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Aiming at the low-frequency oscillation (LFO) phenomenon in high-speed railways, this paper proposes a feedback linearized virtual inertia control (VIC) strategy of the traction converter based on a sliding mode observer (SMO) to improve the robustness of the system and suppress the occurrence of LFO. First, to optimize the total harmonic distortion of the traction grid-side current, the inner loop controller is designed as the feedback linearization control. Then, the voltage outer loop is designed as a combination of the VIC designed in this paper and SMO, which enhances the system inertia and reduces the impact of external interference on the system, while reducing the controller's demand for electrical power on the DC-side. Furthermore, a small-signal model is established to analyze the stability of the single-phase vehicle-grid system under the proposed control. The influence of the control parameters on stability is discussed in detail. Finally, the comparison of software simulation and hardware-in-the-loop experimental results between traditional PI control, feedback linearization control, feedback+SMO, feedback+VIC and the proposed control method shows that the proposed control has better dynamic and static characteristics, and can effectively inhibit the occurrence of LFO. [ABSTRACT FROM AUTHOR]

This paper proposed a method for estimating the suspension relative velocity using a single six-dimensional measurement unit attached to a vehicle body. This method can reduce the number of sensors, reduce costs, and address sensor packaging issues. A wheelbase preview assumption is used to overcome the lack of unsprung mass information. The proposed estimation method is verified by the vehicle simulator Carsim and the stability of the estimation method is also analyzed. In addition, the frequency response and the time response of the sky-hook controller based on the estimated relative velocity are compared with the sky-hook controller based on the actual relative velocity. The results show that the proposed estimator produces very robust performance on various road conditions. [ABSTRACT FROM AUTHOR]

This paper presents a multi-stage series model predictive control (MS-MPC) for PMSM drives in order to further improve the control performance of MPC. Firstly, the basic principle of conventional MPC is introduced. Then, the multi-stage series MPC is proposed in this paper, in which the cost functions of different prediction instant are adopted to form series control structure. In this method, the current prediction trajectory of one voltage vector is extrapolated to multiple control periods, and the control performances of every voltage vector during multiple control periods are evaluated by multi-stage series cost functions. Additionally, the implementation process of the proposed method is described. Finally, the comparison experiments between the conventional MPC and the proposed method show that the proposed method can effectively reduce the switching frequency under the same control frequency. In addition, the improvement of current control performance can be achieved by the proposed method under the condition of the same switching frequency. [ABSTRACT FROM AUTHOR]

The dynamic interaction of vehicles/bridges is an important part of the research field of the Electromagnet Suspension (EMS) maglev train. However, multiple factors affect the dynamics of the coupling system, which increases the complexity of the vehicle-bridge coupling system, and makes it difficult to analyze. Herein, combining the advantages of theoretical derivation and numerical calculation, a fast and accurate analysis method of maglev vehicle and bridge system is proposed to analyze the influence of bridge and controller on the dynamics of the maglev system. This work is directed to the Changsha maglev express line, in which the mathematical equations for the vehicle- bridge system is presented. Then the influence of the parameter on the system is analyzed. Finally, the vibration experimental test system is set up, and the vibration test of the maglev vehicle bridge is tested by the test scheme, and the experimental results data are given. The test results of vibration acceleration are in good agreement with the calculation results of the proposed method, which verifies the accuracy of the model and the analysis method. Therefore, the research in this paper can provide theoretical guidance for Maglev engineering, and the analysis method in this paper can also be used in the study of other vehicles, such as the high-speed maglev train. [ABSTRACT FROM AUTHOR]

Autonomous vehicles will generate data from the variety of sensors they employ to track the surrounding environment. This data is inherently valuable, as it gives algorithm designers the potential to leverage prior experience in order to improve driving performance over time. This paper uses the lens of autonomous racing to provide an example of how data from previous iterations of driving can be used to improve quantitative metrics of performance. Two complementary algorithms are demonstrated in this paper. The first algorithm uses iterative learning control (ILC) to simultaneously improve lateral and longitudinal tracking of the desired racing trajectory over multiple laps, while the second algorithm is focused on altering the trajectory itself using a search method. When driven experimentally at the limits of handling, the result is a reduction in lap time of nearly 1.4 seconds, a major improvement. [ABSTRACT FROM AUTHOR]

Characterization and linearization of RF power amplifiers (PAs) are key issues of fifth-generation wireless communication systems, especially when high peak-to-average ratio waveforms are introduced. Recently, deep learning methods have achieved great success in numerous domains including wireless physical-layer. However, there has been limited work in using deep learning for PAs behavioral modeling and linearization. In this paper, we make a bridge between memory effects of the nonlinear PAs and memory of bidirectional long short-term memory (BiLSTM) neural networks. We then build a BiLSTM-based behavioral modeling architecture and its accompanying digital predistortion (DPD) model by reconciling a non causality concern. Next, an additional model is proposed in this paper to mitigate uncertainty of the tested PA when transforming phases. The experimental results demonstrate the effectiveness of the proposed scheme, in which the adequately trained networks are capable of characterizing the PA, and the artificial intelligence-based DPD shows promising linearization performance when considering the tested PAs inherent unpredictability. [ABSTRACT FROM AUTHOR]

How to reasonably coordinate and control the torque of four wheels to enhance vehicle performance is an important research problem of the four in-wheel-motor independent-drive electric vehicle (4MIDEV). In this paper, a coordinated hierarchical control strategy for the dynamic wheel torque of 4MIDEV steering is proposed, which considers both safety and energy-saving performance. The innovation of this paper lies in two new valuable algorithms in the top and bottom level controller, respectively. Besides the optimal control method on the basis of the 2-degree of freedom (DoF) ideal model in the top level controller, a novel model predictive control method based on the 7-DoF dynamic model is put forward to obtain two virtual generalized forces required during vehicle driving. In the bottom level controller, a novel optimal allocation (OA) algorithm is brought forward to improve the safety and energy-saving performance. The tire stability margin and the electric power of driving system balancing the driving energy consumption and regenerative braking energy recovery are considered in the quadratic objective function of OA algorithm at the same time. Carsim-Simulink joint simulation results under open-loop and closed-loop steering conditions illustrate that, compared with the 2-DoF LQR control method, the 7-DoF MPC method could effectively improve vehicle handling stability and safety. The bottom optimal allocation algorithm can not only improve the vehicle handling stability, but also make the driving system more energy-saving. [ABSTRACT FROM AUTHOR]

Nowadays, the availability of accurate vehicle position becomes more and more indispensable. The GNSS/INS (Global Navigation Satellite Systems/Inertial Navigation System) is currently the most widely-used integrated navigation scheme for land vehicles, which is capable of provide high-accuracy and continuous positioning results in the open-sky environments. However, under the GNSS-denied conditions, the existing GNSS/INS integrated system often fails to provide reliable positioning results due to various and nonlinear errors contained in the MEMS (Micro-Electro-Mechanical System) IMU (Inertial Measurement Unit) measurements. To improve the positioning accuracy during GNSS outage, deep learning has been introduced into the GNSS/INS integrated system in recent years. In this paper, we propose a residual attention network-based confidence (i.e., measurement noise covariance) estimation algorithm for non-holonomic constraint in GNSS/INS integrated navigation system, which adopts a residual attention network to dynamically estimate the noise covariance of the pseudo-observation (i.e., non-holonomic constraint) for optimal Kalman filtering (KF) fusion. To emphasize the more representative features with larger weights for accurate noise covariance estimation, we introduce an attention mechanism to automatically assign proper weights to the learned features according to their contributions. We evaluate our proposed method on three practical road datasets and compare it with other seven methods including the traditional KF, Pure INS, KF with three deep learning networks, K-means, and the Input-Delayed Neural Networks based method. Extensive experimental results demonstrate that our proposed RA-NHC bounds the errors associated with velocities and achieves reasonable accuracy improvement in position and velocity estimation. [ABSTRACT FROM AUTHOR]

Path tracking is one of the most crucial issues of autonomous vehicles, but uncertain factors including road roughness and wind gusts could cause lateral deviation and even divergence of the control system. Therefore, this paper proposes a hierarchical anti-disturbance tracking architecture based on the steer-by-wire (SBW) system to improve tracking accuracy and dynamic stability for autonomous vehicles. The proposed architecture is robust strongly against different types of disturbances in the path tracking process by means of hierarchical decoupling. Firstly, in the upper-layer path tracking module, a robust model predictive control (MPC) method with a feedback correction term calculates the expected front-wheel steering angle. Secondly, in the lower-layer SBW module, a nonlinear high gain rack force observer is proposed to compensate for the generalized rack force disturbance. Then, an improved non-singular fast terminal sliding mode auxiliary control law based on the super-twisting algorithm is presented to attenuate the norm-bounded rack disturbance effectively. Simulation and experiment results show that the proposed control architecture can improve the path tracking accuracy and has strong interference resistance and robustness against both modeled and unmodeled disturbances. [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]

Recent improvements in vehicle-to-everything (V2X) communication and onboard computing power have enabled the development of control algorithms that jointly optimize the vehicle velocity and powertrain control in Connected and Automated Vehicles (CAVs), commonly referred to as the Eco-Driving problem. This paper presents a novel and computationally efficient algorithm to optimize the velocity planning and energy management in a CAV with a hybrid electric powertrain. The Eco-Driving problem is formulated as a dynamic, constrained optimization problem in the spatial domain, where information about the upcoming speed limits and road topography is assumed known. This problem is solved by embedding an Equivalent Consumption Minimization Strategy (ECMS) into a Dynamic Programming (DP) optimization to obtain a sub-optimal solution that provides results close to the global optimum at a fraction of the computational cost. Further, a multi-layer hierarchical control architecture is proposed as a path to a causal, real-time implementation. The DP-ECMS algorithm is converted into a Model Predictive Control (MPC) framework by using principles of Approximate Dynamic Programming (ADP). This causal implementation is finally benchmarked to a global optimal solution obtained with DP for different scenarios. [ABSTRACT FROM AUTHOR]

While bringing convenience to people, the growing number of vehicles already on roads causes inevitable traffic congestion. Traffic congestion can happen for both observable reasons, such as accidents, or occur naturally due to human driving style in high vehicles density scenarios. In the latter case, traffic congestion are referred to as phantom jams. To alleviate the traffic instability caused by phantom jams, bilateral control models have been proposed with the development of intelligent transportation system (ITS) in ideal situations. However, in road scenarios, some factors that severely affect the performance of existing models cannot be ignored, such as uncertainties of vehicle state measurements caused by on-board sensors, time delay caused by intervehicle communications and control systems of vehicles. In this paper, a novel predictable bilateral control model (PBCM) with local wireless communication, which consists of best estimation and state prediction, is proposed to accurately control the host vehicle in traffic flow to alleviate traffic instability. Compared with other models, theoretical analysis and simulation results show that more accurate control decisions could be obtained with the PBCM, which could distribute all vehicles more evenly, and our model could maintain higher velocities when disturbances occurred. The experimental results also reveal that our proposed model could more effectively suppress traffic instability (i.e., the improvement in control decision $a$ is more than 10%) and could restore traffic balance 20% faster than other models on average. [ABSTRACT FROM AUTHOR]

Energy feedback systems (EFSs) have been widely applied in urban rail transit, and a power flow algorithm and energy-saving evaluation of urban rail power supply systems with EFSs are studied in this paper. First, trains, the reflux system, and traction substations with EFSs are modeled, and the multimode transitions are analyzed, including the maximum power state of the EFS. Then, a modified AC/DC unified power flow algorithm for the DC power supply system, which simplified the DC reflux system is proposed, and a field case study based on a multiple train scenario is presented to validate the algorithm. Furthermore, system-level energy-saving evaluation indexes are applied according to system power flow analysis. When the headway time decreases, the proposed indexes decrease in general mainly as there is more regenerative braking energy absorbed by adjacent trains. The higher the load rates of the step-down substations are, the more daily feedback energy is utilized. When the start voltage of the EFS ranges from 1650 V to 1770 V, the maximum energy-saving rate reaches 11.9%, which occurs when the start voltage is 1670 V. When the no-load voltage is relatively high, the start voltage should not be higher than 1750 V to keep the system running in high efficiency. These results can provide advice for the design and operation of traction power supply systems with EFSs. [ABSTRACT FROM AUTHOR]

In the advanced driver assistance system (ADAS) and autonomous driving systems, accurate information on preceding vehicle motion states is vital for the path planning and control. To develop those intelligent driving systems, a modular integrated estimation algorithm for the preceding vehicle longitudinal and lateral states is proposed in this paper, the coupled nonlinear characteristics of vehicle dynamics are applied to improve state estimation accuracy. First, considering driver aggressiveness, a linear moving horizon estimator for the vehicle longitudinal speed is designed based on the car following model. Then, the estimated vehicle longitudinal speed is delivered to the lateral estimator module in real-time. For the arbitrary driving routes, the Serret-Frenet equations and nonlinear lateral dynamics are combined to describe the lateral motion of preceding vehicle. Furthermore, a nonlinear moving horizon estimator is constructed to estimate vehicle lateral states accurately. To reduce the computational burden, the multiple shooting (MS) method is introduced, then the objective function is divided into several segments to improve the strong nonlinearity produced by multiple iterations. The proposed estimation algorithm can effectively handle the initial biases, the additive biases and model error, which are common in the acquired signals, and provide an accurate estimate of preceding vehicle states. [ABSTRACT FROM AUTHOR]

In heavy-duty vehicles, multiple signals are available to estimate the vehicle's kinematics, such as Inertial Measurement Unit (IMU), Global Positioning System (GPS) and linear and angular speed readings from wheel tachometers on the internal Controller Area Network (CAN). These signals have different noise variance, bandwidth and sampling rate (being the latter, possibly, irregular). In this paper we present a non-linear sensor fusion algorithm allowing asynchronous sampling and non-causal smoothing. It is applied to achieve accuracy improvements when incorporating odometry measurements from CAN bus to standard GPS+IMU kinematic estimation, as well as the robustness against missing data. Our results show that this asynchronous multi-sensor (GPS+IMU+CAN-based odometry) fusion is advantageous in low-speed manoeuvres, improving accuracy and robustness to missing data, thanks to non-causal filtering. The proposed algorithm is based on Extended Kalman Filter and Smoother, with exponential discretization of continuous-time stochastic differential equations, in order to process measurements at arbitrary time instants; it can provide data to subsequent processing steps at arbitrary time instants, not necessarily coincident with the original measurement ones. Given the extra information available in the smoothing case, its estimation performance is less sensitive to the noise-variance parameter setting, compared to causal filtering. Working Matlab code is provided at the end of this work. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of improving vehicle stability using an electronic limited slip differential (ELSD). Unlike brake-based vehicle stability control systems, electronic limited slip differentials have the potential to control a vehicle's yaw stability without slowing down its speed. In this study, first, an ELSD model is designed to properly predict ELSD torque distribution. The model's accuracy is evaluated using experimental test data. Then, an intelligent ELSD control design is proposed using the developed ELSD model, capable of working in both off-throttle and on-throttle scenarios. The proposed design, which is based on a model predictive control approach, offers control of the ELSD differential by directly controlling the ELSD clutch pressure. This control design enables the controller to prevent unwanted oversteering yaw moments and avoid chattering in the ELSD clutch. The controller is implemented in a Cadillac CTS vehicle to evaluate its performance experimentally in various driving maneuvers. [ABSTRACT FROM AUTHOR]

The benefits of intermediate couplers (IC) in inductive power transfer (IPT) systems are well documented in the literature. However, the lack of guideline rules makes the selection process of the passive components and operation mode difficult. This paper presents an iterative design methodology based on the equivalent mathematical model of the system to determine the optimal magnetic coupler (MC) dimensions and resonant tanks parameters that operates the system in a quasi constant current mode. It was found that the transconductance function of a series-series (SS) with an intermediate coupler exhibits a quasi-load independent characteristic when the reflected reactance from the receiver side is null. The design methodology then combines the self and mutual coupling profiles of the MC, obtained from a finite element analysis (FEA) tool, with the system specifications and constraints to find all possible solutions. A set of selection metrics are proposed to identify the optimal solution. This work also presents an identification method of the load and mutual inductances based only in the information available from the transmitter side. The proposed method uses the steady-state equations and does not require any form of communication to the receiver side. An experimental prototype was built and assembled in a converted combustion vehicle with efficiency of 89 $\%$. [ABSTRACT FROM AUTHOR]

This paper presents the modeling and analysis of a novel priority valve controlled open-loop Cable Reeling Drum (CRD) drive of Load Haul Dump (LHD) vehicle. The LHD vehicle is basically a trackless loading machine having wide applications in underground mining operations. The CRD incorporated in the said machine performs the reeling/unreeling operation of the flexible trailing cables that supply electric power to the machine. In this respect, a novel hydraulic circuit is proposed that incorporates a priority valve for the operation of the hydro motor driving the CRD, unlike the existing LHD machines having a separate hydraulic arrangement for the CRD operation. In the present study, the flow through the bypass port of the priority flow divider (PFD) valve is utilized for driving the CRD. In analyzing the proposed hydraulic system, the detailed model is analytically solved in MATLAB/Simulink software, and the simulation results are validated experimentally. The effects of change of the critical parameters on the system's dynamic responses are analyzed using the validated simulation model. The investigations show that till the fluid flow metered to the PFD valve is higher than the priority flow demand, the machine's priority function is not affected by the operation of the CRD. However, with the actuation of the priority operation, the hydro-motor drive speed of the CRD decreases. The model proposed in this study may be considered as a basis for the re-design of the hydraulic arrangement of CRD drive incorporated in an LHD machine for its improved performance. [ABSTRACT FROM AUTHOR]

Dynamic battery modeling uncertainties, even if low, may lead to significant performance degradation or even divergence of the state-of-charge estimation algorithm. This paper investigates the integration of the extended-Kalman-filter with the smooth-variable-structure-filter algorithms for state-of-charge estimation of lithium-ion batteries. The robustness of the presented approach to modeling uncertainty is assessed for batteries operating in highly dynamic environments. The presented approach combines the benefit of the smooth- variable-structure-filter in its robustness to model uncertainty with the benefit of the extended-Kalman-filter in its near-optimality for a given dynamics and measurement noise sequences. The algorithm is rigorously tested using various datasets including standardrized and artificial drive cycles with added dynamics. The drive cycle power profile is calculated for an electric Ford F150 truck and scaled for the 18650PF cell used in the tests. Experimental validation is performed by investigating four different scenarios in which knowledge of the initial conditions as well as accuracy of the battery model were varied. The results demonstrate a substantially enhanced estimation accuracy achieved by the adopted approach through its optimality to measurement and model noise as well as its robustness to model uncertainty. The adopted approach results in a reduction in the complexity of the state-of-charge control algorithm and therefore enhances the battery management system. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) or drone, is an evolving technology in today's market with an enormous number of applications. Mini UAVs are developed in order to compensate the performance constraints imposed by larger UAVs during emergency situations. Multiple mini autonomous UAVs require communication and coordination for ubiquitous coverage and relaying during deployment. Multi-UAV coordination or swarm optimization is required for reliable connectivity among UAVs, due to its high mobility and dynamic topology. In this paper, a Secured UAV (S-UAV) model is proposed which takes the location of the UAVs as inputs to form a Wireless Mesh Network (WMN) among multiple drones with the help of a centralized controller. After WMN formation, efficient communication takes place using A* search, an intelligent algorithm that finds the shortest communication path among UAVs. Further, the S-UAV model utilizes cryptographic techniques such as Advanced Encryption Standard (AES) and Blowfish to overcome the security attacks efficiently. Simulation results show that the S-UAV model offers higher throughput, reduced power consumption and guaranteed message transmission with reduced encryption and decryption time. [ABSTRACT FROM AUTHOR]

This paper presents a robust predictive control scheme with a graphic-based delay boundary analysis to mitigate the electric vehicle (EV) drivetrain oscillating issues, subject to the multi-channel compounding-construction loop delays. The application of Controller Area Network (CAN) in autonomous electric vehicles (AEVs) inevitably induces multi-channel compounding-construction loop delays into the control loop. The in-deep analyzing and understanding of the network-induced loop delays is critical for the electrified powertrain and its motion control. This study aims to guarantee, explicitly, the motion stability of AEV drivetrains as safe-critical and hard real-time applications. Firstly, a graphic-based constructional representation approach is presented for modeling of the compounding-construction loop delays. To resolve the upper bound of the compounding-construction loop delays further, a mathematic expression of delay boundary-envelopment analysis is derived. Secondly, based on the reasonable upper bound, Taylor series expansion is applied to make the system model with nonlinear uncertainties caused by the network-induced loop delays represent in the form of the convex polytope. Then, with the convex polytope of the drivetrain system model, a robust model predictive control (RMPC) approach is developed to enhance the system robustness against the unexpected network-induced delays. To attenuate the online calculation burden, a scheme combining off-line design and on-line synthesis is provided. Finally, the satisfactory motion control performance in both the co-simulations (Matlab&Carsim) and bench experimental tests can strongly verify the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a finite set model predictive control (FS-MPC) based thrust maximization technique for linear induction machines used in linear metros. For modeling of the proposed control method, the end effect is taken into consideration. The proposed control method is used to achieve maximum thrust per ampere and to reduce the thrust ripples. It differs from the FS-MPC methods, where the cost function consists of the thrust and angle errors. The thrust error is calculated from the difference between the reference thrust and the predicted thrust, and the angle error is calculated from the difference between the angle of predicted primary current and the angle of the predicted secondary flux in one side and π/4 on the other side. A comparison between the proposed method and the finite set model predictive direct thrust control (FS-MPDTC) is presented to illustrate the superiority of the proposed method. Both simulation and experimental analysis are conducted to validate the effectiveness of the proposed finite set model predictive direct angle control (FS-MPDAC). A prototype test platform is developed in the laboratory with two 3 kW arc induction motors. The simulation model, experimental test platform, and test results are presented in this paper. [ABSTRACT FROM AUTHOR]

This paper analyzes the efficacy of decoupled wireless access in a two-tier heterogeneous network. The decoupled wireless access and its performance benefits have been studied in different scenarios recently. In this paper, an in-depth analysis on its efficacy from spectral efficiency perspective is provided. To achieve this task, (i) new closed-form expressions for probability of association of user equipment with different tiers employing different frequency bands (i.e., microwave and millimeter wave) with different pathloss exponents are derived using univariate Fox's H-functions; (ii) Distributions of the distance to the serving base stations are also derived; (iii) Exact expressions of spectral efficiency for different association cases are further obtained using bivariate Fox's H-functions. Furthermore, rigorous simulation results are provided which validate the aforementioned analytical results. In addition to that, a detailed discussion on the decoupling gain of decoupled wireless access and its efficacy is also provided. Finally, despite the improvement provided by the decoupled wireless access, which is evident from the results presented in this paper, a few questions are raised on its pragmatic value. [ABSTRACT FROM AUTHOR]

At equatorial latitudes, ionospheric scintillation is the major limitation in achieving high-accuracy GNSS positioning. This is because scintillation affects the tracking ability of GNSS receivers causing losses of lock and degradation on code pseudorange and carrier phase measurements, thus degrading accuracy. During strong ionospheric scintillation, such effects are more severe and GNSS users cannot rely on the integrity, reliability, and availability required for safety-critical applications. In this paper, we propose a novel approach able to greatly reduce these effects of scintillation on precise point positioning (PPP). Our new approach consists of three steps: 1) a new functional model that corrects the effects of range errors in the observables; 2) a new stochastic model that uses these corrections to generate more accurate positioning; and 3) a new strategy to attenuate the effects of losses of lock and consequent ambiguities re-initializations that are caused by the need to re-initialize the tracking. We demonstrate the effectiveness of our method in an experiment using a 30-day static dataset affected by different levels of scintillation in the Brazilian southeastern region. Even with limitations imposed by data gaps, our results demonstrate improvements of up to 80% in the positioning accuracy. We show that, in the best cases, our method can completely negate the effects of ionospheric scintillation and can recover the original PPP accuracy that would have existed without any scintillation. The significance of this paper lies in the improvement it offers in the integrity, reliability, and availability of GNSS services and applications. [ABSTRACT FROM AUTHOR]

To improve the maneuverability of a vehicle and fully leverage the advantages of torque vectoring control (TVC) in improving vehicle dynamics, a method to analytically improve the cornering response based on TVC is proposed in this paper. A feedforward and feedback control architecture based on a two-degree-of-freedom vehicle model is first introduced. An analytical expression of the yaw moment feedforward model is derived under the condition that the transfer function of the ideal yaw rate with respect to the real one is equal to 1. Then, the key influencing factors of the additional yaw moment are investigated in detail. More importantly, the real experimental results under steady and transit state are analyzed to demonstrate how the proposed controller can improve vehicle maneuverability. Experimental results show that the bandwidth of vehicle transient response could be improved by 29.6% in the tests. The controller can be easily extended to any type of TVC even though it is applied to a rear-wheel driven electric vehicle in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a new reentry trajectory generation method, which mainly comprises two parts: reentry profile design and trajectory optimization. Different from conventional methods that design angle-of-attack (AOA) and bank-angle profiles, a height-range profile is proposed in this paper. Using this profile, the AOA can be directly derived and the quasi-equilibrium glide condition is easily satisfied. In addition, the bank angle is analytically derived with the heading error, with only one reversal. Finally, the original problem is transformed into a parameter optimization problem and solved by an improved particle swarm optimization (PSO) method. In PSO, a comparison principle is proposed to tackle constraints and three modifications are applied to the swarm evolution to enhance the searching capacity. To conquer disturbances, an online profile updating strategy is developed based on the nominal profile, so complicated work of designing a trajectory tracker is avoided. Simulation validates the effectiveness of the proposed method and demonstrates that the improved PSO is comparable to the pseudospectral method and is better than the basic PSO. [ABSTRACT FROM AUTHOR]

This paper presents a novel piecewise-linear robust traction controller for electric vehicles with in-wheel motors. Considering the nonlinear tire force and the time-varying wheel and vehicle speeds, a piecewise-linear parameter-varying (PWLPV) slip-ratio model with disturbances is first established. A novel traction controller is then designed. The key of the traction controller proposed in this paper is to separate the traction torque into a feedforward control torque and a PWLPV feedback control torque to track the desired slip ratio. The feedforward controller is calculated through the desired working point of the PWLPV slip-ratio model. The PWLPV controller can guarantee the closed-loop stability and $H_{\infty }$ performance for disturbance rejection. To verify the effectiveness of the proposed traction controller, both simulations and experiments are performed. A high-fidelity vehicle model in veDYNA is employed for simulation studies. Experiments were carried out on an electric vehicle with in-wheel motors. The experimental tests were performed on a brake-measurement track at Nong'an Automotive Proving Ground of the First Automobile Works. [ABSTRACT FROM AUTHOR]

Autonomous driving technologies can provide greater safety, comfort, and efficiency for future transportation systems. Until now, much of the research effort has been devoted to developing different sensing and control algorithms. However, there has been limited research on how to handle sensor errors efficiently. A simple error in the sensor may lead to an unexpected failure in the whole autonomous driving function. In those cases, the vehicle is then recommended to be sent back to the manufacturer for repair, which costs time and money. This paper introduces an efficient automatic online sensor correction method. The method includes four major functions: sensor error detection, human teaching, vehicle learning, and vehicle self-evaluation. The first function is assumed to be ready and the major contribution of this paper is the human-vehicle teaching and learning framework, which utilizes human-vehicle interaction to collaboratively adjust the parameter in the control model in order to compensate for the errors of the sensors. The self-evaluation function is also briefly introduced. The applications of this method to radar and vision sensors to recover adaptive cruise control and lane keeping functions are introduced in detail. Experimental results acquired from high-fidelity 1/10-scale autonomous driving vehicles illustrate the effectiveness and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

Torque safety is one of the key elements of functional safety for future automobile development. For drive-by-wire systems that are commonly used in modern vehicles, pedal signals are critical for the vehicle supervisory controller to generate appropriate torque request to the powertrain, in order to keep the vehicle speed as desired. Problems with pedals or pedal sensors will result in incorrect torque request sent to the drivetrain, possibly resulting in torque safety problems, such as sudden unintended acceleration. This paper proposes a fault detection and isolation strategy based on structural analysis as well as fault mitigation methods for torque safety of drive-by-wire systems, with focus on pedal mechanical stiction fault and pedal sensor faults. Before the diagnostic strategy is designed, fault modeling for these two types of faults are introduced to assist the design of diagnostic tests. In addition, for pedal mechanical stiction fault, this paper introduces an effective approach to calibrate the thresholds in the fault diagnostic system in order to achieve the desired tradeoff between false alarm rate and detection delay. The detection and mitigation strategies are calibrated and validated through model-in-the-loop and hardware-in-the-loop simulations using realistic pedal profiles from the testing results of the EcoCAR2 prototype vehicle developed at the authors’ institution. [ABSTRACT FROM AUTHOR]

Next-generation wireless networks will give rise to heterogeneous networks by integrating multiple wireless access technologies to provide seamless mobility to mobile users with high-speed wireless connectivity. Device-to-device (D2D) communication has proven to be a promising technology that can increase the capacity and coverage of wireless networks. The D2D communication was first introduced in long-term evolution advanced (LTE-A) and has gained immense popularity for the offloading traffic using the licensed and unlicensed band. Challenges arise from resource allocation, provision of quality-of-service (QoS), and the quantification of capacity in an unlicensed band due to the distributed nature of Wi-Fi. In this paper, we propose an analytical performance model for the scalable MAC protocol (SC-MP) in which a resource allocation mechanism is based on the IEEE 802.11 point coordinated function to access the Wi-Fi channel for voice and video/multimedia traffic. In the SC-MP, D2D communication is applied to further offload the video/multimedia traffic. In particular, this paper establishes a three-state semi-Markovian model to derive a closed-form expression of effective capacity in terms of transmission rate and quality-of-service. Further, the SC-MP is analytically modeled using the four-state traditional Markov model to derive the saturation throughput. The analytical results are validated through simulations, hence, proving the appropriateness of the model. [ABSTRACT FROM AUTHOR]

The optimal design regarding energy efficiency of hybrid and electric drivetrains is a problem that includes topology generation, topology optimization, component sizing, and control. One of the challenges of this design problem is that the topology defines the plant sizing and control parameters. Furthermore, the topology also determines the transmission model that is needed for the evaluation of the sizing and control problems. To enable automated component sizing and control optimization, a novel method is presented in this paper for the automated dynamic modeling of arbitrary hybrid and electric drivetrain topologies. In this paper, topologies are modeled at the level of (planetary) gears and clutches, making the method suitable for complex and unconventional drivetrain topologies. A generic transmission model is defined, for which the model parameters are automatically determined. The parameter determination is based on the analytic evaluation of the kinematic and kinetic properties of the components of the topology. All transmission modes are identified and classified, and infeasible and redundant modes are automatically excluded. As the method is analytic, the computation time to determine the model parameters is short (less than a second). The model, in this case, provides the rotational speeds and torques of all power sources as a function of the rotational speed and torque at the wheels, and the control variables. Using a case study, it is shown that the method can be used to automatically solve the control and sizing problems for complex drivetrain topologies. [ABSTRACT FROM AUTHOR]

This paper presents a direct voltage control method for interior permanent magnet synchronous motors (IPMSMs) with a single speed regulator. The method achieves maximum torque per ampere (MTPA) operation by controlling the magnitude and the angle of the voltage vector. For that, the mathematical model for the MTPA trajectory of the IPMSM is derived in the voltage plane. As such, no current sensor is needed, which makes the proposed strategy tolerant to current sensors failure unlike cascaded control loop based methodologies. Although no current sensor is used, the control strategy tracks MTPA trajectory by taking into account both voltage and current limits of the machine. The complete MTPA derivation in the voltage plane is presented in this paper; but, only the final solution is needed for real-time implementation. Henceforth, the simplicity of the control scheme combined with its current sensor dependence free characteristics make it a good candidate for real-time implementation in vehicular applications. The concept is developed and evaluated experimentally on a 10-HP IPMSM. [ABSTRACT FROM AUTHOR]

In recent times, lithium iron phosphate (LFP) batteries have found wide usage in electric vehicles (EVs). Efficient utilization of the storage capacity of any battery used in EVs is heavily dependent on the accuracy of state of charge (SOC) estimation. The classical SOC-OCV (open circuit voltage) based approach for estimation of SOC is quite ineffective under rapid variation in operating condition of EVs. The impact of parameter variations and unknown disturbances due to widely varying environmental conditions and drive terrain, demands a robust observer for SOC estimation. In this regard, this paper proposes a novel SOC estimation technique using a nonlinear fractional-order unknown input observer (FOUIO). The scheme based on the combined framework of fractional calculus and state estimation with unknown input is able to meet the challenges of SOC estimation in EVs by decoupling the plant disturbance inputs from the state estimation process. The optimally designed FOUIO using a linear matrix inequality (LMI) based formulation has been analytically proved for convergence. The proposed FOUIO has been validated and compared with reported SOC estimation schemes for different drive profiles in terms of accuracy and convergence speed. The numerical simulations and hardware in loop (HIL) based experimental results confirm the effectiveness of the proposed FOUIO in estimating the SOC robustly under different scenarios of battery operation. [ABSTRACT FROM AUTHOR]

Track-before-detect (TBD) of weak maneuvering targets in range-Doppler plane is investigated. The existing TBD methods suffer from performance degradation due to the use of approximate motion models in range-Doppler plane. In this paper, exact time evolving rules of target range and Doppler are presented for constant acceleration (CA) and coordinated turn (CT) motions in Cartesian coordinates, and two novel pseudo-spectrum based TBD approaches are proposed for effective detection of weak CA and CT targets in range-Doppler plane, respectively. In accordance with the common CA and CT motions in Cartesian coordinates, the accurate time evolution equations of range and Doppler are derived for CA and CT targets, respectively, which are nonlinear functions of range-Doppler and several invariable parameters. Then, the pseudo-spectrum based TBD of CA and CT targets in range-Doppler plane is performed by matching these invariants. Each range-Doppler cell is predicted according to the exact evolution equation. A pseudo-spectrum is constructed around the predicted position, and sample values of the pseudo-spectrum are added onto cells for energy integration. This eliminates performance loss caused by model mismatch and target output envelope can be well maintained. To deal with the unknown target parameters, two pseudo-spectrum based matched filter banks are proposed for CA and CT targets, respectively. Analysis and design of the filter banks are provided in detail. Simulations are conducted to evaluate the validity of the proposed methods in handling weak maneuvering targets. [ABSTRACT FROM AUTHOR]

Polymer electrolyte membrane fuel cell (PEMFC) air-feed subsystem is usually affected negatively by system uncertainty and unavailable variable. This paper investigates a discrete neural network control with an observer for the oxygen excess ratio (OER) control. The control goal is to avoid oxygen starvation and maintain the optimal net power. Specifically, by utilizing coordinate transformation and Euler approximation, the discrete strict-feedback form is obtained, and the backstepping technique can be applied. To estimate the unavailable variable with the measurable system output, a discrete neural network observer is proposed. Besides, the discrete neural network controller is designed to tackle the system uncertainty and achieve an ideal OER tracking. Finally, the system tracking error is proved to be semi-globally uniformly ultimately bounded by Lyapunov stability theory. Numerical simulations and hardware-in-loop (HIL) experiments are presented to demonstrate the effectiveness and superiorities of the proposed controller. [ABSTRACT FROM AUTHOR]

In this paper we propose a one-dimensional convolutional neural network (CNN)-based state of charge estimation algorithm for electric vehicles. The CNN is trained using two publicly available battery datasets. The influence of different types of noises on the estimation capabilities of the CNN model has been studied. Moreover, a transfer learning mechanism is proposed in order to make the developed algorithm generalize better and estimate with an acceptable accuracy when a battery with different chemical characteristics than the one used for training the model, is used. It has been observed that using transfer learning, the model can learn sufficiently well with significantly less amount of battery data. The proposed method fares well in terms of estimation accuracy, learning speed and generalization capability. [ABSTRACT FROM AUTHOR]

Wireless communication technology has promoted the development of connected hybrid electric vehicles (CHEVs). With traffic signal information, the fuel economy of CHEVs can be improved via optimal speed planning. However, the road environment in most existing studies is unreal and riding comfort is ignored. Therefore, this paper uses the real phase and position information of traffic lights to establish a road model and proposes a multi-objective hierarchical optimal (MOHO) strategy. First, a speed planning module is developed as the upper layer. By integrating speed constraints, slope, and traffic light information, a model predictive control (MPC)-based speed planning strategy (SPS) is developed, which improves riding comfort. Second, an energy management module is developed as the lower layer. An adaptive equivalent consumption minimization strategy (A-ECMS)-based energy management strategy (EMS) is proposed, which achieves the optimal power distribution. The results show that the proposed MOHO strategy can improve riding comfort and fuel economy while avoiding vehicle stopping at the signalized intersection under two different road conditions. [ABSTRACT FROM AUTHOR]

Collision avoidance and platooning applications require vehicle localization at cm-level accuracy and at least 50 Hz rate for full autonomy. The RADAR/LIDAR and camera based methods currently used for vehicle localization do not satisfy these requirements, necessitating complementary technologies. Visible light positioning (VLP) is a highly suitable complementary technology due to its high accuracy and high rate, exploiting the line-of-sight propagation feature of the visible light communication (VLC) signals from LED head/tail lights. However, existing vehicular VLP algorithms impose restrictive requirements, e.g., use of high-bandwidth circuits, road-side lights and certain VLC modulation strategies, and work for limited relative vehicle orientations, thus, are not feasible for general use. This paper proposes a VLC-based vehicle localization method that eliminates these restrictive requirements by a novel VLC receiver design and associated vehicular VLP algorithm. The VLC receiver, named QRX, is low-cost/size, and enables high-rate VLC and high-accuracy angle-of-arrival (AoA) measurement, simultaneously, via the usage of a quadrant photodiode. The VLP algorithm estimates the positions of two head/tail light VLC transmitters (TX) on a neighbouring vehicle by using AoA measurements from two QRXs for localization. The algorithm is theoretically analyzed by deriving its Cramer-Rao lower bound on positioning accuracy, and simulated localization performance is evaluated under realistic platooning and collision avoidance scenarios. Results demonstrate that the proposed method performs at cm-level accuracy and up to 250 Hz rate within a 10 m range under realistic harsh road and channel conditions, demonstrating its eligibility for collision avoidance and safe platooning. [ABSTRACT FROM AUTHOR]

Wireless connectivity in airplanes is becoming more important, demanded, and common. One of the largest bottlenecks with the in-flight Internet is that the airplane is far away from both the satellites and the ground base stations during most of the flight time. Maintaining a reliable and high-rate wireless connection with the airplane over such a long-range link thus becomes a challenge. Microwave frequencies allow for long link distances but lack the data rate to serve up to several hundreds of potential onboard customers. Higher bands in the millimeter-wave spectrum (30 GHz–300 GHz) have, therefore, been utilized to overcome the bandwidth limitations. Still, the per-user throughput with state-of-the-art millimeter-wave systems is an order of magnitude lower than the one available with terrestrial wireless networks. In this paper, we take a step further and study the channel characteristics for the terahertz band (THz, 0.3 THz–10 THz) in order to map the feasibility of this band for aviation. We first propose a detailed channel model for aerial THz communications taking into account both the non-flat Earth geometry and the main features of the frequency-selective THz channel. We then apply this model to estimate the characteristics of aerial THz links in different conditions. We finally determine the altitudes where the use of airplane-to-satellite THz connection becomes preferable over the airplane-to-ground THz link. Our results reveal that the capacity of the airborne THz link may reach speeds ranging from 50–150 Gbps, thus enabling cellular-equivalent data rates to the passengers and staff during the entire flight. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of epidemic routing in mobile social networks considering several communities which are frequently visited by nodes. To this end, a monolithic Stochastic Reward Net (SRN) is proposed to evaluate the delivery delay and the average number of transmissions under epidemic routing by considering skewed location visiting preferences. This model is not scalable enough, in terms of the number of nodes and frequently visited locations. In order to achieve higher scalability, the folding technique is applied to the monolithic model, and an approximate folded SRN is proposed to evaluate performance of epidemic routing. Discrete-event simulation is used to validate the proposed models. Both SRN models for predicting the performance of epidemic routing exhibit high accuracy. We also propose an Ordinary Differential Equation (ODE) model for epidemic routing and compare it with the folded model. The obtained results show that the folded model is more accurate than the ODE model. Moreover, it is proved that the number of transmissions by the time of delivery follows a uniform distribution, for a general class of networks, where positions of nodes are always independent and identically distributed. [ABSTRACT FROM AUTHOR]

Trajectory Following Control (TFC) and lateral stability control are always significant for Autonomous Ground Vehicles (AGVs). However, sometimes it is quite a challenge to achieve excellent tracking performance with less stability loss. In this paper, a novel two-agent non-cooperative game framework is built to explore the coordination approach of the integrated TFC and Direct Yaw Control (DYC) issue. Then the game theory-based control strategies are proposed and two information patterns are investigated. In the open-loop case, the Nash equilibrium of each control cycle is determined by solving discrete-time Riccati equations. When it comes to the closed-loop pattern, the game complexity increases because each player can revise his own game strategy according to the other players’ strategy evolution, and thus the dynamic programming method based on the Bellman's principle is applied to derive the control strategies. Besides, a Linear Parameter-Varying (LPV) method is adopted to facilitate the process of discretizing the state-space model in every control cycle. Finally, simulation tests based on Carsim-Simulink joint platform and experimental tests are carried out to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, a systematic approach for the dynamic modeling of complex planetary gear sets is presented. The approach relies upon a set of rules for building the system matrices and vectors of the system full dynamic model for any planetary gear set. A congruent state-space transformation is applied to obtain a reduced-order rigid model of the system, which allows for faster simulations. The behavior of the tangential forces accounting for the gears interactions is proven to be obtained from the reduced-order model. Furthermore, the kinematic relations of the considered planetary gear set are automatically generated when developing the reduced-order rigid model. As an example, two systems of interest in the vehicle industry are then modeled with the proposed approach and simulated in Matlab/Simulink: a Ravigneaux planetary gear set and a double-stage planetary gear set. [ABSTRACT FROM AUTHOR]

This paper presents an optimal control algorithm for connected cruise control (CCC) system in the presence of the stochastic communication delays. In the CCC system, wireless vehicle-to-vehicle (V2V) communication is the key factor to achieve comprehensive environment perception while it gives rise to delays in the meantime. In order to compensate for the influence of the stochastic delays and develop an optimal controller for the CCC system, we first analyze the vehicle dynamics with the car-following model. Then, the mathematical platoon model is formulated with heterogenous platoon structure. With the objective of minimizing the deviations of vehicle's headway and velocity, a linear quadratic optimization problem is formulated. The optimal control strategy is solved in an iterative manner by using a backward recursion which can be divided into two steps. In particular, the optimal control gain is iteratively derived off-line and the control strategy is calculated on-line. In addition, the control gain convergence, the stability analysis, and the effect of the packet losses and state noises are analyzed. Finally, the stability and performance of the proposed control algorithm are verified by numerical simulations to highlight its superiority compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

Battery management system (BMS) is one of the key subsystems of electric vehicle, and the battery state-of -charge (SOC) is a crucial input for the calculations of energy and power. Therefore, SOC estimation is a significant task for BMS. In this paper, a new method for online estimating SOC is proposed, which combines a novel adaptive extended Kalman filter (AEKF) and a parameter identification algorithm based on adaptive recursive least squares (RLS). Specifically, according to the first order R-C network equivalent circuit model, the battery model parameters are identified online using the RLS with multiple forgetting factors. Based on the identified parameters, the novel AEKF is used to accurately estimate the battery SOC. The online identification of parameter tracks the varying model. At the same time, due to the novel AEKF algorithm to dynamically adjust the system noise parameter, excellent accuracy of the SOC real-time estimation is obtained. Experiments are set to evaluate the accuracy and robustness of the proposed SOC estimation method. The simulation test results indicate that under DST and UDDS conditions, the maximum absolute errors are less than 0.015 after filtering convergence. In addition, the maximum absolute error is less than 0.02 in the simulation of DST with current and voltage measurement noise, so is in DST with current offset sensor error. The tests indicate that the proposed method can accurately estimate battery SOC and has strong robustness. [ABSTRACT FROM AUTHOR]

This paper presents models and optimization algorithms to compute the fuel-optimal energy management strategies for a parallel hybrid electric powertrain on a given driving cycle. Specifically, we first identify a mixed-integer model of the system, including the engine on/off signal and the gear-shift commands. Thereafter, by carefully relaxing the fuel-optimal control problem to a linear program, we devise an iterative algorithm to rapidly compute the minimum-fuel energy management strategies including the optimal gear-shift trajectory. We validate our approach by comparing its solution with the globally optimal one obtained solving the mixed-integer linear program and with the one resulting from the implementation of the optimal strategies in a high-fidelity nonlinear simulator. We showcase the effectiveness of the presented algorithm by assessing the impact of different powertrain configurations and electric motor size on the achievable fuel consumption. Our numerical results show that the proposed algorithm can assess fuel-optimal control strategies with low computational burden, and that powertrain design choices significantly affect the achievable fuel consumption of the vehicle. [ABSTRACT FROM AUTHOR]

In this paper, a new integrated tripolar pad using double-sided LCC compensation is proposed and then based on the circuit characteristic, which is analyzed in detail, the introduced system is simulated and investigated. The tripolar pad utilizes three decoupled coils and shows a promising tolerance against rotational displacements and consequently provides a convenience for the user to park the vehicle. Due to compensator coils, the double-sided method of compensation is more massive than the other methods. Here, the authors offer a scheme to make the wireless power transfer system more compact by integrating the compensation inductors with the main couplers. In order to locate the best place to embed the compensator coils, the flux density between the couplers is analyzed. Additionally, it is shown that since the pad is composed of multiple coils, there are some undesired mutual inductances that should be designed for the least possible values. The behaviors of the proposed pad are analyzed and thoroughly discussed based on four different displacements. In order to validate the theoretical results, a 500 W wireless power transfer is fabricated and tested at 85 kHz for the proposed double-sided LCC compensation. The simulation and experimental results show the performance of the proposed structure. [ABSTRACT FROM AUTHOR]

As an emerging technology in 5G, ultra-dense unmanned aerial vehicles (UAVs) network can significantly improve the system capacity and networks coverage. However, it is still a challenge to reduce interference and improve energy efficiency (EE) of UAVs. In this paper, we investigate a downlink power control problem to maximize the EE in an ultra-dense UAV network. Firstly, the power control problem is formulated as a discrete mean field game (MFG) to imitate the interactions among a large number of UAVs, and then the MFG framework is transformed into a Markov decision process (MDP) to obtain the equilibrium solution of the MFG due to the dense deployment of UAVs. Specifically, a deep reinforcement learning-based MFG (DRL-MFG) algorithm is proposed to suppress the interference and maximize the EE by using deep neural networks (DNN) to explore the optimal power strategy for UAVs. The numerical results show that the UAVs can effectively interact with the environment to obtain the optimal power control strategy. Compared with the benchmarks algorithms, the DRL-MFG algorithm converges faster to the solution of MFG and improves the EE of UAVs. Moreover, the impact of the transmit power on EE under the different heights of the UAVs is also analyzed. [ABSTRACT FROM AUTHOR]

The testing of the intelligent driving systems is faced with the challenges of efficiency because real traffic scenarios are infinite, uncontrollable and difficult to be precisely defined. Based on the complexity index of scenario that designed to measure the test effect indirectly, a new combinational testing algorithm of test cases generation is proposed to make a balance among multiple objects including test coverage, the number of test cases and test effect. Then a joint simulation platform based on Matlab, PreScan and Carsim is built up to realize the construction of 3D test environment, execution of test scenarios and evaluation of test results automatically and seamlessly. The strategy proposed in this paper is validated by applying it to a traffic jam pilot system. The result shows that the proposed strategy can improve the overall complexity of the designed test scenarios effectively, which can help us detect system faults faster and easier. And the time required to conduct tests is reduced obviously by means of automation. [ABSTRACT FROM AUTHOR]

Active safety systems are of significant importance for autonomous vehicles operating in safety-critical situations like an obstacle-avoidance manoeuvre with high vehicle speed or poor road condition. However, a conventional electronic stability control system, may not always yield desired path following and yaw stability performance in such circumstances merely through brake intervention. This paper pursues a detailed investigation on utilising model predictive control (MPC) and torque vectoring for path following and yaw stability control of over-actuated autonomous electric vehicles (AEVs) in dangerous double lane change manoeuvre. The control problem of the AEV is formulated based on MPC by utilising active front steering and torque vectoring, and constraints are imposed explicitly on yaw rate and sideslip angle to ensure yaw stability. Four MPC-based controllers are designed based on double-track vehicle models. Specifically, they include two one-level controllers, i.e. one with torque vectoring and one with equal torque allocation, and two two-level controllers, i.e. one with optimisation-based torque allocation and one with rule-based allocation. These controllers are assessed extensively, with respect to passing velocity, tracking accuracy, tyre utilisation and robustness. The effect of horizon length on the control performance and computational efficiency is also investigated. [ABSTRACT FROM AUTHOR]

Cooperative Adaptive Cruise Control (CACC) is considered as a key enabling technology to automatically regulate the inter-vehicle distances in a vehicle string, and improve the traffic throughput efficiency. In the existing CACC systems, the coupling between wireless communication uncertainty, and system states is not well modeled. In this paper, we integrate the jamming attacks, and wireless channel fading effects into the CACC state space equations such that it effectively captures the coupling impact. Then, we propose a novel time domain approach to analyze the mean string stability (MSS) of such a model. Based on the proposed model, we analyze the impact of the jammer's location on the string stability. We derive a sufficient condition for the packet successful delivery probability which indicates that the jammer has a higher probability to destabilize the string when it is closer to the first vehicle following the lead vehicle. We also propose a methodology to compute the upper, and lower bounds of the inter-vehicle distance trajectories between the lead vehicle, and its follower. Furthermore, string safety is investigated by numerically estimating the collision probability across the string. We conduct comprehensive Monte Carlo simulations to evaluate the stability, and safety of the string in various scenarios. We identify that string stability, and safety are highly influenced by the jamming attacks signal, and jammer's location. We show the consistency between the main results achieved by MSS analysis, and the Monte Carlo simulations. [ABSTRACT FROM AUTHOR]