The process of water delivery and distribution in irrigation districts requires the coordinated operation of check gates and turn-out gates. The Integrator-Delay (ID) model is a widely used canal control model, assuming that offtakes are located at the downstream end of canal pools. However, previous studies have often analyzed water delivery and distribution separately, and the assumptions of the ID model fail to reflect the actual distribution of most open-canal offtakes. To address these issues, this paper establishes a coupled model for optimal control and water distribution in canal systems. Firstly, an Optimized Integrator-Delay (OID) model is proposed to more accurately represent the dynamic impact of offtake locations on water level variations. Model Predictive Controllers (MPCs) are then designed based on both the OID model and the ID model for performance comparison. Secondly, to evaluate the applicability and control performance of the two models when coupled with the canal system optimization water distribution model, three irrigation scenarios are defined: (1) prioritizing the backwater area, followed by the uniform flow area; (2) prioritizing the uniform flow area, followed by the backwater area; and (3) random irrigation. Control performance metrics are used to assess the stability of water levels, flow rates, and gate adjustments under the two controllers. Water delivery and distribution strategies are formulated for various scenarios and applied in the Bojili Irrigation District. The results show that， compared to the ID model, the OID model achieves maximum improvements in water level, flow rate, and gate opening control stability by 8.81 %, 16.47 %, and 7.06 %, respectively. The coupled model provides effective target water levels, water distribution schemes, and scheduling schemes for the three scenarios. It significantly reduces the frequency and magnitude of gate adjustments, minimizes water shortages and abandonment, and enhances system efficiency and resilience against complex demands and disturbances.

Open canals are a common water transfer method used in water transfer projects and agricultural irrigation and drainage projects. With the emergence of drawbacks in traditional canal control models and the increasingly severe shortage of water resources, accurate transport and distribution of water in the canal system of the irrigation district, rational allocation of water resources, reduction in water loss, improvement in the efficiency and benefit of water resource utilization, and satisfaction of the water demand of different water users are needed. Many scholars have conducted extensive research on canal operation control and optimal scheduling. This paper systematically reviews and summarizes the relevant research progress, including the theory of unsteady flow in open canals, the operation mode of the canal system, the operation control model and algorithm of canal systems, and the optimization of water distribution in canal systems. By summarizing the research progress already achieved, the existing problems and future development directions are identified according to actual needs, providing a reference for the ongoing modernization of irrigation districts and the research and application of digital twin irrigation district technology.

With the deployment of vehicle-to-everything(V2X) communication technology, Denial-of-Service(DoS) attacks gradually pose potential threats for the truck platooning cyber-physical systems(TPCPS) due to disruption of information exchange in vehicular networks, resulting in instability of truck platooning and even traffic accidents. Motivated by this, the study proposes a resilient event-triggered control strategy to maintain the performance or stability of the TPCPS when DoS attacks happen. First, a resilient event-triggered mechanism is proposed to ensure that the onboard controller can receive and update status information in time after attack intervals, mitigating effect of the vehicle-to-vehicle(V2V) communication disruptions. Subsequently, the sufficient condition is derived which is to confine DoS attacks and makes a key role in maintaining the platoon's internal stability. To guarantee the consensus control performance of the TPCPS, the switched event- triggered controller is designed by the Lyapunov approach. The controller is expected to output corresponding control based on the updated status information in communication interval. Ultimately, the proposed strategy's effectiveness is validated through simulations. The proposed resilient event-triggered control strategy is shown to be able to effectively mitigate abnormalities in the TPCPS under DoS attacks, thus ensuring safe and comfortable driving. Compared with event-triggered sliding mode control, the proposed method achieves smaller inter-vehicle distances while ensuring stability, enhancing traffic efficiency.

This paper investigates an improved adaptive sliding mode fault-tolerant control strategy for a magnetorheological semi-active suspension system with parametric uncertainties and actuator faults. Using the experimental data collected by a quarter-vehicle test rig, an adaptive-network-based fuzzy inference system is employed to establish a learning-based magnetorheological damper model firstly. The Takagi-Sugeno fuzzy approach is introduced to deal with the uncertainties of sprung mass and pitch rotary inertia and then the corresponding Takagi-Sugeno faulty semi-active suspension system is constructed. An adaptive sliding mode fault-tolerant controller is proposed, in which the magnetorheological damper fault gain is observed by the designed estimation law, and the asymptotical stability of the system is further analyzed. Finally, numerical simulation tests are conducted to demonstrate the effectiveness of the designed control scheme.

This paper presents a constrained hybrid optimal model predictive control method for the mobile energy storage system of Intelligent Electric Vehicle. A novel adaptive cruise control system is designed to optimize mobile energy storage management, active safety control, and fuel economy. A hierarchical control structure is proposed for active safety control and energy flow management. The main-loop is proposed to analyze and optimize active cruise safety control and energy management index using non-linear constrained hybrid optimal model predictive control method. The inner loop is used to chase the aim signal from the main loop using hysteresis current control method. Then, an electronic longitudinal control system is designed to avoid the collision and optimize energy management between the IEV and cruise following vehicles. At last, the simulations with typical driving conditions are built to justify the performance of the proposed controller. The results illustrate that the IEV with the designed hybrid controller can adaptively tracking the following vehicles, reduce the possibility of collision with optimal energy flow management.

In order to improve the lateral stability of steer-by-wire（SbW） vehicles, a hierarchical control strategy is proposed for SbW systems, in which the high-level control scheme consists of a tire cornering stiffness estimator and an active front steering（AFS） controller, and the low-level contains a steering angle tracking controller（SATC）. When the SbW vehicles are driven stably at a very small steering angle, the high-level control scheme will firstly estimate the front and rear tire cornering stiffness coefficients. Then, during the SbW vehicles’ steering process, the AFS controller will combine the estimated tire cornering stiffness and the 2-DOF linear single track vehicle dynamics model to derive the yaw rate under steady state, which is considered as the desired yaw rate, corresponding to the front wheel steering angle input by driver. The front wheel steering angle input by driver will be compensated by AFS in real time to make the SbW vehicles quickly track the desired yaw rate, and the compensated front wheel steering angle will be accurately tracked by the motor controlled by the SATC.

With the fast development of driving automation technologies, user psychological acceptance of driving automation has become one of the major obstacles to the adoption of the driving automation technology. The most basic function of a passenger car is to transport passengers or drivers to their destinations safely and comfortably. Thus, the design of the driving automation should not just guarantee the safety of vehicle operation but also ensure occupant subjective level of comfort. Hence this paper proposes a local path planning algorithm for obstacle avoidance with occupant subjective feelings considered. Firstly, turning and obstacle avoidance conditions are designed, and four classifiers in machine learning are used to respectively establish subjective and objective evaluation models that link the objective vehicle dynamics parameters and occupant subjective confidence. Then, two potential fields are established based on the artificial potential field, reflecting the psychological feeling of drivers on obstacles and road boundaries. Accordingly, a path planning algorithm and a path tracking algorithm are designed respectively based on model predictive control, and the psychological safety boundary and the optimal classifier are used as part of cost functions. Finally, co-simulations of MATLAB/Simulink and CarSim are carried out. The results confirm the effectiveness of the proposed control algorithm, which can avoid obstacles satisfactorily and improve the psychological feeling of occupants effectively.

Lateral control has been the most vital problem for vehicle control, especially in the context of automated driving. This paper presents a novel lateral control approach based on the concept of flow guidance for automated vehicles. With pre-planned digitized desired path, a convergent flow field of reference velocity vector is generated. Variable preview distance is designed to increase both stability and path-tracking performance based on lateral deviation, lateral acceleration limit, and longitudinal speed reference. Then a feedforward + feedback steering control architecture is adopted to follow the reference velocity vector. Meanwhile, vehicle state estimation of position and velocity is designed for lateral control implementation. Simulations in MATLAB/Simulink and IPG CarMaker are designed to examine the effectiveness of the proposed control approach and comparisons are carried out with a state-of-the-art standard driver model. Results show that the proposed lateral can achieve satisfactory path-tracking performance while maintaining vehicle stability.

为有效提升汽车防侧倾性能,针对基于开关磁阻电机和谐波齿轮减速器的电机式汽车主动横向稳定杆设计了模型预测控制器.首先,采用Maxwell/RMxprt对开关磁阻电机进行有限元分析计算得到电机的非线性数学模型;然后,建立整车侧倾动力学模型,设计了前、后悬架横向稳定杆的模型预测控制器,计算前、后悬架的防侧倾力矩;最后,通过Car-sim和Simulink联合仿真试验,分析影响防侧倾性能的控制器敏感参数.研究结果表明:模型预测控制能够显著抑制车身侧倾角,预测时域和性能加权系数是控制器的敏感参数.

A general predictive controller based on the subspace model identification method is proposed for vehicle stabilization. Traditional predictive controllers are always developed based on the principle model of vehicles, which inevitably suffers from parameter uncertainty and poor adaptability. In contrast to that, the proposed subspace-based general predictive controller is realized by a data-driven process and presents good adaptability in vehicle stability control. Inspired by subspace-based predictor construction, the keys of the predictive controller are as follows: (1) system model identification according to the model structure of the control object by input and output data; (2) output prediction of the system by the identified model; and (3) optimal control law designed by combining the linear–quadratic–Gaussian index with the predictive output. The main problem in the controller development lies in the recursive estimation of relevant matrices, which is limited by the subspace model identification theory. The implementation of the vector autoregressive with exogenous input model and the propagator method in subspace identification algorithm effectively solves the problem of estimation accuracy and calculation efficiency. Combined with a linear–quadratic–Gaussian index function, the predictive law for vehicle stability control is derived in detail. Finally, based on the vehicle model validated by standard road test, the effectiveness and robustness of the predictive controller are proved through the numerical simulations of various maneuvers under different road adhesive conditions.

This paper proposes a novel adaptive hierarchical control approach for Steer-by-Wire (SbW) vehicles to improve the handling stability. The high-level stability control scheme contains a variable steering ratio (VSR) strategy based on the adaptive-network-based fuzzy inference system (ANFIS) and an active front steering (AFS) controller designed with the integral sliding mode method by tracking the expected yaw rate, in which the desired front wheel angle is generated to enhance the cornering stability performance. Besides, an adaptive tracking controller (ATC) for the SbW system is designed by using the adaptive sliding mode control method to achieve desired steering performance in the lower level. The proposed adaptive control strategy is validated with different driving circles from ISO standards in simulation tests and hardware-in-the-loop (HiL) experiments. The results demonstrate that the designed control approach improve the vehicle handling stability significantly, even in some extreme driving conditions.

With the increasing of vehicle volume and driving speed, traffic accidents and environmental safety have become social concerns. Vehicle traffic accidents, especially multi-vehicle chain accidents, cause damage to property and human lives. Meanwhile, traffic pollution will lead to continuous harm to living environment and health. This is a coupled human-vehicle-environment interaction system, which is difficult to model with traditional mathematical methods. Parallel theory is an effective method to solve such complex problems based on advanced artificial intelligence and computer technology. In this paper, a parallel system is built to analyze and control multi-intelligent connected vehicle based on parallel theory. The parallel system is also used to analyze and assess the exhaust emission of multi-intelligent connected vehicle. The parallel system is carried out with three steps: 1) modeling and representation of multi-intelligent connected vehicle system using artificial societies; 2) analysis and evaluation by computational experiments; 3) control, management and exhaust emission evaluation through parallel execution of real and artificial systems and big data. The parallel control methods, models and conclusions obtained from this paper can be used to enhance the experience of safety in multi-vehicle control under vehicle to everything environment and make the safety intervention measures more efficient.

作为智能汽车感知融合中的重要组成部分,图像采集信息平台的稳定性对图像采集效果至关重要.针对车载相机等灵敏元件对车载图像信息采集装置减振平台的需求,结合实际对结构体质量和动行程等条件约束,所设计的智能车图像信息采集装置减振平台通过调节模型参数以适应在低频区间的减振需求.将路面噪声通过车辆悬架的减振后的输出作为最终输入,通过对所建立的减振试验平台进行调参,研究结构参数对平台减振性能的影响,尤其是在低频区减振效果.通过调节相关系统参数能较好的抑制悬架减振后的低频振动.为了进一步优化减振系统性能且增强低频抑制效果,在Stewart平台基础上又引入惯容器元件,通过仿真分析进一步考察平台设计及优化方案.

Shortening inter-vehicle distance can increase traffic throughput on roads for increasing volume of vehicles. In the process, traffic accidents occur more frequently, especially for multi-car accidents. Furthermore, it is difficult for drivers to drive safely under such complex driving conditions. This article investigates multi-vehicle longitudinal collision avoidance issue under such traffic conditions based on the Advanced Emergency Braking System (AEBS). AEBS is used to avoid collisions or mitigate the impact during critical situations by applying brake automatically. Hierarchical multi-vehicle longitudinal collision avoidance controller is proposed to guarantee safety of multi-cars using Vehicle-to-Infrastructure (V2I) communication capability in addition to radar for longitudinal vehicle control. High-level controller is designed to ensure safety of multi-cars and optimize total energy by calculating the target braking force. Vehicle network is used to get the key vehicle-road interaction data and constrained hybrid genetic algorithm (CHGA) is adopted to decouple the vehicle-road interactive system,which can obtain the maximum ground friction through vehicle-road data, and provide key predictive parameters for multi-vehicle safety controller. Lower level non-singular Fractional Terminal Sliding Mode(NFTSM) Controller is built to achieve control goals of high-level controller. Simulations are carried out under typical driving conditions. Results verify that the proposed system in this article can avoid or mitigate the collision risk compared to the vehicle without this system.

针对主动悬架LQR控制策略中性能指标的权重系数依靠经验选取的不足,提出一种改进人工蜂群算法对LQR控制器的权重系数进行优化.对于标准人工蜂群算法存在的收敛速度慢、易陷入局部最优的缺点,在跟随蜂阶段引入三种解搜索策略,并对选择策略进行调整,从而更好地平衡所提出算法的全局搜索能力和局部搜索能力.在MATLAB/Simulink环境中建立主动悬架的1/4车辆模型并进行仿真,通过与传统LQR控制器的结果对比,证明所设计的基于改进人工蜂群算法的LQR控制器能兼顾悬架整体性能的提升,显著改善车辆的行驶平顺性和操纵稳定性.

Normally, autonomous vehicles (AVs) are limited to be widely used in market not only for technical factors, but also psychological reasons. Considering the psychological feelings of drivers during switching manned to unmanned operation modes, an algorithm for avoiding obstacles is designed for AVs by considering driver psychological feelings. A so-called confidence-limit-distance (denoted as CLD) for driver to avoid obstacle is experimentally obtained by a number of real track tests with 100 volunteer test drivers as required to approach the obstacle in a certain way. Based on Artificial Potential Field (APF) method, a road potential field is established accordingly to characterize the information on the real road. To express the different influences of obstacles on the driver's psychological feeling in both longitudinal and lateral directions, a confidence potential field also is established based on a two-dimensional normal distribution combining von Mises distribution. Hence, the second-order Taylor expansions of the road potential field and the confidence potential field are firstly introduced into the cost functions for model predictive control (MPC). The corresponding MPC algorithm used here selects front-wheel steering angle as the control variable to be solved. The CLD and range of sensed vehicle motion state variables are taken as the constraints of the MPC. Co-simulations and Hardware-in-the-Loop (HIL) tests are carried out, showing the effectiveness of designed algorithm, which can be useful in the development and design for Advanced Driving Assistant System (ADAS) and AVs.

基于自动驾驶车辆安全标准ISO/PAS21448 《Road vehicles-Safety of the Intended Functionality》(SOTIF),为探究导致SOTIF中人员操作误用的原因,以驾驶员对自动驾驶系统的信任感受为出发点,提出驾驶员对自动驾驶车辆正确操作指令进行错误干预的原因是驾驶员对控制指令没有信心,并引出了用以表现驾驶员与自动驾驶系统之间人机信任感受的评价指标—信心度.为验证信心度指标的实用性,设计了城市道路中常见的转弯和避障工况,并设计了相应的主观问卷,利用层次分析法确定了问卷中不同问题间的权重.利用机器学习中四种分类的方法,建立联系车辆动力学客观参数与驾驶员主观信心感受的主客观评价模型,并选出最优分类器.结果 表明,基于马氏距离的分类器准确率最高,可以为自动驾驶车辆控制系统的开发与设计提供支持.

In partially automated and conditionally automated vehicles, a part of the work of human drivers is replaced by the system, and the main source of safety risks is no longer system failures, but non-failure risks caused by insufficient system function design. The absence of unreasonable risk due to hazards resulting from functional insufficiencies of the intended functionality or by reasonably foreseeable misuse by persons, is referred to as the Safety Of The Intended Functionality. Drivers have the responsibility to supervise the automated driving system. When they don't agree with the operation behavior of the system, they will interfere with the instructions. However, this may lead to potential risks. In order to discover the causes of human misuse, this paper takes the trust feeling between the driver and the automated driving system as the starting point, and based on the collected data of track test, establishes the evaluation indicator -- degree of confidence to show the trust feeling between the driver and the automated system. Degree of confidence is a comprehensive interpretation of the driver's physical and psychological feelings. In the process of track test, we simultaneously collect the dynamics indicators of the vehicle. After the test, the drivers' driving feeling was evaluated by questionnaire. Then, the relationship between objective indicator and subjective score was established by machine learning method, and the development of evaluation indicator was completed. Finally, this paper optimizes the automatic driving motion planning algorithm based on this indicator, and verifies the effectiveness of the algorithm through simulation.

Accurate recognition of driver braking intensity is of great importance for intelligent braking system. In this paper, the braking intensity is classified into four clusters based on an unsupervised Gaussian mixture model (GMM). Then, the architecture of an adaptive-network-based fuzzy inference system (ANFIS) is proposed for braking intensity prediction. A batch learning rule that combines the recursive least squares and gradient descent method used for training ANFIS is adopted to improve the generalization capability. The training data are collected from a hybrid vehicle under real driving conditions. In addition, co-simulation with the software of MATLAB/Simulink and Hardware-in-the-Loop (HiL) tests for an Electronic-Hydraulic Brake (EHB) system are carried out. In comparison to other typical learning methods, the simulation and experimental results demonstrate the effectiveness and accuracy of the proposed hybrid learning approach for braking intensity recognition in different braking scenarios.

电磁式主动悬架兼具控制精确、系统响应快和节能高效等优点,对悬架运动的控制能力强,可以显著提升车辆的舒适性和操稳性.随着新能源汽车、电控系统以及悬架减振技术的发展,电磁式作动器在汽车悬架系统上的应用开始受到关注.文中对汽车电磁式主动悬架技术的研究和应用现状进行回顾和分析,并对其应用前景进行展望.