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The driver authority decision for driver‐initiated takeover is closely related to vehicle driving safety. In this paper, a lateral driver‐automation driver authority decision method considering the safety of the intended functionality is proposed to improve vehicle driving safety. Based on systems‐theoretic processes analysis, functional safety analysis of driver initiative takeover is carried out to clarify the safety of the intended functionality issues caused by the unreasonable decision. Then, a method for defining the safe driving area is developed to assess the safety level of the driving area around the ego vehicle. Taking the driver‐vehicle state parameters and vehicle–vehicle state parameters as input, a deep neural network model is constructed to determine the intention of the driver to take over the vehicle. The lateral driver‐automation driver authority decision method is designed to make the driver‐machine take‐over decisions, which considers the safety of the intended functionality. The effectiveness of the proposed method is evaluated via numerical simulation and hardware‐in‐the‐loop experiments. The results show that the designed method not only improves the control ability of the driver to the maximum extent but also integrates the perception ability of the driver into the vehicle control system to improve further vehicle driving safety. [ABSTRACT FROM AUTHOR]

The control goal of the vehicle platoon is to maintain the same speed and desired distance. Most current studies are based on simplified vehicle models, and the leader's state is also rarely considered. However, under complex working conditions, such as low adhesion or curves, the lateral stability of the platoon will be difficult to guarantee, and tracking errors of desired speed and spacing may further increase. To solve the above problems, a new hierarchical coordinated control strategy is proposed. Taking distributed drive electric vehicles (DDEVs) as research objects, the upper control level establishes a stability situation assessment model according to the vehicle's dynamic characteristics. At the medium control level, variable weight model predictive control (MPC) coordinates conflicts between longitudinal tracking and lateral stability. A correction term is also introduced to revise the prediction model. At the same time, the weight of different control objectives of the leader and following vehicle was adjusted, respectively. Torque distribution is carried out at the lower level controller. Finally, the control strategy is tested on a hardware-in-the-loop (HIL) platform. The results show that the proposed control strategy can ensure lateral stability while improving the tracking performance of the vehicle platoon. [ABSTRACT FROM AUTHOR]

This paper presents a fault-tolerant human-machine shared scheme for collision avoidance considering driver error and actuator fault. A fault-tolerant method of trajectory planning and decision-making is developed for dealing with the actuator fault. We calculate the remainder trajectory tracking capability of the faulted actuator and utilise the motion redundancy in the system to generate feasible fault-tolerant trajectories for collision avoidance. In addition, an assessment criterion of driver error is conducted to supervise the driver's steering response to the potential collision and actuator fault. The shared system dynamically adjusts the driving authority based on the driver error. A model predictive control (MPC) algorithm is employed for tracking the fault-tolerant trajectory. Simulation and hardware-in-the-loop (HIL) experiment results demonstrate the effectiveness of the presented approach, reducing traffic accidents due to driver error and actuator fault. [ABSTRACT FROM AUTHOR]

The problem of inaccurate results was presented based on irregular shell finite element analysis using traditional modeling methods,a finite element modeling method was presented based on simplified of transmission components. The paper introduced the bearings,gears and fasteners of simplified model method. For example,finite element analysis of automobile main reducer shell based on finite element software environment. Two finite element analysis models were established based on traditional method and simplify component method; Comparing simulation results of the two models found: stress distribution on the overall trend of automobile main reducer Shell was the same,but the value of the local area to install the bearing stress positions were quite different. Stress tests show in 100 N. m,200 N. m and 400 N. m of input torque main reducer: Simplified model approach closer to actual value of test results,verify the correctness and accuracy of the method.

Appropriate vehicle lateral stability control is the key to ensure vehicle driving safety, whereas accurate lateral stability recognition can help improve the performance of vehicle control. In this article, the vehicle stability recognition and coordinated control are studied. Firstly, the vehicle dynamic model is established, through vehicle simulation software Carsim, the attribute dataset representing the vehicle lateral stability is further obtained. Then, clustering by fast search and find of density peaks method (CFSFDP) based procedure for the classification of the lateral stability as ‘Absolutely stability’, ‘Nearly stability’ and ‘Hardly stability’ is applied. The brain emotional learning network combined with genetic algorithm (GA-BEL) model is used to train datasets to recognize vehicle stability categories during driving. For the different recognition results, a coordinated control strategy based on active front steering control (AFS) and direct yaw moment control (DYC) is designed finally. Through the Carsim/Simulink co-simulations and Hardware-in-the-Loop tests under several typical driving conditions, the superiorities of the stability recognition method and the coordinated control strategy proposed in this paper are verified. [ABSTRACT FROM AUTHOR]

Model predictive control is one of the main methods used in path tracking for autonomous vehicles. To improve the path tracking performance of the vehicle, a path tracking method based on model predictive control with variable predictive horizon is proposed in this paper. Based on the designed model predictive controller for path tracking, the response analysis of path tracking control system under the different predictive horizons is carried out to clarify the influence of predictive horizon on path tracking accuracy, driving comfort and real-time of the control algorithm. Then, taking the lateral offset, the steering frequency and the real-time of the control algorithm as comprehensive performance indexes, the particle swarm optimization algorithm is designed to realize the adaptive optimization for the predictive horizon. The effectiveness of the proposed method is evaluated via numerical simulation based on Simulink/CarSim and hardware-in-the-loop experiment on an autonomous driving simulator. The obtained results show that the optimized predictive horizon can adapt to the different driving environment, and the proposed path tracking method has good comprehensive performance in terms of path tracking accuracy of the vehicle, driving comfort and real-time. [ABSTRACT FROM AUTHOR]

At present, the lane changing technology for a single driving environment has been relatively mature, but the actual situations are much more complicated, such as the changing curvature and the changes in road conditions. In this paper, a new lane changing obstacle avoidance control strategy for the variable curvature road was proposed. It has two stages including path planning for variable curvature lane changing and inverse system decoupling (ISD) control for path tracking. The first stage established the lane changing path models for the variable curvature road. A longitudinal-lateral safe distance model was proposed to constrain the safe boundary of the lane changing path. The second stage proposed a dynamic decoupling method for longitudinal and lateral motion based on the inverse system decoupling, a direct yaw moment and active front wheel steering coordinated control method was designed. The inverse system decoupling algorithm can correct the single point preview (SPP) method to improve the stability and the path tracking accuracy in the lane changing obstacle avoidance process. The strategy was simulated by CarSim/Simulink co-simulation, and the experiments were carried out on the hardware-in-the-loop platform. The results show that the proposed control strategy can effectively avoid obstacles when the vehicle is driving on the variable curvature road. Besides, for the different road conditions and the strong non-linearity generated during the lane changing process, the control strategy can reduce the tracking error by a maximum of 32.7%, both the yaw rate and side slip angle can be controlled in a smaller range. [ABSTRACT FROM AUTHOR]

This paper proposes a novel dynamic path planning and path following control method for collision avoidance, which works based on an improved piecewise affine tire model. The main contribution of this work is the design of a dynamic path planning method based on model predictive control, where it replans a maneuverable path to avoid moving obstacle in real time. A hierarchical control framework contains a high-level path replanning model predictive control and a low-level path following model predictive control. A collision avoidance cost function in the high hierarchies was designed to calculate the relative dynamic distance, which copes with the sudden obstacle. Moreover, the replanning path is the optimized output according to reference trajectory, obstacle, and handling stability. The control objective of the low hierarchies is to accurately track the replanning path, especially for the increased nonlinearity of large tire sideslip angle. For this reason, an improved piecewise affine tire model is designed and used for model predictive control to improve the path following performance and reduce calculated burden. The main improvement of the piecewise affine tire model is that the varied lateral stiffness coefficients adapt to the change of the tire sideslip angle in different tire regions. Based on the CarSim and Simulink platform, the dynamic path planning and path following simulations are designed to test the proposed method. The simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a new curve-lane recognition algorithm is proposed. The algorithm uses edge point curvature voting to determine the region of interest based on near-vision straight-lane information. First, information is detected in the near-vision area regarding the straight lines to the left and right of the current lane. Near-vision lane-line extraction includes lane image filtering, as well as edge detection of the region of interest below the vanishing line. The vanishing point is positioned by determining the position of the edge point and distribution of the direction angle. In addition, the straight line is extracted based on the position of the vanishing point. The straight lines that are constructed for the current lane in this way are selected and used as supplementation, in combination with the lane model. Next, the road curvature range isometry is divided into multiple subdivision regions. The near-vision lane straight-line curvature parameters extending from each edge point in the region of interest are computed by combining the straight-line near-vision lane information with the curve lane model in the pixel coordinate system. Subsequently, voting and counting are carried out for the curvature regions of each edge point to which the corresponding curvature computing values belong. Finally, the counting maximum from the corresponding curvature regions of the straight lines located to the left and right of the current lane are searched for, and the curvature region is converted, to obtain the lane line corresponding to the curvature parameter values. Experimental results indicate that the proposed curve-lane recognition algorithm can effectively detect the curve lanes of different curvatures. The results also indicate that the proposed curve detection method is highly accurate, and the algorithm is very robust in different environments. [ABSTRACT FROM AUTHOR]

Byline: Wuwei Chen, Changbao Chu A Layered Coordinated Control (LCC) strategy for chassis system that is divided into upper and bottom layer control parts is proposed. The bottom controllers are made up of three separate controllers, i.e. suspension, steering and braking systems. Each of them is used to carry out different control tasks and to achieve performance indices of subsystems. The Upper Coordinated Controller receives information coming from chassis system and modifies the decision-making of bottom controllers, aiming at improving whole vehicle synthesis performance. To demonstrate the performance of our proposed method, it is implemented under the MATLAB/SIMULINK software environment, and the simulation results show that chassis system based on LCC strategy is able to achieve better ride comfort, more security and higher handling stability than that of the commonly used single layered control one.

Byline: Jin Li, Wuwei Chen, Tanbin Wang, Bichu Li An intelligent vehicle must have a proper architecture in order to work autonomously in a complicated and dynamic environment. After considering the advantages and disadvantages of the typical architecture, a hybrid architecture for intelligent vehicle navigation is presented. This architecture uses a blackboard to coordinate interactions between modules, and classifies the activities of the intelligent vehicle into reflecting activity and voluntary activity. The reflecting activity is activated by data and the voluntary activity is selected by the blackboard. The navigation task is accomplished by selecting proper activities to control the vehicle according to the environmental condition and the status of the vehicle. The theoretical analyses, simulation and experiment results show that this architecture has good intelligence and reactivity.

Byline: Wuwei Chen, Hansong Xiao, Liqiang Liu, Jean W. Zu This paper addresses the problem of integrated control of electrical power steering systems (EPS) and active suspension systems (ASS). Through integrating EPS with ASS, a full car dynamic model is established. Based on the integrated model, a random sub-optimal control strategy based on output feedback is designed to fulfil the integrated control of both EPS and ASS. The characteristics of the integrated control system are analysed using Matlab/Simulink and a series of comparisons are made with the system without control and the ASS-only/EPS-only system. The simulation results show that the integrated control scheme can not only enhance the steering quality, but also significantly isolate the road excitation. Moreover, the integrated control system has a great improvement on anti-roll and anti-pitch abilities. The proposed research provides a theoretical solution for simultaneously improving the multiple vehicle performance indices including manoeuvrability, handling stability, ride comfort, and safety.

Byline: Jun Wang, Wenli Xu, Wuwei Chen This paper studies the optimal Hankel-norm reduction (OHNR) of a half-vehicle active suspension model and its application in suspension multiobjective control. Firstly, a numerical algorithm of OHNR is presented explicitly and utilised to reduce the order of the active suspension model. Secondly, the advantages of OHNR are illustrated by comparison with other model-reduction methods. Thirdly, the reduced suspension model created by the OHNR method is used to design a mixed I2/pole-placement controller of reduced order for the suspension. Finally, computer simulations are carried out to demonstrate that the controller based on the reduced model is not only of lower order and easy to implement, but also achieves similar performance to that of a full-order controller.

Byline: Wuwei Chen, Qirui Wang, James K. Mills, Dong Sun As closed-loop performance specifications of dynamic system become more and more stringent, leading to higher and higher performance systems, there exists a need to consider more general design approaches of such systems such as the integrated structure/controller design approach to solve such problems. Integrated structure/controller design approaches applied to vehicle active/semi-active suspension systems typically first considers the design of the mechanical structure parameters, followed by design of the control system. When the structure design and controller design are considered independently, global optimal performance for the system cannot be achieved. This paper presents an approach for the design of both mechanical structure parameters and control parameters of semi-active suspension systems, by integrating the structure and control parameter design based on genetic algorithms (GA) and the H∞ control scheme. The effectiveness of the proposed integrated design method, to improve riding comfort, is demonstrated with theoretical analysis, simulation and experimental results.

Byline: Wuwei Chen, James K. Mills, Le Wu This paper describes a neurofuzzy (NF) adaptive controller in conjunction with a modelling neural network (MNN) applied to a vehicle semi-active suspension control. The NF controller employs a simplified fuzzy algorithm which is based on a multi-layer neural network. The modeling network is used as an estimator which can identify the vehicle dynamic model parameters and provide the NF controller with learning signals. A plant model is used initially to tune the parameters of the NF controller using a set of fuzzy control rules. A semi-active suspension system, utilised for vibration control tests, is modelled in simulation and tested with results obtained from vehicle experiments. Significant dynamic nonlinearity is inherent in the plant due to the suspension dynamics. The NF controller results are compared with a conventional fuzzy logic (FL) controller and an open loop passive suspension system. It is found that the NF controller performance is satisfactory and the resultant reduction of vehicle vibration is improved, in the presence of the dynamic complexities of the plant. The method proposed has several advantages over others presented in the literature. In order to verify simulation results, a test vehicle fitted with a semiactive suspension measurement and control system was experimented with under different vehicle speeds and road surfaces. In addition, a semi-active suspension and a passive suspension were also experimented. The test results coincide closely with those obtained from simulation. These results show that the semi-active suspension methodology exhibits good performance leading to reduction of vehicle vibration.

In-wheel motor drive vehicles have many advantages such as more efficient, energy-saving, large space, and flexible control. But the increase of four-wheel actuators and sensors makes the probability of wheel motor fault increase and brings risk to the safety and reliability of the system. For in-wheel motor drive vehicles, the motor torque output fault happens frequently, which seriously affects the stability and safety of the vehicle. The stability control under the single-motor fault condition is considered in this paper, and the vehicle model with a single-motor fault and the fault diagnosis observer are established. Then, combined with the fault observation, a stability control method is formulated based on the different states of the controlled object of the vehicle. At the stage of low speed and small steering, a torque compensation controller including steady state/feedforward/feedback control is designed for the fault motor based on the triple-step method to ensure the vehicle staying in a stable driving state. At the stage of high-speed, large-steering, or large-torque output, the vehicle stability control based on torque distribution is carried out for the sound wheel, which is composed of a torque tracking controller and a torque optimal distribution controller. The former can make the vehicle get rid of the fault state quickly, and the latter can effectively keep the stability of the vehicle and make the vehicle maintain a certain driving response ability. Finally, the control method proposed in this paper is simulated and tested on the CarSim/Simulink joint simulation platform and the CarSim/LabVIEW hardware-in-loop experiment bench. The results show that after the single-motor fault occurs, the stability control system can effectively suppress the instability of the vehicle caused by the single-motor fault and ensure the stability and safety of the vehicle. [ABSTRACT FROM AUTHOR]

A 3-degree of freedom (DOF) nonlinear model including yaw, lateral, and roll motions was constructed, and a numerical simulation of chaotic behavior was performed using the Lyapunov exponent method. The vehicle motion is complex, manifesting double-periodic, quasi-periodic, and chaotic phases, which negatively affects the vehicle lateral stability. To control this chaotic behavior, a controller was designed based on the sliding mode variable structure control (SM-VSC) method. To decrease chattering and further improve lateral stability of the vehicle under extreme operating conditions, the adaptive power reaching law was realized by using a fuzzy control method. The performance of the SM-VSC system was simulated by using Matlab/simulink. The simulation results including the uncontrol, SM-VSC control, and adaptive-reaching SM-VSC control were compared, which demonstrated that the adaptive-reaching SM-VSC control method is more effective in suppressing the chaotic phase of the vehicle lateral motion. The approach proposed in this paper can significantly improve a vehicle's lateral stability under extreme operating conditions. [ABSTRACT FROM AUTHOR]

Estimating vehicle sideslip angle and road friction in real time is of great significance for vehicle stability control and intelligent vehicle lateral control. These parameters are often difficult to obtain directly and the high cost of measuring instruments restricts their application in general vehicle control. Therefore, an estimation method based on online gradient descent (OGD) algorithm for vehicle sideslip angle and road friction is proposed. For the front-wheel-steer and front-wheel-drive vehicle, the vehicle's lateral dynamics model is established with smaller assumptions. And an unknown input observer is designed to estimate the tire's lateral force of the rear wheel. On the basis of this, the parameter estimation is transformed into the parameter optimization problem and the cost function is designed by using the nonlinear tire model, i.e., magic formula, and its gradient formula. Then, the OGD algorithm is used to estimate the sideslip angle and road friction, respectively. The effectiveness of the proposed method is evaluated via numerical simulation based on MATLAB/Simulink and CarSim software platform. The results show that the method can reliably and accurately estimate the vehicle sideslip angle and the road friction under a variety of test conditions. The proposed algorithm can effectively suppress the influences of sensor noise, longitudinal velocity change, and tire force nonlinearity on the estimation results. [ABSTRACT FROM AUTHOR]

To make improvements on vision-based lane departure warning systems (LDWS), a lane departure prediction (LDP) method based on Monte-Carlo simulation and deep Fourier neural network (DFNN) is proposed. Firstly, a closed-loop driver-vehicle-road (DVR) system model is built up and the parameters of the system, consisting of vehicle states, positioning and road conditions, are initialized by random sampling. After simulating a large number of DVR systems with random parameters, the obtained results are used as samples to train a DFNN which predicts the forthcoming maximum lateral deviation and is optimized by employing deep learning method. Then, a LDP strategy is proposed by combining the DFNN with a driver activity index, which takes driver adaptation into consideration. The experimental evaluation shows that the proposed lane departure warning algorithm can predict the lane departure event in time and reduce the false-warning rate of existing methods in a significant way. More importantly, the proposed technique enhances the system's functions of over-speed warning on curved road and over-steer warning on low-adhesion road. [ABSTRACT FROM PUBLISHER]

With the application of active control technology in vehicle, the performances of vehicle such as riding comfort, active safety and handling stability were greatly improved in recent years. However, most of these control systems were aimed to improve individual performance of vehicle respectively. In fact, the improvement of the overall vehicle dynamics performance not only depends on the cooperative work among these various control subsystems but also depends on the coupling interferences of the mechanical structure and control system of vehicle in the running process. In order to remove the coupling interferences between the mechanical structure and control system of vehicle chassis system and to further enhance the overall performance of vehicle, the method of simultaneous optimization of mechanical structure and controller parameters of vehicle chassis system based on Particle Swarm Optimization Algorithm is presented in this paper. According to the fundamental principle of vehicle dynamics, a half car mode of active suspension system and anti-lock braking systems established at first. Then the linear quadratic gauss controller of the active suspension system and sliding mode controller of anti-lock braking system are designed. Taking controllers designed for active suspension system and anti-lock braking system as bottom controllers, the upper coordinated control logic of the systems is put forward and the upper PID coordinated controller is designed on the basis of analyzing the coupling conflict between active suspension system and anti-lock braking system. Finally, a Particle Swarm Optimization Algorithm was adopted for simultaneous optimization of mechanical structure and controller parameters of vehicle chassis integrated control system, because the traditional design method of a vehicle system is always to design control parameters following structure parameters and it can not obtain the global optimal performances for the system. In order to verify the effectiveness of the algorithm, the simultaneous optimization program is developed based on Particle Swarm Optimization Algorithm in MATLAB environment while the mechanical structure and control parameters of chassis control system are set as optimization variables and the overall vehicle dynamics performance is set as objective function. Simulation result shows that the pitching angular acceleration of vehicle is reduced and vehicle riding comfort performance is improved after optimized. Braking distance and dynamic load of front and rear wheels of vehicle are also reduced significantly which indicating vehicle active safety is improved dramatically. The vehicle road test was also carried out based on integrated controller is development of anti-lock braking system and active suspension system using ARM7 when vehicle speed is 40 km/h under braking condition. The road test also shows that the dynamic load of front and rear wheels of vehicle are reduced by 34.20% and 34.10%, braking time and braking distance of vehicle were reduced by 2.31% and 4.50% respectively, the response of vehicle pitching angular acceleration at braking condition is decreased by 15.10% after optimized, and both vehicle active safety and riding comfort are improved at different levels. [ABSTRACT FROM AUTHOR]

In order to investigate the characteristics of air-oil two-phase turbulent flow inside the shell of transfer case, the research method to combine test and numerical simulation is proposed to take analysis. The fluid-structure interaction theory (FSI) is a science to research the interaction and influence between fluid and structure fields, and is applied to research single-direction influencing characteristic from gears rotation to air-oil field flow. The gears of the transfer case transfer power via meshing and rotating, their movement is imitated by dynamic mesh technology is fluent. Fluent UDF definition is used to define the parameters of dynamic mesh control. The fluid field inside the shell of the transfer case has two phases: air and oil. It starts to move and show its fluid characteristics in close relationship with the motion trail of gears. The air-oil fluid field belongs to a two-phase model. It is simulated by the VOF model is fluent. The interface of gas and liquid is defined by fluent UDF definition. High speed photography is the application of advanced photographic technology for high-speed motion characteristics of a clear picture. It is used to observe high-speed motion characteristics. With a transparent shell, the transfer case assembly is installed on the test bench for the observation test. The movement of air-oil fluid field has been observed after high speed photography. The results show that, during 0-0.5T moment, driven gear to rotate with oil plays a main role in the meshing zone lubrication; at 0.25T moment, gears start to mesh and rotate, oil fluid field wave; at 0.375T moment, the gear meshing area is filled with lubricating oil; at 0.5T moment, the continuous oil inflow meshing zone above, and cover the gear meshing area of tooth surface, achieve initial lubrication. The vortex phenomenon appears near the meshing zone in the velocity streamline. The speed near the wall is higher than the other zone. The velocity streamlines into and out of the meshing zone more effectively. After the pressure field stabilizes, the flow field pressure maximum value appeared in the gear meshing area; two phases of flow field in the shell are uniform, the middle of the fluid pressure is relatively small; negative pressure appears below the driven gear part. The numerical simulation results coincide with the test. The analysis results are true and valuable. [ABSTRACT FROM AUTHOR]

A controller for vehicle electronic stability program (ESP) based on direct yaw-moment control (DYC) strategy is designed with nonlinear H∞ feedback control adopted as DYC algorithm. A nonlinear observer is built for accurately estimating the longitudinal and lateral velocities of vehicle. An actuator allocation module is also designed to distribute actuator resources for all sub-controllers to prevent conflict in operation between DYC and ABS or ASR systems and ensuring the normal execution of multi-functions of ESP. An ESP controller is autonomously developed based on processor ARM7 and a platform for hardware-in-the-loop (HIL) test is constructed. The results of HIL test verify the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

A virtual prototype model for four-wheel-steering vehicle is built with ADAMS/Car software and a hybrid control system is designed based on yaw rate feedback and neural network adaptive control. The system effectively solves the nonlinear problem of tire at large sideslip angle, Speeds up and facilitates the on-line correction of weighting coefficients of neural network and endows the system with self-adaptive ability. The results of theoretical analysis and simulation show that the hybrid control system built for four-wheel-steering vehicle is feasible and the ADAMS / MATLAB co-simulation method adopted is effective. [ABSTRACT FROM AUTHOR]

A new type of solenoid-actuated shock absorber with continuously variable damping is studied. Its structural features and working principle are analyzed, the hydraulic model and the mathematic model for compress and rebounce strokes are established and its damping characteristics are simulated. Meanwhile a corresponding test is also conducted on a single-channel electro-hydraulic servo tester. The results of simulation agree well with test data, indicating that the model established for solenoid-actuated shock absorber is relatively accurate and can be used for the research and development of solenoid-actuated shock absorber with continuously variable damping. [ABSTRACT FROM AUTHOR]

Integrated vehicle dynamics control has been an important research topic in the area of vehicle dynamics and control over the past two decades. The aim of integrated vehicle control is to improve the overall vehicle performance including handling, stability, and comfort through creating synergies in the use of sensor information, hardware, and control strategies. This paper proposes a two-layer hierarchical control architecture for integrated control of the active suspension system (ASS) and the electronic stability programme (ESP). The upper-layer controller is designed to coordinate the interactions between the ASS and the ESP. While in the lower layer, the two controllers including the ASS and the ESP are developed independently to achieve their local control objectives. Both a simulation investigation and a hardware-in-the-loop experimental study are performed. Simulation results demonstrate that the proposed hierarchical control system is able to improve the multiple vehicle performance indices including both the ride comfort and the lateral stability, compared with the non-integrated control system. Moreover, the experimental results verify the effectiveness of the design of the hierarchical control system. [ABSTRACT FROM AUTHOR]

This paper presents a new navigation method for an automatic guided vehicle (AGV). This method utilizes a new navigation and control scheme based on searching points on an arc. Safety measure indices are defined and are generated from the output of a fuzzy neural network which define the actions the AGV is to take when in the presence of obstacles. The proposed algorithm integrates several functions required for automatic guided vehicle navigation and tracking control and it exhibits satisfactory performance when maneuvering in complex environments. The automatic guided vehicle with this navigation control system not only can quickly process environmental information, but also can efficiently avoid dynamic or static obstacles, and reach targets safely and reliably. Extensive simulation and experimental results demonstrate the effectiveness and correct behavior of this scheme. © 2004 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Reasonably foreseeable misuse by persons, as a primary aspect of safety of the intended functionality (SOTIF), has a significant effect on cooperation performance for lane keeping. This paper presents a novel human–machine cooperative control scheme with consideration of SOTIF issues caused by driver error. It is challenging to balance lane keeping performance and driving freedom when driver error occurs. A safety evaluation strategy is proposed for safety supervision, containing assessments of driver error and lane departure risk caused by driver error. A dynamic evaluation model of driver error is designed based on a typical driver model in the loop to deal with the uncertainty and variability of driver behavior. Additionally, an extension model is established for determining the cooperation domain. Then, an authority allocation strategy is proposed to generate a dynamic shared authority and achieve an adequate balance between lane keeping performance and driving freedom. Finally, a model predictive control (MPC)-based controller is designed for calculating optimal steering angle, and a steer-by-wheel (SBW) system is employed as an actuator. Numerical simulation tests are conducted on driver error scenarios based on the CarSim and MATLAB/Simulink software platforms. The simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Current research on lane-keeping systems ignores the effect of the driver and external resistance on the accuracy of tracking the lane centerline. To reduce the lateral deviation of the vehicle, a lane-keeping control method based on the fuzzy Takagi-Sugeno (T-S) model is proposed. The method adopts a driver model based on near and far visual angles, and a driver-road-vehicle closed-loop model based on longitudinal nonlinear velocity variation, obtaining the expected assist torque with a robust H∞ controller which is designed based on parallel distributed compensation and linear matrix inequality. Considering the external influences of tire adhesion and aligning torque when the vehicle is steering, a feedforward compensation control is designed. The electric power steering system is adopted as the actuator for lane-keeping, and active steering redressing is realized by a control motor. Simulation results based on Carsim/Simulink and real vehicle test results demonstrate that the method helps to maintain the vehicle in the lane centerline and ensures driving safety. [ABSTRACT FROM AUTHOR]

In this paper, a new extension coordinated controller was proposed for driving stability and handling performance of four wheel independent drive electric vehicles. The proposed controller has three levels. The upper control level uses a new extension coordinated controller to find the weights of a active front wheel steering controller (AFS) and a direct yaw moment controller (DYC). Moreover, considering the different vehicle speed, the road adhesion coefficient and the wheel steering angle, a phase plane method was used to provide the dynamic stability boundary for the switching control strategy of AFS and DYC. The medium control level used the triple-step nonlinear method to calculate the additional front wheel angle and additional yaw moment required by the lower control level. The additional front wheel angle and additional yaw moment was applied to the steering motor and the four wheel drive motor, respectively. In order to obtain the target wheel force, the four wheel drive torque was optimized based on a quadratic programming method. The proposed extension coordinated controller was performed in the CarSim/Simulink co-simulation platform, hardware-in-loop (HIL) and vehicle test. The results showed that the proposed controller can effectively improved both the stability and handling performance. [ABSTRACT FROM AUTHOR]

The anti-lock braking system (ABS) is an essential part in ensuring safe driving in vehicles. The Security of onboard safety systems is very important. In order to monitor the functions of ABS and avoid any malfunction, a model-based methodology with respect to structural analysis is employed in this paper to achieve an efficient fault detection and identification (FDI) system design. The analysis involves five essential steps of SA applied to ABS, which includes critical faults analysis, fault modelling, fault detectability analysis and fault isolability analysis, Minimal Structural Over-determined (MSO) sets selection, and MSO-based residual design. In terms of the four faults in the ABS, they are evaluated to be detectable through performing a structural representation and making the Dulmage-Mendelsohn decomposition with respect to the fault modelling, and then they are proved to be isolable based on the fault isolability matrix via SA. After that, four corresponding residuals are generated directly by a series of suggested equation combinations resulting from four MSO sets. The results generated by numerical simulations show that the proposed FDI system can detect and isolate all the injected faults, which is consistent with the theoretical analysis by SA, and also eventually validated by experimental testing on the vehicle (EcoCAR2) ABS. [ABSTRACT FROM AUTHOR]

The characteristics analysis and decoupling study on the active mount system of powertrain are conducted in this paper. Firstly, the approximate linear model and analytic model for the active mount system of powertrain are set up and verified. Then with the complete response decoupling in torque roll axis direction at idle speed and the rational allocation of natural frequencies as objectives, and using Pareto genetic algorithm, a decoupling optimization on the parameters of the passive part of active mount and the position and stiffness of each mount is performed. Finally the results of optimization are verified by simulation, indicating the effectiveness of decoupling scheme adopted. [ABSTRACT FROM AUTHOR]

In view of the problems of high order and difficulty in engineering implementation of vehicle suspension controller, a 7 DOF active suspension model is built, and with consideration of the sensitive frequency range of human body to vibration, the weighted H∞ controller is designed to ensure the good stability and disturbance rejection performance of closed-loop system. By using minimum information loss method, the order reduction for 20 order active suspension controller is studied. The results of comparison between active suspension closed-loop control systems before and after order reduction in terms of frequency characteristics and ride comfort demonstrate that with the controller order of active suspension system reduced from 20 to 8, the sum of controllable and observable state information loss is less than 50 % while the control performance of controller kept basically similar. [ABSTRACT FROM AUTHOR]