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This article proposes a nonlinear observer for estimating a powertrain transmitted torque of vehicles equipped with dual-clutch transmissions. A new dynamical model of vehicle driveline is presented. It introduces a single characteristic state based on the dynamic friction model to scale the clutch torque capacity, which is available from the actuator position-to-torque map. Based on this model, the generated torque on the clutch disk can be effectively estimated in real time, and the sensitivity of the estimated value caused by high gain feedback can be reduced in the observer. Also, the engine torque uncertainty is estimated from the integrated driveline model. The Lyapunov-based analysis is presented to prove the convergence of the proposed observer. The effectiveness of the proposed method is shown by experimental validations on a test vehicle that allows for real measurements of the input shaft torque of transmission. [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]

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 proposes a novel gain-scheduling control method to improve vehicle lateral stability based on a variable-parameter-dependent approach. A time-varying velocity-dependent model to describe the vehicle lateral dynamic characteristics is constructed under cornering stiffness uncertainties and controller saturation. A novel Lyapunov function with variable-parameter-dependent Lyapunov matrix is established to develop the conditions. Using the proposed Lyapunov function, a general condition of the variable-parameter-dependent saturated robust yaw moment controller is provided in terms of time-varying parameter-dependent matrix inequalities. In order to obtain a tractable solution, a condition is further developed with finite linear matrix inequalities. Moreover, an optimal distribution method is adopted to generate the desired yaw moment based on torque allocation. These torques are computed by optimizing the distribution errors and tire workloads. Simulations results under both J-turn and double-lane changing scenarios are used to illustrate the merits of the proposed method. The control synthesis approach is also applicable to other applications involving time-varying parameters. [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]

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]

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]

This article suggests a model-based controller for an all-wheel drive (AWD) vehicle using a novel vehicle dynamics model. Recently, an active type of AWD system that automatically controls the clutch engagement force of the transfer case has become popular. However, its performance has been limited by its rule-based controller, which necessitates the development of a model-based controller. Although the bicycle model has been most widely adopted for upper level controller of vehicle control systems, it is not appropriate for AWD controllers because the model does not have a direct relationship between the states and control inputs. In this sense, this study adopted a tire force-based full car model for the AWD controller. The clutch engagement force limit, which varies depending on the current engagement state, was calculated systematically by considering changeable dynamic behavior that is inherent in AWD vehicle and was used as a valid value for input limit in linear quadratic regulator (LQR) control algorithm. For the real-time application of the designed controller, only data obtained from the controller area network (CAN) of production vehicles were used. Then, the performance of the proposed controller was validated through vehicle experiments that included scenarios of both longitudinal and lateral movements. [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]

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]

Accurate physical modeling of vehicle dynamics requires extensive a priori knowledge of the studied vehicle. In contrast, data-driven modeling approaches require only a set of data that are a good account of the vehicle’s driving envelope. In this brief, we compare, for the first time, the prediction capabilities of both approaches applied to a large-scale real-world driving data set. The data set contains several cornering maneuvers, acceleration, and deceleration stages and was collected over public roads. Linear and nonlinear physical models were identified through nonlinear optimization of their unknown parameters. Closed-form subspace identification methods were used to initialize the estimate of a linear state-space model, and the initialization was then refined through nonlinear optimization. The optimized models were validated against 59 km of independent driving data. The model fits, in the longitudinal velocity, were 68.9% versus 80.2% for the nonlinear physical model and linear data-driven (second-order) model, respectively, and, in the yaw rate, 43.0% versus 63.5%. These results show that, for this vehicle, a simple linear data-driven model outperformed both linear and nonlinear physical models under real-world driving conditions. This has important implications for control design approaches in autonomous vehicles. [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 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]

This paper deals with the design, improvement, and implementation of a nonlinear control strategy to solve the trajectory tracking problem for an autonomous underwater vehicle under model uncertainties and external disturbances. First, a disturbance observer based on high-order sliding mode control is designed to counteract the negative impact of both parametric uncertainties and bounded external disturbances. Then, the nonlinear control is enhanced through injecting the disturbance estimation into the designed controller. The stability of the closed-loop system with the enhanced proposed nonlinear controller is proven by Lyapunov arguments. Finally, real-time experimental results are also provided to demonstrate the effectiveness of the proposed controller. [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]

This paper provides a comparative assessment of three economic optimal control strategies, aimed at minimizing the fuel consumption of heavy-duty trucks in a highway environment, under a representative lead vehicle model informed by traffic data. These strategies fuse a global, off-line dynamic programming (DP) optimization with online model predictive control (MPC). We then show how two of the three strategies can be adapted to accommodate the presence of traffic and optimally navigate signalized intersections using infrastructure-to-vehicular (I2V) communication. The MPC optimization, which is local in nature, makes refinements to a coarsely (but globally, subject to grid resolution) optimized target velocity profile from the DP optimization. The three candidate economic MPC formulations that are evaluated include a nonlinear time-based formulation that directly penalizes the predicted fuel consumption, a nonlinear time-based formulation that penalizes the braking effort as a surrogate for fuel consumption, and a linear distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity profile obtained from DP. Using a medium-fidelity Simulink model, based on a Volvo truck’s longitudinal and engine dynamics, we analyze the optimization’s performance on four highway routes under various traffic scenarios. Results demonstrate 3.7%–8.3% fuel economy improvement on highway routes without traffic and 6.5%–10% on the same routes with traffic included. Furthermore, we present a detailed analysis of energy usage by “type” (aerodynamic losses, braking losses, and comparison of brake-specific fuel consumption), under each candidate control strategy. [ABSTRACT FROM AUTHOR]

We present a switched adaptive observer for the estimation of the tire extended braking stiffness (XBS) during ABS braking scenarios. The design of the observer is based on Burckhardt’s friction model and on a model of the wheel dynamics in which the XBS appears as one of the state variables. Global asymptotic stability of the estimation error is established, provided that certain dwell time and persistency of excitation conditions hold. The approach is validated under constant road conditions with experimental data from a tire-in-the-loop test rig and under varying road conditions via numerical simulations on a two-axle vehicle model that includes dynamic load transfer and tire relaxation length. [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]

In the design of hybrid unmanned aerial and underwater vehicles, buoyancy management and weight are two major factors. Large wing volumes used by unmanned air vehicles to fly efficiently drive vehicle buoyancy up, preventing them from submerging. Heavy active buoyancy control systems can overcome this, but cost weight, energy, and time to transition between underwater operation and flight. An alternative design, consisting of a passively flooding and draining wing, is presented in this paper. Relevant dynamic parameters for a full vehicle dynamic model are identified. A dynamic model of a draining structure is developed and verified experimentally on both a simple cylinder and a full wing structure. With proper tuning, the model captures the salient dynamic behavior of passive draining during vehicle egress. A prototype unmanned aerial and underwater vehicle was built, flown, and used to collect flight test data. The model is used to accurately predict the takeoff performance of the vehicle. As given, the model can be incorporated into a full vehicle dynamic model to aid in the design, simulation, and control of hybrid unmanned aerial and underwater vehicles with passively draining components. [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]

In this paper, we propose a Lagrangian mechanics framework for modeling the motion of a submerged vehicle operating near a free surface in calm water. By augmenting the system Lagrangian used to derive Kirchhoff's equations for a rigid body moving in an unbounded fluid, we directly incorporate the free surface into the derivation of the equations of motion. This is done using a free surface Lagrangian, which accounts for the energy stored within the free surface due to the motion of a submerged vehicle. The system Lagrangian then enables us to derive the six-degrees-of-freedom nonlinear equations of motion using the Euler–Lagrange equations. The model structure is similar to standard maneuvering models for surface ships, though additional complexities are present since the hydrodynamic parameters are shown to depend on the vessel position and orientation relative to the free surface. We also examine a fundamental tradeoff between model simplicity and model fidelity: we choose to model only the instantaneous fluid response and neglect the hydrodynamic memory effects. Without the memory effects, the proposed model does not incorporate wave-resistance forces. However, the simplified representation of the hydrodynamics does enable a control-oriented representation of the nonlinear radiation forces and moments commonly associated with added mass and free surface suction effects. [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]

This paper focuses on the controller design for path-tracking problem of autonomous ground vehicles (AGVs) by employing a multi-constraint nonlinear predictive control (NMPC) schema. It is aimed to improve the transient performance of the vehicle and to consider a rollover prevention criterion in the proposed method. The path-tracking problem is transformed into the yaw stabilization issue, and a feedback control law with input saturation is developed to decrease the steady-state errors. Furthermore, the yaw-rate signal is generated for the desired path-tracking performance. The major contributions of the present paper are, first, developing a neural network autoregressive with exogenous input system to assist in obtaining an accurate and explicit model in order to contribute to the control of the system over the prediction horizon; second, describing a Frenet–Serret differential geometry based path-following agenda and developing AGV dynamic model by incorporating the vehicle vertical mode of motion to prevent vehicle rollover during critical maneuvers, and finally, achieving an enhanced yaw stabilization and transient tracking performance considering saturated input signal by employing the proposed system identification algorithm. The effectiveness of the proposed control system is verified by comparing with the traditional NMPC method by employing a high fidelity CarSim/MATLAB framework. [ABSTRACT FROM AUTHOR]

Cooperative adaptive cruise control (CACC) employs intervehicle wireless communications to realize short intervehicle distances and, hence, to improve road throughput. However, the vehicle actuator delay and communication delay have a significant effect on the (string) stability properties. Therefore, a Smith predictor has been applied to compensate for the vehicle actuator delay, while utilizing a proportional–derivative controller and a constant time gap spacing policy. The control actuation is conducted on a delay-free vehicle model to follow a preceding vehicle with a desired distance, such that the resulting scheme leads to individual vehicle stability independent of the vehicle actuator delay. Moreover, this approach allows for a smaller minimum string-stable time gap compared to without the compensator, thus taking full advantage of CACC. In addition, the Smith predictor has been adapted to take acceleration disturbances into account. The results are experimentally validated using a platoon of two passenger vehicles, illustrating the practical feasibility of this approach. [ABSTRACT FROM AUTHOR]

The platooning of autonomous vehicles can significantly benefit road traffic. Most previous studies on platoon control have only focused on specific communication topologies, especially those with real eigenvalues. This paper extends existing studies on distributed platoon control to more generic topologies with complex eigenvalues, including both internal stability analysis and linear controller synthesis. Linear platoon dynamics are derived using an inverse vehicle model compensation, and graph theory is employed to model the communication topology, leading to an integrated high-dimension linear model of the closed-loop platoon dynamics. Using the similarity transformation, a sufficient and necessary condition is derived for the internal stability, which is completely defined in real number field. Then, we propose a Riccati inequality based algorithm to calculate the feasible static control gain. Further, disturbance propagation is formulated as an $\text {H}_{\infty }$ performance, and the upper bound of spacing errors is explicitly derived using Lyapunov analysis. Numerical simulations with a nonlinear vehicle model validate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel multirate lane-keeping system (LKS) is introduced by using a kinematic-based model considering a look-ahead distance. First, we developed a kinematic lateral motion model for LKS with respect to the road. To increase a system damping of vehicle, the unique look-ahead output measurement matrix was introduced. The decentralized multirate lane-keeping control scheme with a multirate Kalman filter was also applied to resolve the asynchronous and irregular sampling time of multi sensors in autonomous vehicles. We validated the performance of the proposed kinematic-based multirate LKS using the dynamic vehicle solver CarSim, MATLAB/Simulink, and electric power steering hardware-in-the-loop system. Experimental validation was also conducted using a real test vehicle. Both simulation and experimental results showed that the proposed kinematic-based LKS performed lane-keeping capability comparable to that of dynamic-based LKS. In particular, the experimental results showed that the proposed method, compared to a human driver, can also maintain the lane within reasonable specifications. The experimental validation was performed at vehicle speeds of up to 120 km/h. [ABSTRACT FROM AUTHOR]

This paper presents an integrated parameter estimator and trajectory tracking controller for a hovercraft. A generic parameter estimator for time-varying systems linear in the parameters is derived and then particularized for the dynamic model of the vehicle at hand. A trajectory tracking controller is proposed for the nonholonomic hovercraft, which renders the tracking error system exponentially stable and its zero dynamics stable. The interconnection of the estimator and the controller is proven to be locally asymptotically stable. Experimental results attesting the performance and robustness of the controller and its interconnection with the estimator are presented. [ABSTRACT FROM AUTHOR]