this paper also designed a dual-motor control strategy, which cooperates with the pinion gear target position tracking lower-level controller to quickly and accurately achieve the target front wheel angle. Finally, the simulation and HiL test results show that the proposed control algorithm greatly improves the trajectory tracking accuracy and stability of the intelligent vehicle on roads with different tire–road friction coefficients. • An equivalent stiffness model is established to reflect the steering disturbance. • A tire cornering stiffness adaptive method combining Bi-LSTM and fuzzy is developed. • An adaptive control strategy integrating upper MPC and lower SMC is designed. [ABSTRACT FROM AUTHOR]

Dynamic positioning is an important control feature for an underwater remotely operated vehicle. This paper presents a nonlinear dynamic positioning controller suited for application to vehicles with model uncertainties, operating in environments with unpredictable disturbances, such as an aquaculture net cage. The proposed controller combines the backstepping approach with an adaptation term to ensure robustness. Using Lyapunov theory and Matrosov's theorem the origin of the closed-loop system is proven to be: (i) globally asymptotically stable when assuming persistency of excitation, and (ii) stable and bounded, with the true position converging to the desired position if there is no persistency of excitation. This paper also presents results from simulations where the proposed controller is contextualized and compared to similar controllers, showing promising results. Finally, as the main result of the manuscript that demonstrates the effectiveness of the proposed control law, an extensive field trial campaign is conducted at a full-scale aquaculture site using an industrial ROV where the proposed controller is successfully tested under realistic operational conditions. • Backstepping with adaptation is well suited for controlling ROVs in aquaculture. • The unknown parameters can be adapted if the reference is persistently exciting. • Lyapunov theory and Matrosov's theorem show stability and convergence of the states. • Proposed controller is validated in a field trial. [ABSTRACT FROM AUTHOR]

The force transmitted from the front tires to the steering rack of a vehicle, called the rack force, plays an important role in the function of Electric Power Steering (EPS) systems. Estimates of rack force can be used by EPS to reduce driver effort and enhance driving performance. Further, estimates of the components of rack force can be used to perform targeted compensation to enhance steering feel and vehicle safety. Existing rack force estimators either do not support the estimation of rack force components or are only applicable for driving on flat roads. This paper presents three vehicle and tire model-based rack force estimators that can estimate individual components of rack force as well as the total rack force for driving on uneven roads. The estimators use the same vehicle model but use tire models of differing complexities. The performance of the estimators was validated and compared by performing driving experiments with non-aggressive and aggressive steering maneuvers on roads with low and high frequency profile variations. The results show that the estimation accuracy increases with the complexity of tire model used, especially during aggressive steering maneuvers and high frequency road profile variations. Finally, results from a simulation study are presented to show that the estimators are also capable of estimating the components of rack force. • Developed three rack force estimators with different tire models for uneven roads. • The estimators with nonlinear tire models performed better for aggressive steering. • The estimator with rigid ring tire model performed better for driving on road cleats. • Estimators could successfully decompose rack force into individual components. • Estimates of rack force and its components can be used to improve steering feel. [ABSTRACT FROM AUTHOR]

This paper presents a composite adaptive backstepping robust tracking controller which aims to improve the lateral dynamics stability for an electric vehicle (EV) while considering the uncertain parameters and external disturbances. First, a nonlinear three degree-of-freedom vehicle model is built up to describe the lateral and roll dynamics behaviors of this EV. Then, the projection adaptive control law is developed by combining adaptive backstepping and dynamic surface control techniques, which is expected to make the sideslip angle and the yaw rate approaching the ideal ones as closely as possible. The sufficient conditions for designing such a controller are also derived in the framework of Lyapunov theory to guarantee its asymptotical stability, thus the desirable controller design is devised. Finally, a comparative CarSim–Simulink joint simulation is carried out to verify the effectiveness of the proposed controller under single-lane-change and J-turn maneuvers. • Based on an established 3-DOF dynamics model, a desirable ABRTC is proposed for stabilizing lateral dynamics of EVs in the presence of uncertain parameters and external disturbances. • The DSC technology is employed to estimate the derivatives of the virtual control law, meanwhile a robust feedback term of yaw moment is included. Then, the big-bang complexity can be avoided. • By employing the designed virtual control inputs, a projection-based control law is derived, and the stability analysis of the proposed ABRTC is analyzed. [ABSTRACT FROM AUTHOR]

This research seeks to study the path following issue for distributed drive autonomous electric vehicles (DDAEV). In order to improve the tracking accuracy and vehicle stability, coordinated control of tracking deviation compensation and vehicle stability by yaw-moment is proposed. Based on the three degree-of-freedom vehicle model, model predictive control is used to calculate the steering angle of front wheels to control the vehicle along the reference path by autonomous steering. In this process, two sliding surfaces whose control targets are tracking deviation and yaw rate deviation are first set, then two sliding surfaces are combined into one coordinated sliding surface with weight coefficients considering the vehicle stability, and finally the yaw-moment is figured out and distributed with the constant torque requirement of the vehicle. Carsim co-simulation with Simulink and Hardware-in-the-Loop experiment are conducted, which shows that compared to autonomous steering control, coordinated control achieves better path following accuracy and vehicle stability for DDAEV under different driving conditions. • A linear time-varying model predictive control algorithm for the path following is proposed. • Coordinated control is presented for improving tracking accuracy and vehicle stability. • Simulation and Hardware-in-the-Loop experiment are carried out to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article presents an innovative control approach for autonomous racing vehicles. Linear Parameter Varying (LPV) theory is used to model the dynamics of the vehicle and implement an LPV-Model Predictive Controller (LPV-MPC) that can be computed online with reduced computational cost. The optimal time problem is solved by an optimal off-line trajectory planner that calculates the best trajectory under the constraints of the circuit. An identification of the system model based on optimization is also carried out. The planning and control scheme is validated in simulation and experimentally in a real platform where the effectiveness of the proposed LPV-MPC is demonstrated. [ABSTRACT FROM AUTHOR]

The paper presents a survey of the existing mathematical models of electric vehicles followed by state of the art approaches on modeling hybridization of the energy sources. The mathematical representation of the electric vehicle (EV) ranging from the simple single Degree of Freedom (DOF) models to complex multi-body dynamic models is discussed in detail. Reduced dynamic models applicable to various control design diligence are discussed which facilitates the selection of the optimal model for design. In addition to vehicle dynamics, the paper consolidates dynamic models of the different components of an electric vehicle including the transmission, brake, battery, wheel and tire dynamics. Comparative analysis of different versions of the models for each component is also presented, focusing on their application in controller design. The paper thus acts as a guide for any EV control design requirement, providing optimal models for particular applications. • The paper, first of its kind, summaries existing EV models facilitating control design. • Several vehicular models presented, varying in complexity from bicycle to multibody model. • Various models of each EV physical component compiled together to ease model selection. • A comparative analysis of existing battery & fuel cell models with model description. • The paper can usher EV control designers with apt models that fit design requirements. [ABSTRACT FROM AUTHOR]

The power-split hybrid vehicles are widely used owing to its advantages of high efficiency and low emission. With the increasing of the requirements of the customer's satisfaction nowadays, vibration control is getting progressively more attention. Since the transmission system components are elastic to some extent, meantime the torque of the motor responds quickly for a sudden acceleration, hence torsional vibration takes place, henceforth the passengers will suffer inconvenience attributable to this vibration. This paper studies the active control algorithm for the torsional vibration of the power-split hybrid powertrains under rapid acceleration and deceleration circumstances. Primarily, a dynamic modeling of the power-split hybrid vehicle is built, in addition the uncertainty caused by the variable transmission ratio is analyzed. Moreover, this paper proposes a method combining the model reference adaptive control and the pole configuration method to solve the torsional vibration active control problem with uncertainties. The reference model is constructed by pole configuration method. Based on the dynamic characteristics of the reference model, the model reference adaptive control is implemented and the torque ripple reduction of output shaft under the impact conditions is achieved. Furthermore, this paper designs a dynamic coordinated control algorithm combined with active vibration control strategy, which ensures the dynamic performance and comfort performance of the vehicle. Finally, the effectiveness of this active vibration control strategy under cyclic conditions is verified using Simulink simulation and hardware-in-loop simulation. • A dynamic model of hybrid vehicles that shows the uncertainty of system is proposed. • An active vibration control algorithm is proposed to active damping the vibration. • A dynamic coordinated control strategy is proposed to ensure the dynamic performance. [ABSTRACT FROM AUTHOR]