A Measurement‐Based Approach for Designing Fixed‐Order Controllers for Unknown Closed‐Loop Architecture

This paper presents a new technique to design fixed‐structure controllers for linear unknown systems using a set of measurements. In model‐based approaches, the measured data are used to identify a model of the plant for which a suitable controller can be designed. Due to the fact that real processes cannot be described perfectly by mathematical models, designing controllers using such models to guarantee some desired closed‐loop performance is a challenging task. Hence, a possible alternative to model‐based methods is to directly utilize the measured data in the design process. We propose an approach to designing structured controllers using a set of closed‐loop frequency‐domain data. The principle of such an approach is based on computing the parameters of a fixed‐order controller for which the closed‐loop frequency response fits a desired frequency response that describes some desired performance indices. This problem is formulated as an error minimization problem, which can be solved to find suitable values of the controller parameters. The main feature of the proposed control methodology is that it can be applied to stable and unstable plants. Additionally, the design process depends on a pre‐selected controller structure, which allows for the selection of low‐order controllers. An application of the proposed method to a DC servomotor system is presented to experimentally validate and demonstrate its efficacy.     

Optimal design of a 3D-printed scaffold using intelligent evolutionary algorithms

Fabrication of three-dimensional structures has gained increasing importance in the bone tissue engineering (BTE) field. Mechanical properties and permeability are two important requirement for BTE scaffolds. The mechanical properties of the scaffolds are highly dependent on the processing parameters. Layer thickness, delay time between spreading each powder layer, and printing orientation are the major factors that determine the porosity and compression strength of the 3D printed scaffold.In this study, the aggregated artificial neural network (AANN) was used to investigate the simultaneous effects of layer thickness, delay time between spreading each layer, and print orientation of porous structures on the compressive strength and porosity of scaffolds. Two optimization methods were applied to obtain the optimal 3D parameter settings for printing tiny porous structures as a real BTE problem. First, particle swarm optimization algorithm was implemented to obtain the optimum topology of the AANN. Then, Pareto front optimization was used to determine the optimal setting parameters for the fabrication of the scaffolds with required compressive strength and porosity. The results indicate the acceptable potential of the evolutionary strategies for the controlling and optimization of the 3DP process as a complicated engineering problem.     

文化基因算法求解TSP问题的研究

TSP是组合优化问题中著名的NP-hard问题。针对粒子群算法求解离散的TSP问题收敛速度慢,求解精度低,易于陷入局部最优和模拟退火算法的性能与参数初始值有关及参数敏感等不足,提出了将改进的粒子群算法作为全局搜索策略,改进的模拟退火算法作为局部搜索策略的文化基因算法。介绍了两种算法的协同方法,定义了局部搜索邻域的确定以及在新种群产生中引入自组织随机移民策略。仿真结果表明,改进算法在求解TSP问题中具有很快的收敛速度,且能搜索到最优解。     

基于自适应策略的改进粒子群算法

粒子群算法的参数速度权重在寻优过程中具有重要的作用。如何寻找合适的速度权重是提高算法性能的关键,为解决上述问题,提出了一种基于自适应策略的改进粒子群算法。改进粒子群算法在每次种群进行迭代时,根据每个粒子的适应度值自适应地改变每个粒子的速度权重,动态调整每个种群粒子的活性,提高了算法的全局寻优能力和收敛能力。仿真结果表明,在单目标函数中,改进算法能够更快速地找到最优位置,提高了算法的寻优能力;在多目标函数中,改进算法能够更快速地收敛到问题的Pareto最优边界,提高了算法的收敛能力。     

多无人艇联合攻击任务规划模型仿真

多水面无人舰艇之间的攻击任务规划是保证无人舰艇顺利高效完成攻击任务.多无人舰艇在执行联合攻击任务时,需要考虑到环境因素、不同任务的复杂度与各种复杂攻击任务执行能力等多方面的约束因素.传统的攻击任务调度模型在处理上述问题时,没有考虑攻击任务外界与内部的约束条件,只是将复杂的攻击任务简单地分解为个体的叠加,在任务执行过程中不仅达不到提高任务执行效率的目的,还会由于缺乏相应的协同规划机制陷入混乱状态.提出一种多无人艇联合攻击任务规划模型.根据水面无人舰艇集群攻击任务规划问题的多约束性,建立任务规划模型,引入代价函数,运用改进的生物地理粒子群优化算法对任务分配问题进行求解,避免了生物地理优化算法容易陷入局部最优和粒子群算法解多约束离散问题时的不稳定性.进行仿真的结果表明,所提出算法有效解决了多水面无人舰艇之间的攻击任务规划问题.     

基于PSO-SVM的乳腺肿瘤辅助诊断研究

在乳腺肿瘤识别优化的研究中,传统的识别方法容易漏诊.为提高乳腺肿瘤识别准确率,提出基于粒子群优化(Particle Swarm Optimization,PSO)参数的支持向量机(Support Vector Machine,SVM)辅助诊断方法.首先采用PSO选择最佳的SVM惩罚系数c,核函数参数γ;然后,利用最佳参数c和γy训练SVM;再利用PSO-SVM实现乳腺肿瘤分类识别,进而实现辅助诊断.将PSO-SVM乳腺肿瘤识别方法的仿真结果与LVQ神经网络识别方法、BP神经网络识别方法的结果做比对分析,表明PSO-SVM具有较高的识别准确率和较低的假阴性率.PSO-SVM乳腺肿瘤辅助诊断,可以提供决策支持,辅助医生尽可能地减少和避免采用传统的细针穿刺细胞病理学检查方法诊断乳腺肿瘤时的漏诊、误诊情况,具有非常重要的价值和意义.     

含分布式发电的改进BFO算法配电网无功优化

在含分布式电源的电网无功优化研究中,为了更有效地提高配电网性能,提出了一种改进细菌觅食算法(CP-BFO).以电网网损最小、负荷节点电压和发电机的无功出力约束作为综合目标函数,采用细菌觅食算法,在聚焦操作中引入粒子群变异算子,使算法具有良好的全局搜索能力,提高了算法的寻优效率.同时利用混沌原理对改进的细菌觅食算法的参数进行自适应调节,改善了算法的收敛性能.通过节点系统的仿真表明,CP-BFO算法在提高含分布式电源的智能电网电压质量与减少功率损耗的优化过程中具有可行性和有效性.     

糖厂澄清工段生产指标建模及操作参数优化

针对亚法糖厂澄清工段清汁色值和清汁残硫量难以在线测量的问题,提出了一种基于人工蜂群优化的在线极限学习机软测量方法;先用核主元分析法确定影响清汁质量的关键参数,建立基于在线极限学习机的软测量模型;同时利用人工蜂群算法对在线极限学习机的隐层参数进行寻优,优化所建模型;最后,使用带约束的粒子群对软测量模型进行优化求解,得到典型工况下的最优操作设定值,为后续工况操作提供参考依据;仿真结果表明,基于人工蜂群优化的在线极限学习机模型能够准确地预测清汁色值和残硫量,同时基于此模型优化的操作参数设定值能够达到期望的指标.     

基于PSO算法的运动目标跟踪研究

提出一种基于PSO的运动目标跟踪方法,并在TMS320DM642上实现.该方法首先针对采集到图像的Y分量通过帧间差分和背景差分相结合的方法建立背景模型,然后检测出前景运动目标,接着通过前景运动目标初始化目标模板,再对目标模板和待跟踪视频图像分别进行两次金字塔降采样,降低目标模板和待跟踪视频图像的分辨率.在顶层金子塔上采用粒子群优化算法对跟踪目标进行粗定位,在中间层和底层金字塔上采用钻石搜索方法对跟踪目标进行精确定位.在目标跟踪的过程中,目标模板随运动目标的变化而不断更新,实现对目标的实时连续性跟踪.该方法可以有效降低计算复杂度,提高搜索效率.     

Position control of electro-hydraulic actuator system using fuzzy logic controller optimized by particle swarm optimization

The position control system of an electro-hydraulic actuator system (EHAS) is investigated in this paper. The EHAS is developed by taking into consideration the nonlinearities of the system: the friction and the internal leakage. A variable load that simulates a realistic load in robotic excavator is taken as the trajectory reference. A method of control strategy that is implemented by employing a fuzzy logic controller (FLC) whose parameters are optimized using particle swarm optimization (PSO) is proposed. The scaling factors of the fuzzy inference system are tuned to obtain the optimal values which yield the best system performance. The simulation results show that the FLC is able to track the trajectory reference accurately for a range of values of orifice opening. Beyond that range, the orifice opening may introduce chattering, which the FLC alone is not sufficient to overcome. The PSO optimized FLC can reduce the chattering significantly. This result justifies the implementation of the proposed method in position control of EHAS.