A progressive deployment strategy for cooperative adaptive cruise control to improve traffic dynamics

Cooperative adaptive cruise control(CACC)vehicles are intelligent vehicles that use vehicular ad hoc networks(VANETs)to share trafc information in real time.Previous studies have shown that CACC could have an impact on increasing highway capacities at high market penetration.Since reaching a high CACC market penetration level is not occurring in the near future,this study presents a progressive deployment approach that demonstrates to have a great potential of reducing trafc congestions at low CACC penetration levels.Using a previously developed microscopic trafc simulation model of a freeway with an on-ramp—created to induce perturbations and trigger stop-and-go trafc,the CACC system s efect on the trafc performance is studied.The results show signifcance and indicate the potential of CACC systems to improve trafc characteristics which can be used to reduce trafc congestion.The study shows that the impact of CACC is positive and not only limited to a high market penetration.By giving CACC vehicles priority access to high-occupancy vehicle(HOV)lanes,the highway capacity could be signifcantly improved with a CACC penetration as low as 20%.     

A Progressive Deployment Strategy for Cooperative Adaptive Cruise Control to Improve Trafc Dynamics

Cooperative adaptive cruise control(CACC)vehicles are intelligent vehicles that use vehicular ad hoc networks(VANETs)to share trafc information in real time.Previous studies have shown that CACC could have an impact on increasing highway capacities at high market penetration.Since reaching a high CACC market penetration level is not occurring in the near future,this study presents a progressive deployment approach that demonstrates to have a great potential of reducing trafc congestions at low CACC penetration levels.Using a previously developed microscopic trafc simulation model of a freeway with an on-ramp—created to induce perturbations and trigger stop-and-go trafc,the CACC system s efect on the trafc performance is studied.The results show signifcance and indicate the potential of CACC systems to improve trafc characteristics which can be used to reduce trafc congestion.The study shows that the impact of CACC is positive and not only limited to a high market penetration.By giving CACC vehicles priority access to high-occupancy vehicle(HOV)lanes,the highway capacity could be signifcantly improved with a CACC penetration as low as 20%.     

Adaptive tracking control of an autonomous underwater vehicle

This paper presents the trajectory tracking control of an autonomous underwater vehicle(AUV). To cope with parametric uncertainties owing to the hydrodynamic effect, an adaptive control law is developed for the AUV to track the desired trajectory. This desired state-dependent regressor matrix-based controller provides consistent results under hydrodynamic parametric uncertainties.Stability of the developed controller is verified using the Lyapunov s direct method. Numerical simulations are carried out to study the efficacy of the proposed adaptive controller.     

LBMBS： A Load-Balanced Moveable Bubble Scheme for Tori Interconnection Networks

Moveable bubble flow control is an efficient technique to avoid deadlock for torus networks. However, it does not take into consideration the load balance feature. Therefore, this leads to inefficient resources utilization. Moreover, packets may be discarded due to the congestion of a network area. On the other hand, there are other algorithms that consider load balancing such as rHALB （r hop adaptive load-balanced）routing technique. Nonetheless, rHALB detects deadlock using the timeout threshold. This concept of deadlock detection may give incorrect responses in case the traffic is high. Therefore, packets are dropped although there is no deadlock. At the end, this affects the whole performance of the network. This paper proposes a new routing algorithm for tori interconnection networks, namely, the LBMBS （load-balanced moveable bubble scheme）. LBMBS integrates both concepts of moveable bubble and rHALB in order to provide at the end a load-balanced efficient deadlock-free algorithm. The protocol details are given and compared against other routing algorithms in the literature.     

一种用于目标跟踪的波形自适应选择算法

提出了一种用于雷达目标跟踪的自适应广义调频波形设计算法。该算法根据跟踪器的动态需求,以广义调频信号为样板波形自适应设计下一时刻的发射波形,其目的是最小化预测的目标跟踪均方误差,并假定高信噪比条件,且目标跟踪运动模型和观测模型均为线性,利用与某一波形相对应的克拉美--罗下限（CRLB）以及卡尔曼滤波器,通过最小化预测的跟踪均方误差来实现广义调频波形的自适应设计。仿真结果表明：在信噪比相同的情况下,与使用固定参数、自适应参数的线性调频波形设计算法相比,所提出的算法能够获得更低的目标跟踪均方误差。     

Stochastic Optimal Design for Unknown Linear Discrete‐Time System Zero‐Sum Games in Input‐Output form Under Communication Constraints

In this paper, stochastic optimal strategy for unknown linear discrete‐time system quadratic zero‐sum games in input‐output form with communication imperfections such as network‐induced delays and packet losses, otherwise referred to as networked control system (NCS) zero‐sum games, relating to the H_∞ optimal control problem is solved in a forward‐in‐time manner. First, the linear discrete‐time zero sum state space representation is transformed into a linear NCS in the state space form after incorporating random delays and packet losses and then into the input‐output form. Subsequently, the stochastic optimal approach, referred to as adaptive dynamic programming (ADP), is introduced which estimates the cost or value function to solve the infinite horizon optimal regulation of unknown linear NCS quadratic zero‐sum games in the presence of communication imperfections. The optimal control and worst case disturbance inputs are derived based on the estimated value function in the absence of state measurements. An update law for tuning the unknown parameters of the value function estimator is derived and Lyapunov theory is used to show that all signals are asymptotically stable (AS) and that the estimated control and disturbance signals converge to optimal control and worst case disturbances, respectively. Simulation results are included to verify the theoretical claims.     

基于特征自适应选择的鲁棒跟踪算法

为了解决真实场景下视频目标的跟踪问题,提出一种基于特征自适应选择的鲁棒跟踪算法。首先,针对在线AdaBoost算法特征池特征鲁棒性差的问题,提出一种基于颜色与金字塔梯度方向直方图特征相结合的特征池构造方式;然后,针对分类器在更新过程中容易受到错误样本影响的问题,对每帧跟踪结果增加遮挡检测环节以避免漂移现象的发生。大量的对比实验表明,在真实场景下所提出的方法具有更好的效果。     

分布式多输入系统的自律鲁棒自适应分散控制

为了实现一类分布式多输入系统的分散鲁棒自适应控制, 基于状态扩张和反演干扰抑制控制, 提出一种自律鲁棒自适应分散控制的新方法. 结合直接反馈线性化和最优控制, 给出了自律最优鲁棒自适应分散控制的设计方法. 仿真结果表明, 所提出方法能够有效实现各子系统的自律鲁棒稳定、全系统整体鲁棒稳定和不确定参数自适应, 同时通过求解LMI, 具备了最优干扰抑制的功能.

     

带未知干扰的模块化航天器系统相对轨道的队形控制

基于多智能体系统一致性理论,在有向拓扑结构中对模块化航天器相对轨道的队形控制问题进行研究.考虑与状态相关的未知外部干扰,在存在模块质量不确定性的情形下,基于自适应增益技术,设计仅依赖模块自身及其邻近模块信息的分布式控制算法,并通过Lyapunov稳定性方法证明闭环系统是渐近稳定的.最后在Matlab/Simulink中对6个模块组成的模块化航天器系统的队形进行仿真分析,仿真结果表明所设计的控制律是有效且可行的.     

基于密度调整的改进自适应谱聚类算法

针对谱聚类存在构造相似度矩阵时对尺度参数敏感以及处理多重尺度数据集效果不理想的缺陷, 提出一种基于密度调整的改进自适应谱聚类算法. 该算法将样本点所处领域的密度引入谱聚类, 利用密度差来调整样本点之间的相似度, 使其更符合实际簇类中样本点间的内在关系, 在一定程度上解决了多尺度聚类问题; 同时, 通过样本点的近邻距离自适应得到尺度参数, 使算法对尺度参数相对不敏感. 仿真实验验证了所提出算法的有效性和优越性.