Particle swarm optimization-based algorithm of a symplectic method for robotic dynamics and control

Multibody system dynamics provides a strong tool for the estimation of dynamic performances and the optimization of multisystem robot design. It can be described with differential algebraic equations(DAEs). In this paper, a particle swarm optimization(PSO) method is introduced to solve and control a symplectic multibody system for the first time. It is first combined with the symplectic method to solve problems in uncontrolled and controlled robotic arm systems. It is shown that the results conserve the energy and keep the constraints of the chaotic motion, which demonstrates the efficiency, accuracy, and time-saving ability of the method. To make the system move along the pre-planned path, which is a functional extremum problem, a double-PSO-based instantaneous optimal control is introduced. Examples are performed to test the effectiveness of the double-PSO-based instantaneous optimal control. The results show that the method has high accuracy, a fast convergence speed, and a wide range of applications.All the above verify the immense potential applications of the PSO method in multibody system dynamics.

Dynamic Optimization Method on Electromechanical Coupling System by Exponential Inertia Weight Particle Swarm Algorithm

Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket, the vibration acceleration of tool is taken as objective function, and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations, Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method, exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm（EPSA）, is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation, the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimizati on. Such kind of method can be applied in the design of robots, NC machine, and other electromechanical equipments.

Dynamic Multi-objective Optimization of Chemical Processes Using Modified BareBones MOPSO Algorithm

Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.

Structural optimization strategy of pipe isolation tool by dynamic plugging process analysis

During the pipeline plugging process,both the pipeline and the pipe isolation tool(PIT)will be greatly damaged,due to the violent vibration of the flow field.In this study,it was proposed for the first time to reduce the vibration of the flow field during the plugging process by optimizing the surface structure of the PIT.Firstly,the central composite design(CCD)was used to obtain the optimization schemes,and the drag coefficient and pressure coefficient were proposed to evaluate the degree of flow field changes.Secondly,a series of computational fluid dynamics(CFD)simulations were performed to obtain the drag coefficient and pressure coefficient during dynamic plugging.And the mathematical model of drag coefficient and pressure coefficient with the surface structure of the PIT were established respectively.Then,a modified particle swarm optimization(PSO)was applied to predict the optimal value of the surface structure of the PIT.Finally,an experimental rig was built to verify the effectiveness of the optimization.The results showed that the improved method could reduce the flow field vibration by 49.56%.This study provides a reference for the design of the PIT surface structure for flow field vibration technology.

Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization

In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm.

Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

The design of an antenna requires a careful selection of its parameters to retain the desired performance.However,this task is time-consuming when the traditional approaches are employed,which represents a significant challenge.On the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended performance.In this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial antenna.The proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep network.This optimized network is used to retrieve the metamaterial bandwidth given a set of features.In addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

Interactive Heuristic D* Path Planning Solution Based on PSO for Two-Link Robotic Arm in Dynamic Environment

This paper is devoted to find an intelligent and safe path for two-link robotic arm in dynamic environment. This paper focuses on computational part of motion planning in completely changing dynamic environment at every motion sample domains,?since the local minima and sharp edges are the most common problems in all path planning algorithms. In addition, finding a path solution in a dynamic environment represents a challenge for the robotics researchers,?so in this paper, a proposed mixing approach was suggested to overcome all these obstructions. The proposed approach methodology?for obtaining robot interactive path planning solution in known dynamic environment utilizes?the use of modified heuristic D-star (D*) algorithm based on the full free Cartesian space analysis at each motion sample with the Particle Swarm Optimization (PSO) technique.?Also, a modification on the?D* algorithm has been done to match the dynamic environment requirements by adding stop and return backward cases which is not included in the original D* algorithm theory. The resultant interactive path solution was computed by taking into consideration the time and position changes of the moving obstacles. Furthermore, to insure the enhancement of the?final path length optimality, the PSO technique was used.?The simulation results are given to show the effectiveness of the proposed method.

Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tasks and resources.Compared with the traditional mode,shared manufacturing offers more abundant manufacturing resources and flexible configuration options.This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment,and the characteristics of shared manufacturing resource allocation.The execution of manufacturing tasks,in which candidate manufacturing resources enter or exit at various time nodes,enables the dynamic allocation of manufacturing tasks and resources.Then non-dominated sorting genetic algorithm(NSGA-II)and multi-objective particle swarm optimization(MOPSO)algorithms are designed to solve the model.The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers.In addition,the proposed model’s efficiency,which considers the entries and exits of manufacturing resources in the shared manufacturing environment,is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation.

智能算法的亚群优化策略综述

群智能算法的优化是提升群智能算法性能的一个主要途径,随着群智能算法越来越广泛地运用到各类模型优化、生产调度、路径规划等问题中,对智能算法性能的要求也越来越高。亚群策略作为一种优化群智能算法的重要手段,能够灵活地平衡算法的全局勘探能力和局部开发能力,已经成为群智能算法的研究热点之一。为了促进亚群优化策略的发展和应用,对动态亚群策略、基于主从范式的亚群策略和基于网络结构的亚群策略进行了详细调查,阐述了各类亚群策略的结构特点、改进方式和应用场景。最后,总结了亚群策略目前存在的问题以及未来的研究趋势和发展方向。

考虑系统稳定边界的同步调相机励磁与升压变参数联合优化

现有提升调相机动态无功特性的参数优化方法侧重于电磁参数的优化,这给生产企业带来较高的工艺要求和较大的成本压力。针对该问题提出考虑系统稳定约束的调相机励磁系统及升压变参数联合优化方法,分析其对电磁参数优化的可替代性。首先,推导了基于Park模型下调相机的无功频域特性,与6阶实用模型下的无功频域特性对比,基于调相机的Park模型可提升调相机动态无功特性的分析精度。然后,提出根据调相机并网系统的稳定边界确定参数的优化区间,采用频域灵敏度方法确定重点参数,并基于人工鱼群算法进行参数优化。最后,通过仿真结果表明,励磁系统与升压变参数的联合优化,可获得与仅优化电磁参数时相近的调相机动态无功性能,验证了电磁参数优化的可替代性,从而降低调相机的制造成本,扩大同步调相机的应用场合和范围。

改进粒子群算法的机器人避障偏差控制方法

为针对巡检机器人避障偏差进行良好控制,提升避障效果,提出改进粒子群算法的机器人避障偏差控制方法设计。先分析巡检机器人正向动力学和逆向动力学,获取双走轮坐标系下机器人的运动情况,建立机器人运动学方程。然后以此为基础,在双走轮坐标系中,通过改进粒子群算法确定巡检机器人全局避障最优路径,采用改进人工势场法完成局部避障路径规划,最后采用前馈补偿控制器为动力学方程和最优路径建立动态补偿,根据控制器输出的训练结果,实现巡检机器人避障偏差自动控制。实验结果表明:所提方法避障规划能力及避障运动控制能力均较强,避障偏差控制效果好,具有一定应用价值。

动态价格约束下生鲜无人零售点选址-路径方法研究

“新零售”创新了生鲜无人零售的渠道和销售模式,通过研究网络化无人售货的技术优势,引入在线动态定价机制,解决了生鲜无人销售品控难、客户黏度低的问题,有效减低了系统综合成本;同时构建了一种面向生鲜商品的无人零售的干线和支线混杂配送模型,在多目标求解、问题解耦和PSO全局优化等方面进行突破,建立了一种动态价格约束下的带时间窗选址-路径二级运输模型(2E-dPLRPTW),并进行了案例验算,确认该方法能有效提升生鲜商品无人售卖的效益。

改进粒子群算法的自动充电机械臂时间最优轨迹研究

针对桁架充电机械臂关节空间轨迹规划的时间优化问题,提出了一种非线性动态学习因子的粒子群算法。通过运动学分析获取工作空间,引入3-5-3多项式插值进行轨迹规划。结合运动过程中的速度与加速度约束,寻求运动过程中的最短时间。对比改进粒子群算法和基本粒子群算法的收敛速度,分析各关节优化前后运动时间的变化情况,并进行仿真实验验证。结果表明:改进粒子群算法的收敛性能较基本粒子群算法更快,整体运动时间缩短约33%,证实改进粒子群算法的可行性。

面向用户偏好的动态网页数据交互式查询算法

为提高网页数据查询速度、精度及工作效率,提出一种面向用户偏好的动态网页数据交互式查询算法.首先,构建用户偏好模型,增加偏好组合的演化个体适应性,综合计算适配值;其次,为防止数据冗余和重复,基于兴趣相似性,分离相似度高的查询数据和重复数据,识别出网络数据的性质;最后,利用粒子群优化算法寻找最优的动态网页数据交互式查询方案.实验结果表明:在数据集基数影响下,该算法的查询结果集质量在0.95以上;在查询最大维数影响下,该算法的查询结果集质量在0.96以上,表明其查询使用时间短、结果集精度高、自适应能力强.

面向动态公交的离散分层记忆粒子群优化算法

随着智慧城市、智慧交通的发展,移动互联网和公交智能基础设施以及相关数据的不断完善,通过用户手机预约公交服务的新型公交运营方式——动态公交,已经成为许多城市公交发展的重要探索方向。但目前,对动态公交问题的建模、算法研究不足。基于这一研究现状,提出动态公交问题模型和面向动态公交的离散分层记忆粒子群优化(PSO)算法。首先给出动态公交问题的目标函数和约束条件,给出动态公交问题的解的形式,并定义解的编辑距离;其次提出使用数据驱动的预计算路径集生成PSO算法的优质初始解的方法,给出基于解的编辑距离的PSO算法中粒子的变异概率和自适应收敛系数的计算方式;最后提出将粒子群分层求解的方法,其中低层粒子群可复用、可继承,从而减少单时间片内、时间片间复制和重初始化带来的性能损耗。基于重庆市北碚区蔡家岗街道的真实场景和亿级历史数据建立仿真环境进行实验,实验结果表明:相对于不分层PSO算法,分层PSO算法通过复用和继承能缩短超80%计算用时;自适应参数和变异机制能帮助算法更稳定地收敛到更优解;相对于传统公交系统,动态公交能在同等运力限制下,提高22%的乘客接单率,节省39.1%的乘客出行时间,所提算法能满足公交运营商在片区内进行动态公交调度的需求;相对于对比算法,所提算法平均缩短了85.3%的计算用时,并且在仅耗用80%里程的情况下提高了至少12%的接单率。

基于动态遗忘因子递推最小二乘法和改进粒子滤波算法的锂电池SOC估计

为了提高锂电池荷电状态(SOC)估计的精度,提出了一种基于动态遗忘因子递推最小二乘法和改进粒子滤波算法相结合的锂电池SOC估计方法。针对固定遗忘因子递推最小二乘法在电池参数辨识中难以同时保持快速收敛和稳定性的问题,引入动态遗传因子,以模型辨识值和实际值的残差为变量构建修正公式,实现遗忘因子动态调整。为了改善粒子滤波(PF)的粒子多样性丧失问题,采用白鹭群优化算法(ESOA)对粒子滤波算法进行优化。仿真结果表明,基于动态遗忘因子递推最小二乘法和改进粒子滤波算法的锂电池SOC估计误差始终保持在0.3%以内,平均绝对误差和标准差为0.15%和0.17%,与其他算法相比具有更好的精度和稳定性。

复杂地形风电场微观选址的GA-PSO混合算法研究

提出一种结合改进遗传算法(GA)和粒子群算法(PSO)的GA-PSO混合算法对复杂地形的风力机排布方案进行优化。以湖南省某实际复杂地形为对象,开展风场全风向数值模拟,结合长期观测风资料评估区域的潜在风能分布,提出考虑网格预处理、时变变异率、唯一化和并行化的改进GA(IGA)对风力机排布方案进行优化,在此基础上利用PSO算法进行进一步优化,并针对尾流模型和目标函数对优化结果的影响进行不确定性分析。结果表明,在复杂地形风电场微观选址方面,所提GA-PSO算法比贪婪算法、GA、IGA分别改善16.4%、12.9%和5.1%。

浮式风机动态缆防弯器优化设计

针对防弯器设计时几何结构参数多,需同时考虑刚度、寿命及质量,设计过程复杂,难以快速获得最优结构方案等问题,建立动态缆整体动力响应分析模型及防弯器有限元分析模型,以质量和疲劳寿命为优化目标,将其主要几何参数作为变量,利用BP神经网络算法实现几何参数与疲劳寿命之间的全局化映射,建立防弯器疲劳寿命预测模型,并通过多目标粒子群优化算法得到考虑寿命与质量的最优结构方案。结果表明,优化后防弯器质量和疲劳寿命有一定的提升,形成了浮式风机动态缆防弯器快速优化设计方法。研究成果可为海洋工程结构与装备的优化设计提供理论指导。

基于膜计算的搬运机器人轨迹规划和模型预测控制

针对复杂环境下搬运机器人存在轨迹规划效率低、跟踪控制误差较大及系统不稳定的问题,提出基于膜计算及模型预测的搬运机器人轨迹优化控制方法。首先,针对传统动态窗口算法在速度采样空间中采用均匀等分的方式进行采样,导致轨迹规划效率低的问题,设计了一种基于膜计算粒子群算法改进的搬运机器人动态窗口算法。借助粒子群的随机性和膜计算的分布式并行计算能力对传统动态窗口算法进行优化设计,不断迭代得到最优路径;其次,针对搬运机器人系统模型的非线性特点,采用线性模型预测控制方法完成轨迹跟踪,通过构建预测模型、设定目标函数及设计积分器来完成高精度轨迹跟踪。实验结果表明,在稀疏障碍物和复杂障碍物2种实验场景中,改进后的算法在路径长度、时间及步数上平均减少了15.07%、6.72%、7.68%,且所提的模型预测控制方法在跟踪精度、系统鲁棒性方面都具有一定的优势。

基于充电需求预测的电动汽车充电站选址规划研究

针对目前电动汽车充电站选址规划问题,提出了一种基于充电需求分布预测的充电站选址优化策略。该策略建立了基于Dijkstra最短路径的Voronoi图方法和双层动态排队方法的充电站选址定容模型,来满足电动汽车保有量持续增长下的充电需求;在充电站年均建设、运营成本,配电网惩罚成本以及电动汽车充电成本多目标约束下,得到以电动汽车充电站规划总成本最小化为目标函数。最后基于改进粒子群优化算法求解目标函数,对新增充电站进行多场景实例分析。MATLAB和MATPOWER仿真结果表明,在不同电动汽车保有量的场景下,通过合理规划充电站布局,可以提高电动汽车充电站选址规划的经济性,从而验证了模型的有效性,为充电站的选址规划提供理论依据。