基于WOA- LQR的无人水翼航行器横航向控制

针对一种新型的无人水翼航行器在航行过程中受到不规则海浪干扰时的横向姿态与航向控制问题,首先建立无人水翼航行器横向四自由度运动的非线性数学模型,并对数学模型进行线性化处理;然后针对普通线性二次型调节器(linear quadratic regulator, LQR)控制算法手动调试参数效率较低且控制效果难以达到最优的问题,使用鲸鱼优化算法(whale optimization algorithm, WOA)对LQR控制器的参数进行寻优以增强控制效果;最后在不同强度的随机海浪干扰下对航行器的横向姿态与航向控制进行仿真,验证了鲸鱼算法优化LQR的有效性。

基于遗传算法的四轮转向-驱动汽车时变LQR控制

四轮转向-驱动汽车相较于传统车辆能保证四轮转角/转矩独立可控,具有十分优异的主动动力学控制性能。文中针对四轮转向-驱动汽车转角转矩的协调控制提出了一种基于遗传算法的时变LQR控制系统。该系统区别于传统的线性化轮胎参考模型,考虑轮胎的变刚度特性建立线性时变系统,并利用遗传算法对状态量的控制权重矩阵进行优化。仿真结果表明,在给定的转角阶跃输入下,考虑轮胎非线性特性的时变LQR控制系统相较于线性化模型控制系统,对质心侧偏角的零化效果更优异,横摆角速度对理想值的跟踪精度提升3.01%。在高速低附路面下的双移线工况仿真表明,基于遗传算法的时变LQR控制系统能确保车辆具有较好的轨迹跟踪能力,最大侧向位移误差控制效果相较于前轮转向车辆提升44%。

基于LQR的小车-倒摆闭环控制系统研究

运用Lagrange方法建立了小车-倒摆控制系统的数学模型,并推导出该模型的状态空间表达式,分析了系统的稳定性、能控性和能观性,建立了基于LQR的小车-倒摆闭环控制系统,利用Matlab仿真实验讨论了加权矩阵Q和R对最优控制的影响,实现了对系统的最优控制。

基于PID-LQR主动悬架控制策略的车辆平顺性研究

为了进一步提高悬架系统的综合平顺性能,建立了主动悬架系统的单轮车辆模型,以路面不平度作为车辆系统的干扰信号。考虑到带有LQR控制器的主动悬架,加权系数的确定受人为影响较大,故在LQR控制器中加入一个PID控制器,形成PID-LQR控制器。通过MATLAB对LQR及PID-LQR控制器分别建立了系统的Simulink模型,运用遗传算法和单纯形法,对LQR中加权系数矩阵L、M以及PID控制器中的相关参数进行了优化,使悬架系统控制性能得到一定的提高。在同等情况下,与进行相同优化步骤的LQR控制器进行对比分析,得到两种悬架的平顺性性能差异。结果表明,相比传统的LQR主动悬架控制,所提出的PID-LQR悬架控制策略能在一定程度综合改善模型的平顺性能。

结构减震的稳定时滞LSSVM-LQR智能控制算法

针对时滞LSSVM-LQR智能控制算法存在的稳定性问题,提出相关的稳定性控制算法,以确保时滞LSSVM-LQR智能控制算法的鲁棒性。该算法的主要思路为:在时滞LSSVM-LQR控制算法中,加入控制力限制条件。当满足控制力限制条件时,控制程序继续运行;当不满足控制力限制条件时,控制程序自动跳出,便执行稳定性控制算法(或称为稳定/鲁棒的时滞LSSVM-LQR智能控制算法)。稳定性控制算法主要是通过调整反馈来控制作动器运行,从而确保控制系统的稳定性。数值结果表明,稳定性控制算法能够有效地保证时滞LSSVM-LQR智能控制算法的稳定性/鲁棒性;与时滞LSSVM-LQR智能控制算法相辅相成。

7自由度半主动悬架整车模型PID-LQR控制研究

建立七自由度模型,PID控制理论和LQR控制理论相互结合,设计了PID-LQR控制器,对车身垂直加速度、悬架动行程、轮胎动位移、俯仰角加速度和侧倾角加速度进行优化控制.利用MATLAB/Simulink搭建了仿真模型,利用遗传算法优化各项性能指标权重,使用PID参数整定工具箱PID Tuner整定PID参数.为了突出PID-LQR控制的效果,加入被动悬架和采用LQR控制的半主动悬架进行对比,仿真结果表明,采用PID-LQR控制后,相比于被动悬架和采用LQR控制后的半主动悬架,PID-LQR控制方法控制效果显著,各项性能指标有不同程度优化.

基于改进人工蜂群算法的受电弓LQR控制器设计

针对受电弓LQR控制策略中性能指标的权重系数依靠人工经验选取不足的问题,建立SS400型受电弓三元质量模型,设计受电弓LQR控制器,以接触力标准差和弓头位移标准差之比为评价指标建立目标函数,由于标准人工蜂群算法存在的收敛速度慢、易陷入局部最优的缺点,通过改进蜂群算法的选择策略和搜索方式对LQR控制器的权重系数进行优化。在MATLAB/Simulink环境中建立弓网耦合模型分别在250 km/h、300 km/h的速度下进行仿真,通过与被动控制和标准蜂群算法寻优的结果对比,改进后弓网接触力标准差与弓头位移标准差分别优化了52.8%、37.5%、27.8%、31.8%、40.5%、43.1%、9.3%、7%,显著改善受电弓受流质量。

轨道车辆蛇行运动GA-LQR主动控制研究

基于简化低自由度车辆模型与整车蛇行运动模型,研究性能指标对车辆主动悬挂LQR控制效果差异,利用遗传算法迭代得到最优的LQR控制器。研究结果表明:利用简化1/4车辆模型与整车模型设计出的主动控制器控制效果相差很大;性能指标的选择对车辆悬挂优化控制至关重要;对比2种不同性能指标条件,车体前、后端Sperling指数均有明显下降,而合理的性能指标会达到更加优异的主动控制效果,可以在较大运行速度范围内提高系统的平稳性。

主动悬架的遗传算法优化LQR控制策略研究

为了提高车辆的行驶平顺性和操纵稳定性,本文提出了一种基于遗传算法的线性二次型调节器(linear quadratic regulator,LQR)控制,求解最佳权重矩阵参数的方法。此控制算法克服了传统的LQR控制方法中,由经验主观性确定的权重矩阵Q和R的缺点,使其达到最优的控制效果。本文首先建立了1/4车辆主动悬架模型和道路输入模型,并将遗传算法优化的LQR控制器应用于主动悬架系统中。以主动悬架作为被控对象,车身垂向加速度、悬架弹簧动挠度以及轮胎动位移作为评价指标,通过仿真分析,相比被动控制和LQR控制的主动悬架系统,此优化算法可以显著减小车身垂向加速度与悬架弹簧动挠度,而轮胎动位移的影响不大,从而验证了控制策略的有效性。

Study on LQR control algorithm using superelement model

The conventional linear quadratic regulator(LQR) control algorithm is one of the most popular active control algorithms.One important issue for LQR control algorithm is the reduction of structure's degrees of freedom(DOF). In this work, an LQR control algorithm with superelement model is intended to solve this issue leading to the fact that LQR control algorithm can be used in large finite element(FE) model for structure. In proposed model, the Craig-Bampton(C-B) method, which is one of the component mode syntheses(CMS), is used to establish superelement modeling to reduce structure's DOF and applied to LQR control algorithm to calculate Kalman gain matrix and obtain control forces. And then, the control forces are applied to original structure to simulate the responses of structure by vibration control. And some examples are given. The results show the computational efficiency of proposed model using synthesized models is higher than that of the classical method of LQR control when the DOF of structure is large. And the accuracy of proposed model is well. Meanwhile, the results show that the proposed control has more effects of vibration absorption on the ground structures than underground structures.

Simple Insulin Dose Adjustment Using 3-3-1 Algorithm in Japanese Patients with Type 2 Diabetes: Start Kanazawa Study (Self-Titration Aggressive Algorithm with Glargine Trial)

We implemented a 3-3-1 algorithm in order to provide safe and simple self-titration in patients who newly initiated BOT as well as who were already on BOT and evaluated its utility in clinical setting. A total of 46 patients, 21 patients in the newly-initiated group and 25 patients in the existing BOT group performed dose adjustment using 3-3-1 algorithm. HbA1c was significantly improved 4 weeks after the initiation from 8.5% ± 1.2% at baseline to 7.3% ± 0.7% at the final evaluation (p 0.01, vs. Baseline). The average daily insulin units increased throughout the study period from 10.1 ± 6.7 at baseline to 14.6 ± 8.9 units at the final evaluation. Weight didn’t significantly change throughout the study (p = 0.12). The incidents of hypoglycemia were 0.8/month during the insulin dose self-adjustment period and 0.4/month during the follow-up period. The 3-3-1 algorithm using insulin glargine provided a safe and simple dose adjustment and demonstrated its utility in patients who were newly introduced to insulin treatment as well as who were already on BOT.

基于SOA-LQR控制的电控空气悬架系统

为研究电控空气悬架系统(ECAS)的控制策略,基于MATLAB/Simulink平台搭建了空气悬架模型,以白噪声路面作为系统输入,采用LQR最优控制器实现悬架力的输出,并以人群搜索算法作为控制器优化方式,完成对空气悬架LQR控制器中所包含的主要指标权重数据优化,仿真结果表明,优化后的LQR控制系统对于ECAS的性能提升具有较好效果,其中簧上质量垂直加速度、动挠度瞬态响应峰值相较无控制状态分别减小了45.84%和39.07%,均方根值则分别减小了54.98%和35.4%,整车平顺性得到较大改善。

A new integration algorithm for finite deformation of thermo-elasto-viscoplastic single crystals

An algorithm for integrating the constitutive equations in thermal framework is presented, in which the plastic deformation gradient is chosen as the integration variable. Compared with the classic algorithm, a key feature of this new approach is that it can describe the finite deformation of crystals under thermal conditions. The obtained plastic deformation gradient contains not only plastic defor- mation but also thermal effects. The governing equation for the plastic deformation gradient is obtained based on ther- mal multiplicative decomposition of the total deformation gradient. An implicit method is used to integrate this evo- lution equation to ensure stability. Single crystal 1 100 aluminum is investigated to demonstrate practical applications of the model. The effects of anisotropic properties, time step, strain rate and temperature are calculated using this integration model.

An Algorithm of 0-1 Knapsack Problem Based on Economic Model

In order to optimize the knapsack problem further, this paper proposes an innovative model based on dynamic expectation efficiency, and establishes a new optimization algorithm of 0-1 knapsack problem after analysis and research. Through analyzing the study of 30 groups of 0-1 knapsack problem from discrete coefficient of the data, we can find that dynamic expectation model can solve the following two types of knapsack problem. Compared to artificial glowworm swam algorithm, the convergence speed of this algorithm is ten times as fast as that of artificial glowworm swam algorithm, and the storage space of this algorithm is one quarter that of artificial glowworm swam algorithm. To sum up, it can be widely used in practical problems.

Relative efficiency appraisal of discrete choice modeling algorithms using small-scale maximum likelihood estimator through empirically tailored computing environment

Discrete choice models are widely used in multiple sectors such as transportation, health, energy, and marketing, etc., where the model estimation is usually carried out by using commercial software. Nonetheless, tailored computer codes offer modellers greater flexibility and control of unique modelling situation. Aligned with empirically tailored computing environment, this research discusses the relative performance of six different algorithms of a discrete choice model using three key performance measures： convergence time, number of iterations, and iteration time. The computer codes are developed by using Visual Basic Application （VBA）. Maximum likelihood function （MLF） is formulated and the mathematical relationships of gradient and Hessian matrix are analytically derived to carry out the estimation process. The estimated parameter values clearly suggest that convergence criterion and initial guessing of parameters are the two critical factors in determining the overall estimation performance of a custom-built discrete choice model.

An Improved Binary Wolf Pack Algorithm Based on Adaptive Step Length and Improved Update Strategy for 0-1 Knapsack Problems

Binary wolf pack algorithm (BWPA) is a kind of intelligence algorithm which can solve combination optimization problems in discrete spaces.Based on BWPA, an improved binary wolf pack algorithm (AIBWPA) can be proposed by adopting adaptive step length and improved update strategy of wolf pack. AIBWPA is applied to 10 classic 0-1 knapsack problems and compared with BWPA, DPSO, which proves that AIBWPA has higher optimization accuracy and better computational robustness. AIBWPA makes the parameters simple, protects the population diversity and enhances the global convergence.

基于PSO的LQR车辆横纵向跟踪控制研究

为了解决LQR控制器在固定权重下对复杂道路的跟踪精度和稳定性较差的问题,该文提出一种基于PSO算法优化的预测前馈转角补偿横纵向系统控制方法。横向控制采用PSO优化的LQR控制器,纵向控制采用位置PID和速度PID的双PID控制器进行路径跟踪,位置误差通过PID控制器对前轮转角进行补偿,来解决车辆转向不足的问题。通过MATLAB/Simulink和Carsim进行联合仿真,结果表明,在侧方泊车实验中,最大横向误差降低了约51%;在避障换道超车实验中,最大横向误差减少了约49%。

车辆悬挂LQR主动控制权矩阵权重参数优化

针对LQR主动控制算法权矩阵Q和R的取值困难的问题,为降低权矩阵取值复杂度,分别设计权矩阵Q和R的权重参数λ和ξ。建立优化目标函数f,并分析了λ和ξ对f的影响。利用遗传算法(Genetic Algorithm,GA)的快速搜索能力,通过降维寻优,得到目标函数最优解。对GA-LQR的振动控制性能分析结果表明,GA-LQR对乘坐舒适性和操纵稳定性的改善比LQR明显,目标函数f的值更小。该LQR权矩阵权重参数优化方法简单有效,适用于车辆工程领域。

基于遗传算法的LQR算法中权矩阵的优化分析

LQR控制算法是目前结构主动控制设计分析时最广泛采用的方法。在LQR算法中,权矩阵Q和R的选取直接影响着结构的动力反应和控制力。目前如何确定最优形式和大小的Q和R以获得全局最优控制力仍然是个难题。采用遗传算法,在一定的目标函数下对LQR算法中权矩阵Q和R进行优化,使其控制效果能够满足结构性能要求,并对优化结果下的动力反应进行了分析比较和结果的验证。

基于LQR指标的鲁棒PID整定方法及鲁棒稳定性裕度评估

根据 L QR指标 ,结合最小最大原理 ,使用改进遗传算法作为优化搜索算法 ,提出一种新的鲁棒 PID参数整定方法 .该方法有三个优点 :首先能通过选取衰减系数和自然频率来获取期望的闭环特性 ,并获取近似的 L QR权矩阵 ;其次为 PID控制器增加了一定程度的预测性能 ,最后用本方法设计的控制器对模型误差和外部干扰都表现出一定的鲁棒性 .仿真实例证实了该方法的有效性 .