A necessary and sufficient stability criterion for networked predictive control systems

Stability of a networked predictive control system subject to network-induced delay and data dropout is investigated in this study. By modeling the closed-loop system as a switched system with an upper-triangular structure, a necessary and sufficient stability criterion is developed. From the criterion, it also can be seen that separation principle holds for networked predictive control systems. A numerical example is provided to confirm the validity and effectiveness of the obtained results.     

Adaptive key SURF feature extraction and application in unmanned vehicle dynamic object recognition

A new method based on adaptive Hessian matrix threshold of finding key SRUF( speeded up robust features) features is proposed and is applied to an unmanned vehicle for its dynamic object recognition and guided navigation. First,the object recognition algorithm based on SURF feature matching for unmanned vehicle guided navigation is introduced. Then,the standard local invariant feature extraction algorithm SRUF is analyzed,the Hessian Metrix is especially discussed,and a method of adaptive Hessian threshold is proposed which is based on correct matching point pairs threshold feedback under a close loop frame. At last,different dynamic object recognition experiments under different weather light conditions are discussed. The experimental result shows that the key SURF feature abstract algorithm and the dynamic object recognition method can be used for unmanned vehicle systems.     

Statistical learning makes the hybridization of particle swarm and differential evolution more efficient—A novel hybrid optimizer

This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality. Supported by the National Natural Science Foundation of China (Grant No. 60374069), and the Foundation of the Key Laboratory of Complex Systems and Intelligent Science, Institute of Automation, Chinese Academy of Sciences (Grant No. 20060104)     

Robust anti-sliding control of autonomous vehicles in presence of lateral disturbances

Path following control problem of autonomous vehicles is investigated, concerning both unmeasurable sliding effects and lateral disturbances which lead to some difficulties in designing autonomous control under complex environment. To deal with the sliding effects, sideslip angles are modeled and reconstructed by estimating the tire cornering stiffness, which plays important role in analyzing the sliding effects. To this end, a Luenberger-type observer is designed, which is able to identify the tire cornering stiffness adaptively even in presence of time-varying lateral disturbances. Furthermore, to guarantee high-precision guidance, a sliding mode controller is designed based on chained system theory, and this controller is shown to be robust to both the lateral disturbances and the inaccuracy of the sliding reconstruction. Simulations illustrate that the proposed methods can reconstruct the sliding angles and provide high-accuracy anti-sliding control even in presence of the time-varying lateral disturbances.     

电动舵机的复合自适应非奇异终端滑模控制

非奇异终端滑模控制（NTSMc）能够实现误差的有限时间收敛,但一般NTSMC的控制律采用高增益项来消除系统不确定性的影响,具有较强的保守性．为了降低这种保守性,本文采用复合白适应律对系统的不确定参数进行估计,按照估计值设计复合自适应非奇异终端滑模控制（CANTSMC）,对不确定参数引起的系统动态进行补偿．本方案曾经应用于电动舵机,以补偿不确定参数对模型动态性能的影响．本文证明了闭环稳定性,以及输出跟踪误差在有限时间内的收敛性．通过仿真验证了该方法的有效性．     

Trajectory optimization under kinematical constraints for moving target search

Various recent events in the Mediterranean sea have shown the enormous importance of maritime search-and-rescue missions. By reducing the time to find floating victims, the number of casualties can be reduced. A major improvement can be achieved by employing unmanned aerial systems for autonomous search missions. In this context, the need for efficient search trajectory planning methods arises. Existing approaches either consider K-step-lookahead optimization without accounting for kinematics of fixed-wing platforms or propose a suboptimal myopic method. A few approaches consider both aspects, however only applicable to stationary target search. The contribution of this article consists of a novel method for Markovian target search-trajectory optimization. This is a unified method for fixed-wing and rotary-wing platforms, taking kinematical constraints into account. It can be classified as K-step-lookahead planning method, which allows for anticipation to the estimated future position and motion of the target. The method consists of a mixed integer linear program that optimizes the cumulative probability of detection. We show the applicability and effectiveness in computational experiments for three types of moving targets: diffusing, conditionally deterministic, and Markovian. This approach is the first K-step-lookahead method for Markovian target search under kinematical constraints.     

面向动态场景去模糊的对偶学习生成对抗网络

针对动态场景下的图像去模糊问题,提出一种对偶学习生成对抗网络(dual learning generative adversarial network, DLGAN),该网络可以在对偶学习的训练模式下使用非成对的模糊图像和清晰图像进行图像去模糊计算,不再要求训练图像集合必须由模糊图像与其对应的清晰图像成对组合而成. DLGAN利用去模糊任务与重模糊任务之间的对偶性建立反馈信号,并使用这个信号约束去模糊任务和重模糊任务从两个不同的方向互相学习和更新,直到收敛.实验结果表明,在结构相似度和可视化评估方面, DLGAN与9种使用成对数据集训练的图像去模糊方法相比具有更好的性能.     

采用温控镂空发热网的热像仪与3D激光雷达配准

在热像仪与3D 激光雷达组合感知系统上,对基于特征点的配准问题进行了研究遥结合热像仪与3D 激光雷达的工作特性,设计制作了温控镂空发热网配准靶,可同时为热像仪与3D 激光雷达提供特征点遥红外图像特征点使用Harris 角点探测器进行采集曰为减小混合像素和激光点稀疏的影响,对配准靶平面进行了拟合并对点云进行了配准平面符合度检查,确定了深度图边缘曰使用计算角点附近深度边缘均值的方法提取深度特征点坐标,并对坐标进行了修正曰最后使用NMSM-EM 优化方法对配准结果进行了优化遥基于以上研究成果,使组合感知系统能够在微光条件下完成对移动机器人行驶环境的感知遥     

Model and Simulation of Multipath Error in DLL for GPS Receiver

Although different multipath error models of Delay lock loop （DLL） used in GPS receiver are established, they have never been put together for comparison. Furthermore, no universal simulation method is developed to get a fair comparison among these models. A new model with implicate expression is hence proposed for the coherent DLL and the noncoherent Dot-product （DOT） power mode DLL. Meanwhile, a new simulation method based on the anonymous function in Matlab, which is especially suitable for models with implicit expressions is also proposed to compare the new model with the existing ones. The theoretical analysis and simulation results show that the existing models are the special case of the proposed one. The new simulation method can be used for the comparison of different multipath error models and the multipath error analysis of other DLLs for which only the implicit model is available.