一种船队编队控制的backstepping 方法

针对多船舶之间的协同合作问题,对船舶的编队控制进行了研究.通过运用领航者-跟随者方法,选择在Cartesian坐标系下建立新的船队编队控制模型,基于这种模型,利用反步技术和李亚普诺夫理论设计了一种可使船队按期望队形航行的船队编队控制器.通过考虑领队船舶与跟随船舶的航向角误差,保证了跟随船舶航向角的稳定性,从而避免其在航行过程中不断振荡.最后对所设计的控制方法的正确性及有效性进行了仿真验证.     

扰动作用下的多移动机器人编队模型预测控制

针对多移动机器人编队中的领航者和跟随者同时受系统内部未建模动态和风向、路面平整度等内外扰动影响而带来的无法保持编队队形的问题,提出了基于扩张状态观测器(ESO)的模型预测(MPC)编队控制方法。首先,建立带有扰动项的领航跟随编队模型,然后分别设计ESO对领航者和跟随者所受扰动进行估计,将该编队模型进行线性化离散化处理作为MPC编队控制器的预测模型,在预测输出方程中引入对扰动的估计结果,最后通过滚动优化求解最优控制律,实现对多移动机器人的鲁棒编队控制。仿真实验结果验证了所提出的控制方法的有效性。     

电驱移动机器人多变量固定时间连续编队控制

针对扰动下电驱动非完整移动机器人固定时间编队控制问题,通过引入包含驱动器动力学的领航者-跟随者状态空间动力学模型,分两步对编队控制器进行了设计。对领航者跟随者编队运动学模型进行了多变量固定时间控制设计。在动力学层面,为实现扰动下的速度跟踪,通过辅助输入设计了一种跟随者机器人多变量超螺旋固定时间连续电压控制器。所提算法使机器人编队克服了跟随者机器人所受干扰,确保了跟随者机器人与领航者在固定时间达到期望队形,跟随者在固定时间内跟随期望速度,设计的连续控制消除了开关控制的抖振现象。通过参数设计提前给定系统收敛的固定时间,与系统初始状态无关。基于Lyapunov方法进行了系统稳定性分析。通过仿真对算法进行了验证。     

基于领航位置信息的AUV三维编队控制方法

研究了自治水下机器人（Autonomous Underwater Vehicle,AUV）三维环境中编队控制问题,应用领航一跟随式队形控制方法,仅利用领航者的位置信息及期望编队队形得到虚拟机器人的航行轨迹及速度信息,作为跟随者的航行参考量,应用反步及滑模控制方法为跟随者设计自适应控制律,使其轨迹收敛于虚拟机器人的轨迹,从而与领航者保持期望位姿关系。随后,在具体AUV动力学模型上,利用MATLAB／SIMULINK平台进行了编队控制的仿真研究,实现了预期的控制效果,验证了算法的有效性及实用性。     

异构集群系统分布式自适应输出时变编队跟踪控制

提出了一种能够解决高阶异构集群系统输出时变编队跟踪问题的控制方法. 集群系统中的智能体分为领导者和跟随者, 领导者和跟随者的动力学模型可以完全不同. 跟随者的输出在跟踪领导者输出的同时保持时变编队实现协同运动. 考虑了领导者存在已知或未知控制输入、领导者和跟随者均存在未知扰动、有向通信拓扑存在切换等多种因素并存的情况, 结合观测器理论、自适应控制理论和滑模控制理论设计了完全分布式的输出时变编队跟踪控制协议, 摆脱了对领导者控制输入上界值、与通信拓扑相关的拉普拉斯矩阵的特征值以及时变编队函数等全局信息的依赖. 利用Lyapunov理论证明了在有向拓扑切换条件下异构集群系统的闭环稳定性. 最后通过数值仿真对理论结果的有效性进行了验证.     

基于改进的领导-跟随者编队算法研究

针对领导-跟随者算法的不足,提出了基于先驱者的多机器人编队控制方法.先驱者采用SLAM方法规划编队行进轨迹的关键点,领导者则根据关键点和环境信息,调整运动轨迹,并计算与跟随者间的相互距离和夹角,决定跟随者的实时位姿.当编队队形遇障不能通过时,采用基于逻辑层次的变换策略进行队形变换.同时在Player-Stage仿真平台上,对算法进行了比较和分析,实验结果表明,改进的领导-跟随者算法能更好的保证编队队形的完整性和编队系统的稳定性,控制效果较好.     

领航跟随法和势函数组合的船舶编队控制

多船舶作业在海上越来越盛行,最常见的运行方式就是船舶编队协控,本文考虑领航跟随法过于集中控制的不足,将船舶编队分为领航–跟随控制阶段和跟随–跟随控制两个阶段.在第1个阶段使用多输入滑模控制以及船舶响应模型进行控制律分配;第2阶段引入势函数方法,结合图形拓扑来约束跟随船舶间的间距,提高队形的稳固性,弥补单一领航跟随法的不足,并通过Lyapunov函数验证系统控制的稳定性.后期通过直线和曲线路径的仿真验证,得到编队船舶的各状态历时趋势,船舶间状态最终达到一致性,实现队形保持的目标,得出良好效果,验证了算法的有效性.     

移动机器人编队的递归模糊神经网络滑模控制

研究非完整移动机器人编队控制优化问题,由于动态模型存在诸多不稳定性,针对领航者-跟随者l-ψ控制结构,提出了一种Back stepping运动学控制器与自适应神经滑模控制器相结合的新型控制策略.采用动态递归模糊神经网络(dynam-ic recurrent fuzzy neural network,DRFNN)对跟随者及领航者动力学非线性不确定部分进行在线估计,并通过自适应鲁棒控制器对神经网络建模误差进行补偿.所提方法不但解决了移动机器人编队控制的参数与非参数不确定性问题,同时也确保了机器人编队在期望队形下对指定轨迹的跟踪;根据Lyapunov方法的设计过程,保证了控制系统的稳定;仿真结果表明了改进方法对机器人编队优化控制的有效性.     

任意初始位置机器人领航跟随型迭代学习编队

针对移动机器人编队形成与队形保持问题,提出了一种适用于任意初始位置条件下的迭代学习编队控制算法。采用领航-跟随型编队法,仅利用领航者的运动轨迹和期望的编队队形推导出跟随者的参考航迹,引入迭代学习控制（Iterative Learning Control,ILC）方法,设计跟随者的控制律,使跟随者随着每次迭代调节自身的线速度和角速度,与领航者一起以期望编队队形工作;引入对初始位置的学习,即同时进行编队队形的学习和编队初始位置的学习。解决了任意初始位置的多移动机器人形成并保持期望编队队形的问题。并在理论上分析了控制算法的可行性,仿真结果验证了控制算法的有效性。     

基于二阶一致性算法的多仿生机器鱼分布式编队控制

针对动态领航者按照自身动力学模型运动, 多个跟随者机器鱼以其为编队参考点, 根据编队要求形成队形并整体跟随领航者运动的问题, 提出一种多仿生机器鱼分布式编队控制方案. 首先, 基于二阶一致性算法给出各跟随者机器鱼估计领航者位姿信息的分布式算法;其次,给出以领航者为参考点的多仿生机器鱼编队描述方法,进而各机器鱼根据编队要求以所估得的参考点信息实时确定其在编队中的期望位姿; 再次, 各跟随者机器鱼以期望速度和角速度以及所估得的领航者位姿信息为输入, 利用模糊控制器确定其速度档位和方向档位, 实现编队的形成与保持. 仿真和实验结果均表明, 所提分布式编队控制方法是有效的, 仿生机器鱼群体能够较快形成期望队形并跟随领航者游动.     

基于离散航迹点的舰船航行轨迹仿真估计

最近,航海技术发展迅速,在贸易、军事、科研等众多领域起到越来越重要的作用. 人们对于舰船航行的规划、仿真、监控与跟踪等方面的研究更细致、更全面. 本文主要研究基于舰船航行中跟踪到的稀疏航迹点的航线估计问题,用于态势图的仿真显示. 为了估计出通过所有航迹点、合理、平滑的航线,我们提出了由粗到细的航线估计算法,该算法主要包含三个层级. 提出的算法使用数据量较小的经验航海线路图和全球海陆分布图,这两种图的数据获取容易,并且可实现较快速的航线估计. 大量实验证明,提出的由粗到细的层级算法能较好的完成航线的估计. 同时,提出的算法也可以用于舰船航行前的航线规划. 算法仍然存在一些问题和不足,例如没有考虑海洋水深等问题,这将是我们下一步的研究内容.     

欠约束多机器人并联协调吊运系统运动控制

针对吊运过程中如何协调控制各机器人以实现负载高精度快速运动问题,采用了一种基于动力学模型前馈补偿+PD反馈的欠约束多机并联协调吊运系统轨迹跟踪控制方法。利用位置几何关系进行了逆运动学分析,并采用拉格朗日方程建立了系统的逆动力学模型。考虑到模型不确定性及外界扰动,采用前馈补偿+PD反馈控制方法进行轨迹跟踪控制。为了使被吊运物的轨迹跟踪控制更加快速准确,采用遗传算法对PD参数进行优化。理论分析和数值仿真结果表明,控制方法实现简单,能够快速有效地跟踪被吊运物的运动轨迹,满足被吊运物轨迹跟踪精度的要求。     

A new bounded jerk on-line trajectory planning for mimicking human movements in robot-aided neurorehabilitation

In this paper we propose a new on-line control strategy that can generate motion primitives mimicking human movement for robot assistance in stroke neurorehabilitation. The proposed strategy, with respect to other methods, allows the generation of bounded jerk trajectories characterized by inter-joint synchronization, e.g. joint variables complete the same percentage of their trajectories at each instant of time.The algorithm can on-line automatically identify, localize and track target objects to be reached, and adapt the level of assistance to be provided to the patient, so that the robot assistance is provided to let the patient operate in a real world setting, where he/she can reach and grasp common everyday life objects,To evaluate the performance of the proposed algorithm, its implementation was derived to control the movement of an upper limb robotic exoskeleton, the L-Exos, and an experimental evaluation was conducted in a group of healthy subjects to assess the plausibility of generated trajectories in terms of similarity with human motion.     

面向网络攻击下弹性均值趋同的智能体群组编队

本文研究通信范围有限的智能体群组编队问题, 探索网络攻击下多智能体系统弹性均值趋同控制策略. 现 有的工作表明, 多智能体系统可通过维持一个所谓r–鲁棒的通信网络, 实现分布式弹性趋同控制器的设计. 然而, 传 统的方法中只有当单个智能体的通信范围足够大时, 才能使网络满足r–鲁棒这一特定条件. 本文利用智能体可移动 的特性放宽了对通信范围的要求, 通过小组化和模块化的设计思想以及相应的编队策略, 让智能体沿预设轨道做周 期性运动, 从而达成具有r–鲁棒的通信网络, 并基于该通信网络提出了一种分布式弹性均值趋同控制方法. 此外, 分 析给出了单模块和多模块情况下多智能体网络达成r–鲁棒的所需条件. 最后在仿真实例中, 实现了网络攻击下系统 状态的均值趋同, 实验结果验证了群组编队和均值趋同控制方法的有效性.     

MPC-based torque control of permanent magnet synchronous motor for electric vehicles via switching optimization

In order to effectively achieve torque demand in electric vehicles (EVs), this paper presents a torque control strategy based on model predictive control (MPC) for permanent magnet synchronous motor (PMSM) driven by a two-level three-phase inverter. A centralized control strategy is established in the MPC framework to track the torque demand and reduce energy loss, by directly optimizing the switch states of inverter. To fast determine the optimal control sequence in predictive process, a searching tree is built to look for optimal inputs by dynamic programming (DP) algorithm on the basis of the principle of optimality. Then we design a pruning method to check the candidate inputs that can enter the next predictive loop in order to decrease the computational burden of evaluation of input sequences. Finally, the simulation results on different conditions indicate that the proposed strategy can achieve a tradeoff between control performance and computational efficiency.     

A study on bunker fuel management for the shipping liner services

In this paper, we consider a bunker fuel management strategy study for a single shipping liner service. The bunker fuel management strategy includes three components: bunkering ports selection (where to bunker), bunkering amounts determination (how much to bunker) and ship speeds adjustment (how to adjust the ship speeds along the service route). As these three components are interrelated, it is necessary to optimize them jointly in order to obtain an optimal bunker fuel management strategy for a single shipping liner service. As an appropriate model representing the relationship between bunker fuel consumption rate and ship speed is important in the bunker fuel management strategy, we first study in detail this empirical relationship. We find that the relationship can be different for different sizes of containerships and provide an empirical model to express this relationship for different sizes of containerships based on real data obtained from a shipping company. We further highlight the importance of using the appropriate consumption rate model in the bunker fuel management strategy as using a wrong or aggregated model can result in inferior or suboptimal strategies. We then develop a planning level model to determine the optimal bunker fuel management strategy, i.e. optimal bunkering ports, bunkering amounts and ship speeds, so as to minimize total bunker fuel related cost for a single shipping liner service. Based on the optimization model, we study the effects of port arrival time windows, bunker fuel prices, ship bunker fuel capacity and skipping port options on the bunker fuel management strategy of a single shipping liner service. We finally provide some insights obtained from two case studies.     

Slot co-chartering and capacity deployment optimization of liner alliances in containerized maritime logistics industry

With the growing traffic of containerized shipping worldwide, container liners have seen increasing cooperation. Slot co-chartering has drawn wide attention as a way of cooperation within liner alliances. However, liners face the issue of finding proper measures to optimize alliance operations. In response to this, this paper comprehensively considers shipping capacity dispatch for containerized shipping companies within liner alliances. First, we explained the general rules of liner shipping dispatch and analyzed the interfering factors. Second, we created a liner shipping capacity dispatch model without consideration of slot co-chartering costs and a liner shipping capacity dispatch model with consideration of slot co-chartering costs and proposed a new column generation algorithm to solve both problems. Third, we tested our algorithm in a real case of a large Chinese containerized shipping company, which belongs to Ocean Alliance, and an optimal ship dispatch strategy on relevant routes and a decision on optimal slot chartering can be reached. Computational results indicate that the proposed column generation algorithm exhibits effective and efficient performance for large-scale slot co-chartering in liner shipping.     

Energy-efficient real-time object tracking in multi-level sensor networks by mining and predicting movement patterns

A number of studies have been written on sensor networks in the past few years due to their wide range of potential applications. Object tracking is an important topic in sensor networks; and the limited power of sensor nodes presents numerous challenges to researchers. Previous studies of energy conservation in sensor networks have considered object movement behavior to be random. However, in some applications, the movement behavior of an object is often based on certain underlying events instead of randomness completely. Moreover, few studies have considered the real-time issue in addition to the energy saving problem for object tracking in sensor networks. In this paper, we propose a novel strategy named multi-level object tracking strategy (MLOT) for energy-efficient and real-time tracking of the moving objects in sensor networks by mining the movement log. In MLOT, we first conduct hierarchical clustering to form a hierarchical model of the sensor nodes. Second, the movement logs of the moving objects are analyzed by a data mining algorithm to obtain the movement patterns, which are then used to predict the next position of a moving object. We use the multi-level structure to represent the hierarchical relations among sensor nodes so as to achieve the goal of keeping track of moving objects in a real-time manner. Through experimental evaluation of various simulated conditions, the proposed method is shown to deliver excellent performance in terms of both energy efficiency and timeliness.     

目标感知事件驱动的滑行道冲突控制策略

为避免机场滑行道发生冲突,减轻管制员负荷,提出一种基于传感器网络的滑行道冲突控制策略.根据滑行道运行过程,利用Petri网建立传感器网络条件下的滑行道动态模型,并定义了滑行道运行控制规范,将滑行道冲突避免问题转化为状态禁止问题。针对状态禁止问题利用局部关联矩阵方法和逻辑互斥方法设计控制器,并提出基于变迁使能状态决策灯光控制指令的方法,利用助航灯光实现滑行道冲突避免和自动引导。最后通过实验仿真验证控制策略的有效性。     

定向钻具姿态的双线性补偿控制策略

在地质钻进过程中,钻具运动具有复杂非线性特性,因而很难有效实现钻具姿态的控制.对此,提出一种面向定向钻具姿态的双线性补偿控制策略,以实现对非线性钻具运动的准确控制.首先,通过分析定向钻具运动特性,建立定向钻具的运动模型,针对运动模型存在非线性和耦合特性,利用泰勒展开法和双线性逼近变换技术对钻具运动模型进行变换;然后,运用双线性补偿控制思想扩展局部线性控制范围和优化控制效果.仿真结果表明,所提出的控制策略能够简化定向钻具控制过程,提高定向钻具控制的准确性,具有较好的稳定性.