纸纱复合制袋印刷一体机张力控制器设计

目的研究纸纱复合制袋印刷一体机纸张张力控制器。方法针对纸纱复合制袋印刷一体机的张力控制问题,结合模糊自适应PID与粒子群算法,设计基于余弦自适应调整惯性权重的粒子群优化算法的模糊自适应PID张力控制器。利用余弦自适应调整惯性权重的粒子群优化算法,搜索出一组最优的PID参数,来提高张力的控制精度。结果仿真结果表明,该张力控制方法的响应时间为0.25 s,最大超调量为2%,小于其他方法的响应时间和最大超调量。结论设计的控制器与传统的PID控制和模糊自适应PID控制相比,具有响应速度快、控制输出稳定、调节时间短等优点。     

Chattering‐ free hybrid adaptive neuro‐fuzzy inference system‐particle swarm optimisation data fusion‐based BG‐level control

In this study, a closed‐loop control scheme is proposed for the glucose–insulin regulatory system in type‐1 diabetic mellitus (T1DM) patients. Some innovative hybrid glucose–insulin regulators have combined artificial intelligence such as fuzzy logic and genetic algorithm with well known Palumbo model to regulate the blood glucose (BG) level in T1DM patients. However, most of these approaches have focused on the glucose reference tracking, and the qualitative of this tracking such as chattering reduction of insulin injection has not been well‐studied. Higher‐order sliding mode (HoSM) controllers have been employed to attenuate the effect of chattering. Owing to the delayed nature and non‐linear property of glucose–insulin mechanism as well as various unmeasurable disturbances, even the HoSM methods are partly successful. In this study, data fusion of adaptive neuro‐fuzzy inference systems optimised by particle swarm optimisation has been presented. The excellent performance of the proposed hybrid controller, i.e. desired BG‐level tracking and chattering reduction in the presence of daily glucose‐level disturbances is verified.Inspec keywords: fuzzy control, variable structure systems, particle swarm optimisation, neurocontrollers, fuzzy neural nets, blood, genetic algorithms, closed loop systems, medical control systems, fuzzy reasoning, diseases, nonlinear control systems, sugarOther keywords: data fusion, adaptive neuro‐fuzzy inference systems, particle swarm optimisation, hybrid controller, desired BG‐level tracking, chattering reduction, daily glucose‐level disturbances, closed‐loop control scheme, glucose–insulin regulatory system, type‐1 diabetic mellitus patients, innovative hybrid glucose–insulin regulators, artificial intelligence, fuzzy logic, genetic algorithm, Palumbo model, blood glucose level, T1DM patients, glucose reference tracking, insulin injection, mode controllers, glucose–insulin mechanism, chattering‐free hybrid adaptive neuro‐fuzzy inference system, particle swarm optimisation data fusion‐based BG‐level control     

叶片精密抛光伺服系统的干扰观测补偿控制

叶片型面尺寸精度及表面质量的提高对数控抛光伺服系统性能提出了更高的要求.针对非线性摩擦和被控对象参数摄动对数控抛光伺服系统定位精度和跟踪精度的影响,提出了一种基于干扰观测器的粒子群优化模糊PID(PFPID)控制方法.该方法通过构造干扰观测器来预测伺服系统中的非线性摩擦和参数摄动等各种干扰,并在控制中引入等效的补偿来抑制干扰,同时利用粒子群优化算法对模糊控制器的量化因子和比例因子进行在线调整,进而利用模糊控制器对PID控制参数进行自适应整定.仿真分析和实验结果表明,基于干扰观测器的PFPID控制器具有控制精度高、鲁棒性强、抑制干扰能力强等优点,其能够提高叶片型面尺寸精度和表面一致性,降低表面粗糙度,减小残余应力并提高抛光效率.     

Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

In the field of energy conversion, the increasing attention on power electronic equipment is fault detection and diagnosis. A power electronic circuit is an essential part of a power electronic system. The state of its internal components affects the performance of the system. The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits. Therefore, an algorithm based on adaptive simulated annealing particle swarm optimization (ASAPSO) was used in the present study to optimize a backpropagation (BP) neural network employed for the online fault diagnosis of a power electronic circuit. We built a circuit simulation model in MATLAB to obtain its DC output voltage. Using Fourier analysis, we extracted fault features. These were normalized as training samples and input to an unoptimized BP neural network and BP neural networks optimized by particle swarm optimization (PSO) and the ASAPSO algorithm. The accuracy of fault diagnosis was compared for the three networks. The simulation results demonstrate that a BP neural network optimized with the ASAPSO algorithm has higher fault diagnosis accuracy, better reliability, and adaptability and can more effectively diagnose and locate faults in power electronic circuits.     

基于混沌差分进化粒子群算法的模糊资源受限项目调度问题

本文研究了工期模糊情况下的资源受限项目调度问题,采用一种基于区间数距离的模糊取最大运算比较模糊工期的大小,解决了以往研究中忽略的工期模糊情况下,项目关键路径可能会发生改变,相应地各活动的模糊调度时间以及项目的模糊最短工期也可能随之发生改变的问题。引入一种基于混沌和差分进化的混合粒子群优化算法,并对算法的惯性权重进行改进来求解上述问题。通过一个算例验证了所建立模型及提出方法的有效性。     

光伏发电系统发电功率预测

为解决光伏发电系统发电功率在不同条件下误差较大问题,提出光伏发电系统发电功率预测新方法。通过分析光伏发电系统结构,研究光伏发电系统发电功率影响因素;以季节和天气类型作为历史样本选取样本源,针对气象部门提供的预测日分时气象数据在历史数据库中寻找相似数据点作为历史样本;依据历史样本构建离线参数寻优数据总集,使用核函数极限学习机算法构建发电系统发电功率预测模型,通过粒子群算法优化模型参数。实验结果表明：所提方法在不同条件下预测太阳能光伏发电系统发电功率的平均绝对百分比误差分别为1.47%和6.39%,光伏组件在综合异常条件下发电功率预测误差相对变化均低于1%,证明所提方法满足实际预测要求。     

Fuzzy entropy‐based MR brain image segmentation using modified particle swarm optimization

This article presents an image segmentation technique based on fuzzy entropy, which is applied to magnetic resonance (MR) brain images in order to detect brain tumors. The proposed method performs image segmentation based on adaptive thresholding of the input MR images. The image is classified into two membership functions (MFs) of the fuzzy region: Z‐function and S‐function. The optimal parameters of these fuzzy MFs are obtained using modified particle swarm optimization (MPSO) algorithm. The objective function for obtaining the optimal fuzzy MF parameters is considered to be the maximum fuzzy entropy. Through a number of examples, The performance is compared with existing entropy based object segmentation approaches and the superiority of the proposed method is demonstrated. The experimental results are compared with the exhaustive search method and Otsu's segmentation technique. The result shows the proposed fuzzy entropy‐based segmentation method optimized using MPSO achieves maximum entropy with proper segmentation of infected areas and with minimum computational time. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 281–288, 2013     

A cloud theory-based particle swarm optimization for multiple decision maker vehicle routing problems with fuzzy random time windows

This article puts forward a cloud theory-based particle swarm optimization (CTPSO) algorithm for solving a variant of the vehicle routing problem, namely a multiple decision maker vehicle routing problem with fuzzy random time windows (MDVRPFRTW). A new mathematical model is developed for the proposed problem in which fuzzy random theory is used to describe the time windows and bi-level programming is applied to describe the relationship between the multiple decision makers. To solve the problem, a cloud theory-based particle swarm optimization (CTPSO) is proposed. More specifically, this approach makes improvements in initialization, inertia weight and particle updates to overcome the shortcomings of the basic particle swarm optimization (PSO). Parameter tests and results analysis are presented to highlight the performance of the optimization method, and comparison of the algorithm with the basic PSO and the genetic algorithm demonstrates its efficiency.     

基于PLC的颗粒包装机称量系统设计

目的 为提高颗粒包装机称量精度和稳定性,基于PLC设计一种颗粒包装机称量系统.方法 分析颗粒包装机结构和工艺流程,并给出控制系统结构,包括核心处理器PLC、交流控制器、伺服电机驱动器、传感器、三相电机和伺服电机等.以称量控制为主要研究对象,提出一种粒子群模糊PID称量控制器,以提高称量控制系统的收敛速度、通用性和可移植性.最后进行仿真和实验研究.结果 相关结果表明,与模糊PID控制器相比,加入粒子群优化算法后,系统的响应速度更快,达到稳定状态所需时间更短,实际包装误差仅为0.528％.结论 所述称量控制系统可以有效地提升称量精度,有利于提高颗粒包装机的自动化水平.     

改进粒子群算法的啤酒灌装机液位控制PID参数整定

目的 整定最优的PID控制参数,对啤酒灌装机中贮液罐液位进行控制,以保证PID控制器能满足啤酒生产中的控制要求。方法 结合Rosenbrock搜索法和个体扰动策略来改进粒子群算法,并利用改进算法整定PID参数,最后将整定好参数的PID控制器用于控制液位对象;基于Matlab进行仿真实验,利用粒子群算法与文中方法做比较。结果 通过Matlab仿真验证,改进了粒子群算法整定的PID参数,其跟踪特性的调节时间为16.18 s,超调量为10.20%,IAE性能指标约为6.09;粒子群算法整定结果表明,跟踪特性的调节时间为27.72 s,超调量为26.90%,IAE性能指标约为7.23。结论 与原始粒子群算法相比,文中算法整定的3个PID参数在控制液位对象时综合性能评价指标更好,且能使系统平稳过渡,超调较小,响应速度快,调节时间快,其控制器性能能满足啤酒灌装机的生产要求。     

太阳能最大功率点跟踪的航模电调设计

根据太阳能光伏输出特性,采用最小二分法实现最大功率点跟踪(MPPT),对于提高太阳能电池的输出功率及太阳能的利用率,特别是在控制电机等感性负载上,意义重大.在航模飞机上,太阳能飞机的飞行受太阳能板输出功率的直接影响,而航模飞机的电调采用中颖公司的SH79F168作为主芯片,太阳能最大功率输出也需要芯片控制太阳能板的输出电压,此设计的目的就是把两者合二为一,以节省空间并提高效能.     

An improved hybrid self-inertia weight adaptive particle swarm optimization algorithm with local search

As an evolutionary computing technique, particle swarm optimization (PSO) has good global search ability, but the swarm can easily lose its diversity, leading to premature convergence. To solve this problem, an improved self-inertia weight adaptive particle swarm optimization algorithm with a gradient-based local search strategy (SIW-APSO-LS) is proposed. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation of the gradient-based local search strategy. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The gradient-based local search focuses on the exploitation ability because it performs an accurate search following SIW-APSO. Experimental results verified that the proposed algorithm performed well compared with other PSO variants on a suite of benchmark optimization functions.     

基于改进粒子群算法的航空行李在线装载优化

目的 为解决航空行李自动装卸中关键装载算法问题,实现航空行李自动装卸,同时满足流水作业的实际需要.方法 基于关键点装载策略,提出一种以装载空间利用率为优化目标,考虑行李质量、体积及装载顺序等约束条件的改进粒子群算法.首先,通过关键点法输出流水线上待装载行李的全部可放点序列,然后根据约束条件重新定义粒子群算法的速度与位置,以空间利用率为适应度函数进行迭代寻优,输出全局最优解,实现对装载位置与姿态的优化.结果 实验部分采用真实行李数据对算法进行仿真验证表明,改进粒子群算法优化后可将箱体空间利用率提高了10.8％,平均规划布局效率提高了26.5％.结论 提出的装载算法能够有效地解决实际行李装载问题,为行李流水作业的货物装载提供理论依据及参考.     

太阳能光伏发电最大功率点跟踪控制器设计

为提高光伏发电系统的效率,设计一种太阳能光伏发电最大功率点跟踪控制器。该控制器采用升降压式DCDC变换电路,利用变步长占空比扰动法实现最大功率点跟踪。设计以超级电容和蓄电池混合储能的充电方式,该方法在太阳光强较弱时用小电流继续对蓄电池充电,实现对蓄电池实时充电。对太阳能充电进行相关实验,结果表明:设计的最大功率点跟踪控制器可以在任何光照条件下完成充电,超级电容与蓄电池混合充电比蓄电池单独充电时的充电电流增加了29.1%,提高光伏发电系统的效率。     

基于自适应粒子群遗传算法的柔性关节机器人动力学参数辨识

提出一种基于自适应粒子群遗传算法的柔性关节机器人动力学参数辨识方法。该算法采用动态自适应调整策略,提高了粒子群算法收敛速度;同时引入新型遗传算法混合交叉变异机制,避免了粒子群陷入局部最优。将自适应粒子群遗传算法与标准粒子群算法、遗传算法、人工蜂群算法进行了比较,仿真实验结果表明该算法在迭代60次左右完成参数辨识,各参数的辨识相对误差均降低到了1%以内。最后利用旋转柔性关节实验平台进行了实验验证,实验结果证明了该算法具有更好的收敛速度和寻优精度。     

MVDR自适应波束形成的一种智能算法

最小方差无失真响应（MVDR）自适应波束形成方法在声纳阵列信号处理中得到了广泛的应用。当存在强干扰时,传统的MVDR算法的稳定性较差,在有限次快拍数条件下,会带来一定的信噪比损失。将粒子群优化算法（PSO）引入MVDR的求解过程,提出了一种MVDR的数值实现方法。该方法在保持信号能量不变、干扰和噪声能量最小的约束条件下,利用粒子群优化算法通过数值搜索的方法获得MVDR的最优权向量。在小快拍数条件下,较一般的MVDR具有更好的抑制干扰能力。计算机仿真结果验证了该方法的有效性。     

基于改进量子粒子群算法的纸浆浓度控制系统

目的为了克服传统PID控制在具有大时滞性、非线性等特点的纸浆浓度控制系统中性能不足和参数调整困难等问题,研究参数在线调整的方法。方法在传统PID控制的基础上,结合量子粒子群仿生算法(QPSO),提出一种量子粒子群算法优化的传统PID控制器参数,并应用于纸浆浓度控制系统;同时对基本量子粒子群算法进行改进,引入交叉算子,并将该控制算法应用到纸浆浓度控制系统中,并与传统控制进行对比。结果与传统PID控制和基本量子粒子群优化的PID相比较,改进的优化算法能够得到更加令人满意的控制效果,具有系统超调量小、响应速度快、鲁棒性高等优良的性能。结论基于改进的量子粒子群优化算法的纸浆浓度控制系统可有效控制纸浆浓度,能够明显提高系统的控制精度等性能指标,更好地满足实际应用的要求。     

A particle swarm optimizer for constrained multi-objective engineering design problems

This article presents a particle swarm optimizer (PSO) capable of handling constrained multi-objective optimization problems. The latter occur frequently in engineering design, especially when cost and performance are simultaneously optimized. The proposed algorithm combines the swarm intelligence fundamentals with elements from bio-inspired algorithms. A distinctive feature of the algorithm is the utilization of an arithmetic recombination operator, which allows interaction between non-dominated particles. Furthermore, there is no utilization of an external archive to store optimal solutions. The PSO algorithm is applied to multi-objective optimization benchmark problems and also to constrained multi-objective engineering design problems. The algorithmic effectiveness is demonstrated through comparisons of the PSO results with those obtained from other evolutionary optimization algorithms. The proposed particle swarm optimizer was able to perform in a very satisfactory manner in problems with multiple constraints and/or high dimensionality. Promising results were also obtained for a multi-objective engineering design problem with mixed variables.     

不确定电液位置伺服系统的动态面跟踪控制

针对一类含有动态不确定性的双作用液压缸电液伺服系统跟踪控制问题,采用动态面控制方法设计了一个鲁棒自适应跟踪控制器.由于在逆推设计过程中加入了低通滤波器使得该方法不用对模型非线性进行多次微分,因而设计方法简化.所设计的自适应鲁棒控制器不仅能保证闭环系统的半全局渐近稳定,使得输出渐近跟踪期望轨迹;而且,跟踪误差可以通过控制器的设计参数加以调整.数字仿真结果表明,控制系统对给定位置的跟踪具有良好的动态特性,对系统的不确定性,具有较强的鲁棒性.     

Control of Velocity-Constrained Stepper Motor-Driven Hilare Robot for Waypoint Navigation

Finding an optimal trajectory from an initial point to a final point through closely packed obstacles, and controlling a Hilare robot through this trajectory, are challenging tasks. To serve this purpose, path planners and trajectory-tracking controllers are usually included in a control loop. This paper highlights the implementation of a trajectory-tracking controller on a stepper motor-driven Hilare robot, with a trajectory that is described as a set of waypoints. The controller was designed to handle discrete waypoints with directional discontinuity and to consider different constraints on the actuator velocity. The control parameters were tuned with the help of multi-objective particle swarm optimization to minimize the average cross-track error and average linear velocity error of the mobile robot when tracking a predefined trajectory. Experiments were conducted to control the mobile robot from a start position to a destination position along a trajectory described by the waypoints. Experimental results for tracking the trajectory generated by a path planner and the trajectory specified by a user are also demonstrated. Experiments conducted on the mobile robot validate the effectiveness of the proposed strategy for tracking different types of trajectories.