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Driver distraction has been deemed a major cause of traffic accidents. However, drivers’ brain response activities to different distraction types have not been well investigated. The purpose of this study is to investigate the response of electroencephalography (EEG) activities to different distraction tasks. In the conducted simulation tests, three secondary tasks (i.e., a clock task, a 2-back task, and a navigation task) are designed to induce different types of driver distractions. Twenty-four participants are recruited for the designed tests, and differences in drivers’ brain response activities concerning distraction types are investigated. The results show that the differences in comprehensive distraction are more significant than that in single cognitive distraction. Friedman test and post hoc two-tailed Nemenyi test are conducted to further identify the differences in band activities among brain regions. The results show that the theta energy in the frontal lobe is significantly higher than that in other brain regions in distracted driving, whereas the alpha energy in the temporal lobe significantly decreases compared to other brain regions. These results provide theoretical references for the development of distraction detection systems based on EEG signals.

Efficient, safe, and comfortable electric vehicles (EVs) are essential for the creation of a sustainable transport system. Distributed-driven EVs, which often use in-wheel motors (IWMs), have many benefits with respect to size (compactness), controllability, and efficiency. However, the vibration of IWMs is a particularly important factor for both passengers and drivers, and it is, therefore, crucial for the successful commercialization of distributed-driven EVs. This article provides a comprehensive literature review and state-of-the-art vibration-source analysis and mitigation methods in IWMs. First, selection criteria are given for IWMs, and a multidimensional comparison for several motor types is provided. The IWM vibration sources are then divided into internally and externally induced vibration sources and discussed in detail. Next, vibration reduction methods, which include motor-structure optimization, motor controller, and additional control components, are reviewed. Emerging research trends and an outlook for future improvement aims are summarized at the end of this article. This article can provide useful information for researchers who are interested in the application and vibration mitigation of IWMs or similar topics.

During patrol and surveillance tasks, attitude control is crucial for improving the terrain adaptability of unmanned wheel-legged hybrid vehicles. This paper proposes an attitude control strategy for unmanned wheel-legged hybrid vehicles, considering the contact of the wheels and ground. The proposed method can naturally achieve torque control efficiently of each joint actuator and wheel-side actuator and avoid the discrepancy between off-road terrain and stability. First, an inverse kinematics model is established to resolve the body and each joint rotation angle, and the dynamic model is built based on the multi rigid body theory, considering the contact points planning of wheel and ground. Considering the nonholonomic constraint of the structure scheme, a hierarchical real-time attitude controller for a wheel-legged vehicle is proposed. The upper layer calculates the contact points of each wheel and the ground through the quadratic programming algorithm, and the lower layer is divided into a legged motion generator and a wheel motion generator by a mathematical analysis method. Finally, the proposed method is applied to achieve the tracking and control of the whole-body trajectory. The proposed strategy can achieve the decoupling of wheeled motion generator and legged motion generator, and improve control efficiency.

This article presents a novel integrated path-following, crash avoidance, and crash mitigation control algorithm for autonomous vehicles. To improve stability and tracking accuracy of the algorithm in extreme conditions, combined-slip tire forces are considered in the system model. A predictive control framework that monitors slip conditions at each tire is then developed to achieve good dynamics performance by controlling active front steer and brake modulation at each corner. A novel switching mechanism that does not rely on a separate path generation module is designed for avoidance and mitigation phases, which is verified in various harsh driving conditions. Another strong point is the objective function for the crash mitigation phase that is developed based on real-world crash statistics. Simulation results confirm that the proposed algorithm can not only track the desired path in normal driving phase, but also avoid crash and reduce crash severity with ensured vehicle stability.

Advanced algorithms can promote the development of energy management strategies (EMSs) as a key technology in hybrid electric vehicles (HEVs). Reinforcement learning (RL) with distributed structure can significantly improve training efficiency in complex environments, and multi-threaded parallel computing provides a reliable algorithm basis for promoting adaptability. Dedicated to trying more efficient deep reinforcement learning (DRL) algorithms, this paper proposed a deep q-network (DQN)-based energy and emission management strategy (E&EMS) at first. Then, two distributed DRL algorithms, namely asynchronous advantage actor-critic (A3C) and distributed proximal policy optimization (DPPO), were adopted to propose EMSs, respectively. Finally, emission optimization was taken into account and then distributed DRL-based E&EMSs were proposed. Regarding dynamic programming (DP) as the optimal benchmark, simulation results show that three DRL-based control strategies can achieve near-optimal fuel economy and outstanding computational efficiency, and compared with DQN, two distributed DRL algorithms have improved the learning efficiency by four times.

This paper proposes a novel robust path-following strategy for autonomous road vehicles based on type-2 fuzzy PID neural network (PIDT2FNN) method coupled to an Extended Kalman Filter-based Fuzzy Neural Network (EKFNN) observer. Uncertain Gaussian membership functions (MFs) are employed to self-adjust the universe of discourse for MFs using the adaptation mechanism derived from Lyapunov stability theory and Barbalat’s lemma. External disturbances are significant in autonomous vehicles by changing the driving condition. Furthermore, parametric uncertainties related to the physical limits of tires and the change of the vehicle mass may significantly affect the desired performance of autonomous vehicles. The robustness of the proposed controller against the parametric uncertainties and external disturbances is compared with one active disturbance rejection control (ADRC) algorithm, and a linear-quadratic tracking (LQT) method. The obtained results in terms of the maximum error and root mean square error (RMSE), demonstrate the effectiveness of the proposed control algorithm to reach the minimized path-tracking error.

A comparative study of model predictive control (MPC) schemes and robust$$H_{\infty }$$H∞state feedback control (RSC) method for trajectory tracking is proposed in this paper. The main objective of this paper is to compare MPC and RSC controllers’ performance in tracking predefined trajectory under different scenarios. MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire, which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode. RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison. Then, three test cases are built in CarSim-Simulink joint platform. Specifically, the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions. Besides, the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability. Furthermore, an extreme curve test is built where the road adhesion changes suddenly, in order to test the performance of both controllers under extreme conditions. Finally, the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.

The in-wheel motor (IWM)-driven electric vehicles (EVs) attract increasing attention due to their advantages in dimensions and controllability. The majority of the current studies on IWM are carried out with the assumption of an ideal actuator, in which the coupling effects between the non-ideal IWM and vehicle are ignored. This paper uses the braking process as an example to investigate the longitudinal–vertical dynamics of IWM-driven EVs while considering the mechanical–electrical coupling effect. First, a nonlinear switched reluctance motor model is developed, and the unbalanced electric magnetic force (UEMF) induced by static and dynamic mixed eccentricity is analyzed. Then, the UEMF is decomposed into longitudinal and vertical directions and included in the longitudinal–vertical vehicle dynamics. The coupling dynamics are demonstrated under different vehicle braking scenarios; numerical simulations are carried out for various road grades, road friction, and vehicle velocities. A novel dynamics vibration absorbing system is adopted to improve the vehicle dynamics. Finally, the simulation results show that vehicle vertical dynamic performance is enhanced.

This article presents an interacting multiple model (IMM) for short-term prediction and long-term trajectory prediction of an intelligent vehicle. This model is based on vehicle's physics model and maneuver recognition model. The long-term trajectory prediction is challenging due to the dynamical nature of the system and large uncertainties. The vehicle physics model is composed of kinematics and dynamics models, which could guarantee the accuracy of short-term prediction. The maneuver recognition model is realized by means of hidden Markov model, which could guarantee the accuracy of long-term prediction, and an IMM is adopted to guarantee the accuracy of both short-term prediction and long-term prediction. The experiment results of a real vehicle are presented to show the effectiveness of the prediction method.

Obstacle avoidance strategy is important to ensure the driving safety of unmanned ground vehicles. In the presence of static and moving obstacles, it is challenging for the unmanned ground vehicles to plan and track the collision-free paths. This paper proposes an obstacle avoidance strategy consists of the path planning and the robust fuzzy output-feedback control. A path planner is formed to generate the collision-free paths that avoid static and moving obstacles. The quintic polynomial curves are employed for path generation considering computational efficiency and ride comfort. Then, a robust fuzzy output-feedback controller is designed to track the planned paths. The Takagi–Sugeno (T–S) fuzzy modeling technique is utilized to handle the system variables when forming the vehicle dynamic model. The robust output-feedback control approach is used to track the planned paths without using the lateral velocity signal. The proposed obstacle avoidance strategy is validated in CarSim® simulations. The simulation results show the unmanned ground vehicle can avoid the static and moving obstacles by applying the designed path planning and robust fuzzy output-feedback control approaches.

Road intersection is one of the most complex and accident-prone traffic scenarios, so it’s challenging for autonomous vehicles (AVs) to make safe and efficient decisions at the intersections. Most of the related studies focus on the solution to a single scenario or only guarantee safety without considering driving efficiency. To address these problems, this study proposed a deep reinforcement learning enabled decision-making framework for AVs to drive through intersections automatically, safely and efficiently. The mapping relationship between traffic images and vehicle operations was obtained by an end-to-end decision-making framework established by convolutional neural networks. Traffic images collected at two timesteps were used to calculate the relative velocity between vehicles. Markov decision process was employed to model the interaction between AVs and other vehicles, and the deep Q-network algorithm was utilized to obtain the optimal driving policy regarding safety and efficiency. To verify the effectiveness of the proposed decision-making framework, the top three accident-prone crossing path crash scenarios at intersections were simulated, when different initial vehicle states were adopted for better generalization capability. The results showed that the developed method could make AVs drive safely and efficiently through intersections in all of the tested scenarios.

Chassis control systems play a significant role in achieving the desired vehicle performance and stability during various severe maneuvers. A probabilistic estimation approach by hybridization of optimal robust control and a damped least-square backpropagation based neural networks (NN) is proposed to design a control system for dealing with unknown nonlinear dynamics of a passenger car. To this end, a four-wheel active steering (4WAS) model is employed and a multilayer perceptron (ML) feed-forward backpropagation neural network (FFBPNN) model is developed as an approximator. The optimal robust control is employed to regulate the yaw rate and side-slip angle of the vehicle to follow the desired vehicle response. The developed FFBPNN model is trained to distinguish the nonlinear dynamics of the vehicle and the corresponding optimal feedback gain during a wide range of operating conditions via the state variables. The robustness of the controller is evaluated using Lyapunov stability method. The performance of the proposed controller is analyzed considering the open-loop and closed-loop responses of the nonlinear vehicle model and a sliding mode controller to track the desired yaw rate and side-slip angle responses. The results obtained during severe maneuvers suggest that the proposed control method can substantially enhance the handling and stability performances of the vehicle.

The paper proposes a motion control framework for the unmanned wheel-legged hybrid vehicle to track the motion trajectory considering uncertain disturbances. The whole-body dynamic model is built with the contact force of each rolling wheel, which serves as the foundation to obtain trajectory tracking. The angular momentum and linear momentum are optimized by the robust model predictive control algorithm considering the soft constraint of the relaxation variable. The contact force between wheel and ground is solved by the quadratic programming algorithm to efficiently obtain the flexion/extension joint and wheel motion planning. Then, the explicit algorithm to calculate the torque commands of the flexion/extension joint considering the feed-forward torque and feedback torque to improve the control accuracy. Simulation results validate that the control framework based on the robust model predictive control algorithm can solve the uncertain disturbances in process of the vehicle running on the rough road.

In this paper, a novel algorithm is proposed for the motion planning and path following automated cars with the incorporation of a collision avoidance strategy. This approach is aligned with an optimal reinforcement learning (RL) coupled with a new risk assessment approach. For this purpose, a probabilistic function-based collision avoidance strategy is developed, and the proposed RL approach learns the probability distributions of the adjacent and leading vehicles. Subsequently, the nonlinear model predictive control (NMPC) algorithm approximates the optimal steering input and the required yaw moment to follow the safest and shortest path through the optimal RL-based probabilistic risk function framework. Additionally, it is attempted to maintain the travel speed for the ego vehicle stable such that the ride comfort is also offered for the vehicle occupants. For this purpose, the steering system dynamics are also incorporated to provide a thorough understanding of the vehicle dynamics characteristic. Different driving scenarios are employed in the present paper to evaluate the proposed algorithm’s effectiveness.

This article proposed a reconfigurable control scheme for articulated vehicles’ stabilization by leveraging optimization-based control techniques. The central objective is to maintain a good lateral and yaw stability of the vehicle with optimal corrective brakes, meanwhile applicable to different actuation configurations. This is achieved by a two-layer control structure, where the high-level controller formulates as a model predictive control (MPC) tracking problem to generate corrective center-of-gravity (CG) yaw moment of each unit. The lower level controller utilizes the control allocation (CA) algorithm with real-time constraints to optimally calculate differential brakes at each wheel with maximum utilization-of-tires capacity. To evaluate its real-time performance, experimental validation is carried out on the electrified tractor-trailer with selective differential braking systems. It is observed that the controller is effective in dynamics control, meanwhile reconfigurable to various actuation configurations. Furthermore, the proposed system has great potential in production tractor-trailer systems due to the low cost and number of sensor requisites.

Ride comfort and road holding are two substantial performance criteria related to the suspension system of road vehicles. These performance criteria are critical in controller design and chassis stability of automated driving vehicles. Control demands in terms of accuracy, quick response and robustness to matched and mismatched uncertainties are suggestive of employing adaptive robust controllers. Herein, a state observer-based modified sliding mode interval fuzzy type-2 neural network (FT2NN) controller is designed to suppress the vibrations from a typical rough terrain imposed to the nonlinear suspension system of the vehicles. The nonlinear system dynamics are estimated using the universal approximation capacity of the neuro-fuzzy type-2 approach and the states are obtained by the adaptive robust state observer. The membership functions (MFs) of the fuzzy type-2 system are employed to deal with the uncertainties through variable mean and variances for upper and lower MFs. Furthermore, the proposed controller has the advantage of relaxing the condition for the approximation error boundaries while the estimation step is utilized to observe the boundaries adaptively. A new adaptive compensator is employed to withstand the effect of the external disturbance, the approximation errors related to the unknown nonlinear functions and state estimations. The results obtained from the proposed controller are suggestive of the higher effectiveness of the proposed controller compared to the tested Neuro-PID controller, and also the passive suspension system. The high-fidelity MSC ADAMS based co-simulations were implemented to validate the practicality of the proposed controller.

This paper presents a novel local motion planning framework in a hierarchical manner for autonomous vehicles to follow a trajectory and agilely avoid obstacles. In the upper layer, a new path-planning method based on the resistance network is applied to plan behaviors (e.g. lane keeping or changing), where the human-like factors can be included to simulate different driver styles, such as the aggressive, moderate, and conservative. The planned results (i.e. the lane-change command and the local planned path) will guide the lower-layer planner to decide the local motion. In the lower layer, for the sake of simplicity and alleviation of the computational burden, two separate model predictive controllers (MPC) based on a point-mass kinematic model are utilized for both longitudinal and lateral motion planning. Finally, a super-twisting sliding mode controller (STSMC) based motion tracker is designed to show the feasibility of the proposed decoupled planning method and decide the desired control actions of autonomous vehicles. Several scenarios are defined to comprehensively test and demonstrate the effectiveness and the real-time applicability of the new motion-planning framework. The results show that the proposed method performs very well in the planning and tracking process and takes less than $\text{25 ms}$ for the whole planning process, which can be easily implemented in real-world applications.

Distributed-drive electric vehicles (EVs) replace internal combustion engine with multiple motors, and the novel configuration results in new dynamic-related issues. This paper studies the coupling effects between the parameters and responses of dynamic vibration-absorbing structures (DVAS) for EVs driven by in-wheel motors (IWM). Firstly, a DVAS-based quarter suspension model is developed for distributed-drive EVs, from which nine parameters and five responses are selected for the coupling effect analysis. A two-stage global sensitivity analysis is then utilized to investigate the effect of each parameter on the responses. The control of the system is then converted into a multiobjective optimization problem with the defined system parameters being the optimization variables, and three dynamic limitations regarding both motor and suspension subsystems are taken as the constraints. A particle swarm optimization approach is then used to either improve ride comfort or mitigate IWM vibration, and two optimized parameter sets for these two objects are provided at last. Simulation results provide in-depth conclusions for the coupling effects between parameters and responses, as well as a guideline on how to design system parameters for contradictory objectives. It can be concluded that either passenger comfort or motor lifespan can be reduced up to 36% and 15% by properly changing the IWM suspension system parameters.

For the suspension system equipped with nonlinear hydraulic actuators and excited by external road conditions, a road adaptive intelligent suspension control strategy is developed. In this work, (1) a multi-phase intelligent road adaptive control architecture is developed to enhance the ride comfort in the presence of varying road excitations; (2) a modified algorithm is proposed to improve the system performance. Initially based on the nonlinear system dynamics, a sliding mode controller based on an improved super-twisting algorithm is proposed. In the Off-line phase, the optimized control parameters based on particle swarm optimization (PSO) approach for each road level are determined and supplied to a probabilistic neural network (PNN)-based classifier for training. In the On-line phase, the PNN classifier employs the measured unsprung mass acceleration to determine the road level and supplies the information to the controller database. Based on the classified road level, corresponding control parameters as determined by PSO are then selected. These control parameters are then supplied to the nonlinear controller which provides the active control. The closed-loop stability of the proposed approach is proved, and the simulation results for different road levels are presented to show the effectiveness of the proposed approach.

An online energy management controller is presented in this paper for a plug-in hybrid electric vehicle (PHEV), which is based on driving conditions recognition and genetic algorithm (GA). The proposed controller can be used in the real-time application. First, the studied multimode PHEV is modeled and four traction operation modes are introduced in detail. Second, the principal component analysis (PCA) algorithm is utilized to classify the real historical driving conditions data. Four types of driving conditions are constructed to describe the representative scenarios. Then, GA is applied to search the optimal values for seven control actions offline. These parameters for different driving conditions are preserved and can be activated online. Finally, the driving condition is identified online and the corresponding control actions are loaded and adopted. Simulation results indicate that the proposed approach is close to the globally optimal method, dynamic programming, and is superior to the charge-depleting/charge-sustaining technique. Also, hardware-in-the-loop experiment is built to validate the real-time characteristic of the proposed strategy.

This paper investigates the lane keeping control for four-wheel independently actuated autonomous vehicles. To guarantee the vehicle safety when the active-steering motor entirely fails, the steering manoeuvre is realized by the differential drive assisted steering (DDAS) that is generated by the differential moment between the front wheels. A novel adaptive multivariable super-twisting control strategy is proposed to realize the control objective in finite time, considering the multiple unknown and mismatched disturbances of the steering system with the chattering effect removed. In the sliding surface, a nonlinear function is designed to adaptively change the damping ratio of the closed-loop system so as to improve the transient performance of the lane keeping control in the faulty steering condition. The controller design has avoided the use of the lateral velocity which is usually hard to measure in practice. Instead, the lane keeping errors and their time derivatives are estimated with a high-order sliding mode observer based on a vision system. The finite-time convergence of the closed-loop system is proved by the Lyapunov method. Results of CarSim-Simulink simulations with the proposed control strategy compared with a tradition sliding mode controller based on a high-fidelity and full-car model have verified the effectiveness and robustness of the proposed controller in the lane keeping control via DDAS with the guaranteed high performance.

© 2018 Elsevier Ltd Driven by the increasing requirement for road conditions in the field of the controllable suspension system, this paper presents a novel nonlinear road-excitation classification procedure for arbitrary suspension control strategy. The proposed procedure includes four steps: the definition of controller parameters, selection of insensitive frequency ranges, calculation of superior features, and generation of the classifier. To better illustrate the proposed procedure, the clipped optimal control strategy is taken as an example in the simulation part. Simulation results reveal that the proposed method can accurately estimate road excitation level for various controller parameters, vehicle speeds, and vehicle models. Three contributions have been made in this paper: (1) A road classification procedure that can be used for road adaptive suspension control with any control algorithm is developed; (2) In order to improve classification accuracy, the concept of insensitive index which is based on the time-frequency analysis is proposed; (3) Experimental validation with a quarter vehicle test rig is performed, which has verified the effectiveness of the proposed method for the adaptive controllable suspension system.

This article investigates the integrated lane-keeping and roll control for autonomous ground vehicles (AGVs) considering the transient performance and system disturbances. The robust integral of the sign of error (RISE) control strategy is proposed to achieve the lane-keeping control purpose with rollover prevention, by guaranteeing the asymptotic stability of the closed-loop system, attenuating systematic disturbances, and maintaining the controlled states within the prescribed performance boundaries. Three contributions have been made in this article: 1) a new prescribed performance function (PPF) that does not require accurate initial errors is proposed to guarantee the tracking errors restricted within the predefined asymptotic boundaries; 2) a modified neural network (NN) estimator which requires fewer adaptively updated parameters is proposed to approximate the unknown vertical dynamics; and 3) the improved RISE control based on PPF is proposed to achieve the integrated control objective, which analytically guarantees both the controller continuity and closed-loop system asymptotic stability by integrating the signum error function. The overall system stability is proved with the Lyapunov function. The controller effectiveness and robustness are finally verified by comparative simulations using two representative driving maneuvers, based on the high-fidelity CarSim–Simulink simulation.

This paper proposes a novel observer-based sliding mode control (SMC) to enhance the performance of autonomous vehicles (AVs) rollover behavior under various road profile input. The model of half-car system is first established to describe the AVs rollover behavior by considering nonlinear dynamics of tire force and controllable suspension force under various movement conditions. Moreover, an unscented Kalman Filter (UKF) algorithm is proposed to identify the sprung mass. Combing with the interacting multiple model (IMM) approach and Markov Chain Monte Carlo (MCMC) theory, a novel interacting multiple model unscented Kalman Filters (IMMUKF) observer based is developed to estimate the movement state of AVs system. Then, an adaptive observer-based sliding mode control (AOSMC) strategy is proposed to constrain the AVs roll performance under the various external input. The stability of the proposed algorithm is proved by using Lyapunov function. Finally, simulations and validations are performed on a high-fidelity CarSim® software by using J-turn scenario under various road excitation, to validate the proposed algorithm for AVs system, and the results illustrate that the improved roll states are more than 15% compared with the traditional SMC algorithm.

The coupling effect between road excitation and an in-wheel switched reluctance motor (SRM) on vehicle ride comfort is numerically analysed. A hybrid control system consisting of fault tolerant H∞ suspension controller and SRM controller for an in-wheel SRM driven electric vehicle is proposed to improve the vehicle ride comfort and motor operation performance. By conducting numerical simulations based on the developed quarter-car active suspension model and switched reluctance motor model, it is observed that the road roughness is highly coupled with SRM airgap eccentricity and unbalanced residual vertical force. The SRM airgap eccentricity is influenced by the road excitation and becomes time-varying such that a residual unbalanced radial force is induced; which is one of the major causes of SRM vibration. To suppress SRM vibration and to prolong the SRM lifespan, while at the same time improving vehicle ride comfort, a fault tolerant controller based on output feedback H∞ control method is designed to reduce the sprung mass acceleration. Moreover, an SRM controller is adapted by using the combined Current Chopping Control (CCC) and Pulse Width Modulation control (PWM) to further improve the SRM performance. A comparison of passive suspension and suspensions with hybrid control method on the vehicle and SRM dynamic response under stochastic road excitation and bump road excitation is illustrated. The results indicate that the proposed hybrid control method can effectively reduce the SRM airgap eccentricity, residual unbalanced radial force and achieve better vehicle ride comfort.

This paper presents a speed-independent road classification strategy (SIRCS) based on sole measurement of unsprung mass acceleration. The new method provides an easy yet accurate classification methodology. To this purpose, a classification framework with two phases named off-line and online is proposed. In the off-line phase, the transfer function from unsprung mass acceleration to the road excitation is firstly formulated, and a random forest-based frequency domain classifier is then generated according to the standard road definition of ISO 8608. In the online phase, unsprung mass acceleration and vehicle velocity are firstly combined to calculate the equivalent road profile in the spatial domain, and then a two-step road classifier attributes the road excitation to a certain level based on the power spectral density (PSD) of the equivalent road profile. Simulations are carried out for different classification intervals, varying velocity, system uncertainties and measurement noises. Road experiments are finally performed in a production vehicle to validate the proposed SIRCS. Measurement of only unsprung mass acceleration to identify road classification and less rely on the training data are the major contributions of the proposed strategy.

A comparative study of longitudinal and lateral control maneuverer in model predictive control (MPC) schemes and robust state feedback control (RSC) method for trajectory tracking of automated ground vehicles (AGVs) is presented in this paper. Both MPC-based and RSC-based tracking controller are designed on the same basis of longitudinal-lateral-yaw motions of a single-track vehicle model. The main objective is to compare the controllers’ performance of tracking accuracy of path and velocity under different test scenarios. The simulation is implemented on Carsim-Simulink joint platform using high-fidelity vehicle model and the mass uncertainties, sensor measurement noise and the performance in extreme driving conditions: turn with big curvature are considered. The simulation results indicate that mass uncertainty and sensor measurement noise of lateral velocity have little effect on the RSC-based controller, while that have relatively great influence on MPC-based one. However, MPC-based controller shows a shorter response time and more accurate tracking performance than RSC-based scheme. Finally, for the test of turn with curvature 0.02 , the maximum velocity that RSC-based controller can carry out has reached 22m/s, which is slightly better than MPC-based one: 21m/s.

This paper presents a novel observer-based control strategy to improve the vehicle roll behavior performance through magneto-rheological (MR) dampers under steering wheel input and various road excitation conditions. Since the vehicle roll with sudden steering input is an essential part of driving safety and possesses inherent nonlinearities, the full-car nonlinear Takagi-Sugeno (T-S) fuzzy model is first established to describe the vehicle roll dynamics considering nonlinear coupling dynamics of tire lateral force and MR damper force under road excitation input. Furthermore, a T-S model-based fuzzy observer is adapted to estimate the vehicle roll angle and roll rate. The stability conditions for the used T-S observer are calculated using linear matrix inequalities (LMIs), and the proposed observer is induced by solving the proposed LMI. Based on the Lyapunov function, sliding mode theory and prescribed performance function, a novel state observer-based prescribed performance control strategy is developed to constrain the controlled vehicle roll angle and roll rate state within the prescribed performance boundaries. Finally, the proposed techniques are validated through the J-turn and Fishhook tests conducted via a high-fidelity CarSim software platform.

A single-time velocity estimator-based reinforcement learning (RL) algorithm, integrated with a chaotic metaheuristic optimization technique is proposed in this article for the optimal path-planning of the nonholonomic robots considering a moving/stationary obstacle avoidance strategy. The additional contribution of the present study is by employing the Terramechanics principles to incorporate the effects of wheel sinkage into the deformable terrain on the dynamics of the robot aiming to find the optimal compensating force/torque magnitude to sustain a robust and smooth motion. The designed systematic control-oriented system incorporates a cost function of weighted components associated with the target-tracking and the obstacle avoidance. The designed velocity estimator contributes to the finite-state Markov decision process (MDP) in order to train the transition probabilities of the problem objectives. Based on the obtained results, the optimal solution for the Q-learning in terms of the adjusting factor for the minimized tracking error and obstacle collision risk propagation profiles is found at 0.22. The results further confirm the promising capacity of the proposed optimization-based RL algorithm for the collision avoidance control of the nonholonomic robots on deformable terrains.

This paper proposes a novel 2-step global sensitivity analysis algorithm to provide an in-depth sensitivity analysis of the vehicle parameters on the system responses. A 9 degree-of-freedom...

This paper presents a new approach for vibration mitigation based on a dynamic vibration absorbing structure (DVAS) for electric vehicles (EVs) that use in-wheel switched reluctance motors (SRMs). The proposed approach aims to alleviate the negative effects of vibration caused by the unbalanced electromagnetic force (UMEF) that arises from road excitations. The analytical model of SRMs is first formulated using Fourier series, and then a model of the coupled longitudinal-vertical dynamics is developed taking into consideration the external excitations consisting of the aerodynamic drag force and road unevenness. In addition, numerical simulations for a conventional SRM-suspension system and two novel DVASs are carried out for varying road levels specified by ISO standards and vehicle velocities. The results of the comparison reveal that a 35% improvement in ride comfort, 30% improvement of road handling, and 68% improvement in air gap between rotor and stator can be achieved by adopting the novel DVAS compared to the conventional SRM-suspension system. Finally, multi-body simulation (MBS) is performed using LMS Motion to validate the feasibility of the proposed DVAS. Analysis of the results shows that the proposed method can augment the effective application of SRMs in EVs.

This paper presents a novel integrated control framework that includes simultaneous vehicle lateral stabilization and path tracking by considering the combined-slip effect, wheel dynamics, and tire force nonlinearities for driver-assistance systems. Active front steering and brakes are the actuators of the developed slip-aware receding horizon control system, in which the loss of cornering forces caused by longitudinal slip dynamics is considered in the prediction model. The controller monitors tire capacities and normal forces, corrects driver’s input, and adjusts wheel torques and steering to provide safe performance in path tracking (e.g., lane-keeping) while stabilizing the vehicle within its handling limits. The main advantage of the developed slip-aware driver-assistance controller is the ability in handling multi-actuation vehicle systems through its more accurate prediction model. The performance and computational efficiency of the integrated strategy is evaluated in hardware-in-the-loop real-time experiments, in various pure- and combined-slip maneuvers, under different road friction conditions. Stability of the controller and longitudinal tire force observer is also investigated. The real-time experiments and simulations reveal that the proposed control framework outperforms the existing algorithms in dealing with reduced tire capacities in harsh maneuvers as a consequence of simultaneous vehicle and wheel stabilization, and path tracking.

This paper presents novel algorithms for vibration control of the in-wheel motor (IWM) driven electric vehicles to improve its ride comfort and reduce IWM vibration. A quarter vehicle model is first developed based on a dynamic vibration absorbing structure (DVAS) driven by a switched reluctance motor (SRM). This model considers the coupled longitudinal-vertical dynamics and the unknown road profile as well as the unbalanced electromagnetic force induced by the SRM are treated as the excitation. The dynamics and boundary models of two commercially available semi-active dampers are then presented, which are used as the actuators of both the suspension and the DVAS structure. Based on the developed model, a hybrid controller with a hybrid acceleration driven damping algorithms is proposed to reduce the vibration of the sprung mass and the SRM. The controller parameters are subsequently determined by solving the multi-objective optimization problem with a multi-objective evolutionary optimization method. Numerical simulation results for random road and bumpy excitations are analyzed, and multi-body simulation is finally performed to validate the feasibility of the proposed controllers. Results indicate that the proposed hybrid controllers can effectively improve ride comfort and reduce the SRM vibration compared with the traditional suspension system with IWM.

This paper addresses the path-tracking performance improvement of unmanned surface vessels (USVs) through a novel adaptive immersion and invariance (I&I) based robust adaptive control algorithm. The substantial drawback related to the control of USVs is the uncertain dynamics of the vessel, and unexpected environmental disturbances due to surges or crosswinds that promote the instability. The asymptotic stability of the closed-loop USV system is guaranteed based on the I&I stability theory, and the adaptation laws for the uncertain parameters are derived accordingly. The design variables of I&I algorithm are varied in a convex optimization problem with constraints on the control inputs. The robustness of the proposed algorithm is further assessed for the target USV subject to uncertain hydrodynamic damping in surge, sway and yaw. The performance of the proposed control algorithm is tested against benchmark schemes of disturbance observer-based composite nonlinear feedback (DO-CNF) and robust LQR (RLQR). The results are suggestive of the improved performance of the USV to follow the intended trajectory while guaranteeing optimality in the sense of actuation constraints.

To improve tracked vehicle ride comfort and minimize weapon's vibration, a coordinated control strategy is developed for tracked vehicles' semi-active suspension systems. A model with eight degrees-of-freedom for a tracked vehicle equipped with magnetorheological dampers is established, and is followed by the formulation of a sliding mode controller. The proposed control algorithm is a localized-based controller that can change its target location in the tracked vehicle to where it is needed most. A co-simulation system model including a six-wheel tracked vehicle multi-body dynamics model, coordinated control strategy, and magnetorheological damper force allocator is developed to analyze the ride performance under bump and random road excitations. The simulation results demonstrate that the proposed strategy is very effective in improving the vehicle's ride performance and is much better than the traditional skyhook controllers. The innovation of this paper can be concluded as the coordinated control strategy can simultaneously improve vertical acceleration and pitch acceleration for the hull, which is of great importance for combat situations.