Model-based adaptive locomotion and clustering control of microparticles through ultrasonic topological charge modulation

We present a novel motion control technique for microrobot clusters to exploit the characteristics of the ultrasonic field.The method comprises two steps,i.e.,introducing an ultrasonic actuation(UA)linear model for three-dimensional(3D)locomotion and controlling the topological charge(TC)in the ultrasonic vortex for microrobot clustering.Here,the TC is a controllable parameter for the expansion and contraction of the pressure null space inside the vortex.We present a TC control method to cluster sporadically distributed microrobots in a specific workspace.To validate the concept,a UA system composed of 30 ultrasonic transducers with 1 MHz frequency is fabricated,and the characteristics of the generated acoustic pressure field are analyzed through simulations.Subsequently,the performances of the adaptive controller for precise 3D locomotion and the TC control method for clustering are evaluated.Finally,the UA technology,which performs both clustering and locomotion in a complex manner,is validated with a gelatin phantom in an in-vitro environment.

A Discrete-Time Traffic and Topology Adaptive Routing Algorithm for LEO Satellite Networks

“Minimizing path delay” is one of the challenges in low Earth orbit (LEO) satellite network routing algo-rithms. Many authors focus on propagation delays with the distance vector but ignore the status information and processing delays of inter-satellite links. For this purpose, a new discrete-time traffic and topology adap-tive routing (DT-TTAR) algorithm is proposed in this paper. This routing algorithm incorporates both inher-ent dynamics of network topology and variations of traffic load in inter-satellite links. The next hop decision is made by the adaptive link cost metric, depending on arrival rates, time slots and locations of source-destination pairs. Through comprehensive analysis, we derive computation formulas of the main per-formance indexes. Meanwhile, the performances are evaluated through a set of simulations, and compared with other static and adaptive routing mechanisms as a reference. The results show that the proposed DT-TTAR algorithm has better performance of end-to-end delay than other algorithms, especially in high traffic areas.

An adaptive method for high-resolution topology design

For the purpose of achieving high-resolution optimal solutions this paper proposes a nodal design variablebased adaptive method for topology optimization of continuum structures. The analysis mesh-independent density field, interpolated by the nodal design variables at a given set of density points, is adaptively refined/coarsened accord- ing to a criterion regarding the gray-scale measure of local regions. New density points are added into the gray regions and redundant ones are removed from the regions occupied by purely solid/void phases for decreasing the number of de- sign variables. A penalization factor adaptivity technique is employed-to prevent premature convergence of the optimiza- tion iterations. Such an adaptive scheme not only improves the structural boundary description quality, but also allows for sufficient further topological evolution of the structural layout in higher adaptivity levels and thus essentially enables high-resolution solutions. Moreover, compared with the case with uniformly and finely distributed density points, the proposed adaptive method can achieve a higher numerical efficiency of the optimization process.

Topology Optimization of Stiffener Layout Design for Box Type Load-Bearing Component under Thermo-Mechanical Coupling

The structure optimization design under thermo-mechanical coupling is a difficult problem in the topology optimization field.An adaptive growth algorithm has become a more effective approach for structural topology optimization.This paper proposed a topology optimization method by an adaptive growth algorithm for the stiffener layout design of box type load-bearing components under thermo-mechanical coupling.Based on the stiffness diffusion theory,both the load stiffness matrix and the heat conduction stiffness matrix of the stiffener are spread at the same time to make sure the stiffener grows freely and obtain an optimal stiffener layout design.Meanwhile,the objectives of optimization are the minimization of strain energy and thermal compliance of the whole structure,and thermo-mechanical coupling is considered.Numerical studies for square shells clearly show the effectiveness of the proposed method for stiffener layout optimization under thermo-mechanical coupling.Finally,the method is applied to optimize the stiffener layout of box type load-bearing component of themachining center.The optimization results show that both the structural deformation and temperature of the load-bearing component with the growth stiffener layout,which are optimized by the adaptive growth algorithm,are less than the stiffener layout of shape‘#’stiffener layout.It provides a new solution approach for stiffener layout optimization design of box type load-bearing components under thermo-mechanical coupling.

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network.This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms.The efficiency of the sensor node is energy bounded,acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors.Network management plays a significant role in wireless sensor networks,which was obsessed with the factors like the reliability of the network,resource management,energy-efficient routing,and scalability of services.The topology of the wireless sensor networks acts dri-ven factor for network efficiency which can be effectively maintained by perform-ing the clustering process effectively.More solutions and clustering algorithms have been offered by various researchers,but the concern of reduced efficiency in the routing process and network management still exists.This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks.The memetic algorithm employs a local searching methodology to mitigate the premature convergence,while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain(i.e.,)best cluster head in the wireless sensor networks.The proposed hybrid algorithm is compared with the state of art clus-tering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time,energy consumption,throughput,etc.

TOPOLOGY OPTIMIZATION OF TRUSS STRUCTURE WITH FUNDAMENTAL FREQUENCY AND FREQUENCY DOMAIN DYNAMIC RESPONSE CONSTRAINTS

In this paper, adaptive genetic algorithm （AGA） is applied to topology optimization of truss structure with frequency domain excitations. The optimization constraints include fundamental frequency, displacement responses under force excitations and acceleration responses under foundation acceleration excitations. The roulette wheel selection operator, adaptive crossover and mutation operators are used as genetic operators. Some heuristic strategies are put forward to direct the deletion of the extra bars and nodes on truss structures. Three examples demonstrate that the proposed method can yield the optimum structure form and the lightest weight of the given ground structure while satisfying dynamic response constraints.

Topology identification for a class of complex dynamical networks using output variables

A problem of topology identification for complex dynamical networks is investigated in this paper. An adaptive observer is proposed to identify the topology of a complex dynamical networks based on the Lyapunov stability theory. Here the output of the network and the states of the observer are used to construct the updating law of the topology such that the communication resources from the network to its observer are saved. Some convergent criteria of the adaptive observer are derived in the form of linear inequality matrices. Several numerical examples are shown to demonstrate the effectiveness of the proposed observer.

Distributed Fault-Tolerant Containment Control for Nonlinear Multi-Agent Systems Under Directed Network Topology via Hierarchical Approach

This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network topology.The proposed control framework which is independent on the global information about the communication topology consists of two layers.Different from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault propagation.Based on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower layer.Finally,simulation results are given to demonstrate the effectiveness of the proposed control protocol.

Parameter Estimation and Topology Identification of Uncertain General Fractional-order Complex Dynamical Networks with Time Delay

Complex networks have attracted much attention from various fields of sciences and engineering in recent years.However, many complex networks have various uncertain information, such as unknown or uncertain system parameters and topological structure, which greatly affects the system dynamics.Thus, the parameter estimation and structure identification problem has theoretical and practical importance for uncertain complex dynamical networks. This paper investigates identification of unknown system parameters and network topologies in uncertain fractional-order complex network with time delays(including coupling delay and node delay). Based on the stability theorem of fractional-order differential system and the adaptive control technique, a novel and general method is proposed to address this challenge. Finally two representative examples are given to verify the effectiveness of the proposed approach.

An Adaptive Non-Linear Map and Its Application

A novel adaptive non-linear mapping （ANLM）, integrating an adaptive mapping error （AME） with a chaosgenetic algorithm （CGA） including chaotic variable, was proposed to overcome the deficiencies of non-linear mapping （NLM）. The value of AME weight factor is determined according to the relative deviation square of distance between the two mapping points and the corresponding original objects distance. The larger the relative deviation square between two distances is, the larger the value of the corresponding weight factor is. Due to chaotic mapping operator, the evolutional process of CGA makes the individuals of subgenerations distributed ergodieally in the defined space and circumvents the premature of the individuals of subgenerations. The comparison results demonstrated that the whole performance of CGA is better than that of traditional genetic algorithm. Furthermore, a typical example of mapping eight-dimenslonal olive oil samples onto two-dimensional plane was employed to verify the effectiveness of ANLM. The results showed that the topology-preserving map obtained by ANLM can well represent the classification of original objects and is much better than that obtained by NLM.

Partial Topology Identification of Stochastic Multi-Weighted Complex Networks Based on Graph-Theoretic Method and Adaptive Synchronization

This article aims to identify the partial topological structures of delayed complex network.Based on the drive-response concept,a more universal model,which includes nonlinear couplings,stochastic perturbations and multi-weights,is considered into drive-response networks.Different from previous methods,we obtain identification criteria by combining graph-theoretic method and adaptive synchronization.After that,the partial topological structures of stochastic multi-weighted complex networks with or without time delays can be identified successfully.Moreover,response network can reach synchronization with drive network.Ultimately,the effectiveness of the proposed theoretical results is validated through numerical simulations.

面向任务的无人飞行器自组网OLSR协议

无人飞行器(unmanned aerial vehicle,UAV)自组网的路由研究多以性能指标出发、忽略无人飞行器网络的任务驱动性,与实际需求动态耦合弱、适用性不强。针对该问题基于无人飞行器多任务网络提出了面向任务的无人飞行器联盟组网架构,提出了无人飞行器联盟的任务自适应优化链路状态路由协议(task adaptive optimized link state routing,TA-OLSR)。基于模糊逻辑设计拓扑稳定度计算方法,利用拓扑稳定度实现TA-OLSR控制消息的自适应广播,同时结合稳定度设计新的多点中继选择策略。仿真结果表明,TA-OLSR算法能从宏观面向任务的角度出发,实现不同任务下的良好自适应性,提升数据包投递率,减少冗余信息传播,降低网络开销,有效提高整体网络性能。

一种基于拓扑结构变化的主动配电网自适应保护方法

随着逆变型分布式电源大量接入配电网,配电网运行方式灵活多变,保护方案的设计与保护装置的动作范围受拓扑变化的影响。在此背景下并结合工程实际,提出一种基于拓扑结构变化的主动配电网自适应保护方法。通过多点量测信息感知配电网的运行状态,利用电压量和电流量之间的相关性,对拓扑结构的变化进行识别。然后,根据拓扑结构的变化,形成新的关联矩阵,自适应调整系统的保护方案与参数。最后,通过仿真验证了所提出的方法有效性。

层次多叉树映射布尔体廓后验误差的自适应层切

在给定构建方向下,层切厚度影响熔融沉积成型零件表面的阶梯效应,层切轮廓及其层次关系是路径规划的研究基础.为此,提出基于布尔体廓运算的自适应层切方法,在零件模型不同高度层切面上构建局部轮廓拓扑并计算内核域,根据内核点连线与轮廓环的交点,将轮廓环数据映射到一维坐标轴,基于拓扑映射判定轮廓环层次关系.建立层次关系多叉树,以树高映射体廓特征变化的布尔运算,根据层变阈值设计自适应层厚,通过体廓后验误差估计任意层厚的体积误差,验证自适应层切的有效性.实例打印及不同自适应层切方法对比结果表明,采用体廓后验误差计算的层切体积误差精度比牙尖高度法的更高;通过控制体廓特征部位的层厚来减小体积误差,有助于提高模型特征部位的表面成型精度.

自适应材料插值在柔顺常力机构非线性拓扑优化中的应用

为解决柔顺常力机构的非线性拓扑优化中存在的数值不稳定问题,对非线性拓扑优化的材料插值方法进行了研究。首先,根据常力机构的工作特点以及输出特性,分别构建了其非线性拓扑优化的设计域以及优化模型;然后,对数值不稳定问题产生的原因展开了讨论,从控制应变以避免数值不稳定问题的角度出发,提出了一种新的材料插值模型,模型中引入了参数α,对低密度单元抗变形能力进行了统一调节,并根据最大等效应变给出了一种参数α的自适应调整策略;最后,基于提出的材料插值模型,推导了常力机构优化模型的目标函数敏度表达式,对不同常力区间的常力机构进行了优化,分别对优化结果以及优化效率进行了讨论;并搭建了实验平台,对优化得到的常力机构的实际输出性能进行了验证。实验结果显示:该方法优化得到的常力机构在特定的常力区间内,最大输出力误差为2.3%。研究结果表明:自适应材料插值方法有效地避免了数值不稳定问题,稳定性得到了提升,且优化效率相比于附加超弹材料方法得到了提升。

基于改进四叉树和比例边界有限元法的自适应设计域拓扑优化方法

针对大型结构拓扑优化计算成本高和固体各向同性材料惩罚模型(SIMP)在优化后结构边界处求解精度低的问题,提出了一种基于SIMP法的自适应设计域(ADD)拓扑优化方法。将改进四叉树法应用在拓扑优化的过程中,通过自动划分不同等级的网格单元来减少网格数量、减轻计算负担并提高边界处求解精度;采用比例边界有限元法(SBFEM)实时计算划分后结构的有限元信息,解决了不同等级网格间悬挂节点的问题。所提方法可在初始网格相对较少的情况下得到更加精确的结果,大幅度地降低了计算成本。数值算例结果表明,所提方法在最终结构边界处精度相同的情况下,计算时间最快可缩短为原来的1/100,可以为后续降低大型结构拓扑优化的计算成本提供参考。

基于变更设计的数据驱动产品供应链系统预测控制

针对突发事件导致的产品供应链系统设计变更问题,设计一种基于数据驱动的预测控制算法,使产品生产满足用户的不确定需求。首先,根据产品供应链系统各部分之间的协作关系,将系统建模为多智能体系统,供应链系统设计变更描述为多智能体系统的拓扑结构切换。其次,设计了一种分布式无模型自适应预测控制方法,使供应链系统中的生产率水平能够满足用户需求。再次,引入了未来时刻的输入输出,算法设计可以使产品供应链系统预测未来一段时间内生产满足的库存水平。最后,对算法的稳定性进行了分析,并通过数值模拟仿真验证了所提出算法的有效性。结果表明,该方法可以有效控制产品供应链系统的生产供应,使得变更发生后的生产水平满足市场需求。

基于和声搜索遗传算法的桁架结构拓扑优化

为改善传统设计理念和遗传算法优化不足,促进桁架结构离散变量拓扑优化发展与创新,将遗传算法与和声搜索算法混合,同时对遗传交叉和变异分3种情况进行自适应改进,建立了用于桁架结构拓扑优化的新型混合遗传算法——和声搜索遗传算法,利用该方法分别对平面桁架和空间桁架结构进行拓扑优化分析,并与改进遗传算法、拟满应力遗传算法、相对差商法、复合形遗传算法和改进蚁群算法比较,证明了此方法是有效、可行的。