基于遗传算法的鲁棒PID控制器设计

从分析控制系统的性能指标入手,从抗干扰性能和鲁棒性能两方面综合考虑控制器的设计,得到一种鲁棒 PID 控制器的设计思路,把 PID 控制器的设计问题转化为求解一个带鲁棒性能约束的绝对误差积分指标 (IAE) 优化问题。鉴于问题是非凸的,用常规优方法无法得到最优解,采用遗传算法来求解该问题。分别对一阶加纯迟延(FOPDT)和非线性对象进行了仿真,结果表明了方法的有效性,不仅控制系统性能优良,而且抗干扰能力和鲁棒性能也很好。     

状态多重时滞大系统时滞相关分散鲁棒镇定

考虑状态多重时滞大系统的稳定性问题,依赖系统最大时滞界,给出了其可分散状态反馈镇定的时滞相关鲁棒稳定的充分条件.应用Lyapunov-Krasovsii泛函和LMI方法及积分不等式方法,研究其分散鲁棒稳定化的鲁棒控制器设计问题,在此基础上,通过求解一凸优化问题,提出了具有较小反馈增益的分散稳定化状态反馈控制器的设计方法.通过仿真验证了该方法的有效性.     

带积分环节的线性系统鲁棒控制器的插值法设计

利用Ｈ∞控制理论中的插值法,研究了带积分环节的线性系统鲁棒控制器的设计。根据系统的内稳定性和鲁棒稳定性要求,给出了满足带积分环的线性系统控制器的插值条件,使设计得到的线性系统鲁棒控制器具有积分控制作用,从而可使系统既能够满足稳态性能的要求,又具有鲁棒稳定性。     

LMI方法和μ综合方法设计鲁棒控制系统

介绍了目前广泛使用求解H∞控制问题的LMI(线形矩阵不等式)方法和μ综合设计方法.由于H∞方法的设计框架导致了系统保守性并忽略了鲁棒性能设计而与H∞同时期发展的μ理论则考虑了结构化的不确定性问题,它不但能有效地、无保守性的判断"最坏情况"下摄动的影响,而且在存在不同表达形式的结构化的情况下,能分析控制系统的鲁棒稳定性和鲁棒性能问题.最后分析两种方法的关系并对两种方法进行了比较.     

随机网络控制系统的H∞控制

目的 建立更加符合实际的网络控制系统模型,探讨随机网络控制系统的随机鲁棒均方指数稳定和鲁棒H∞控制问题.方法 运用随机控制、Ito微分、线性矩阵不等式(LMI)和Lya-punov稳定性理论,推导出改进的网络控制系统鲁棒均方指数稳定且满足H∞性能的充分条件及H∞控制器的设计方法.结果 提出了同时具有网络诱导时延、数据包丢失和随机扰动等各因素下的网络控制系统模型,分析了该模型随机鲁棒均方指数稳定且满足H∞性能的充分条件,并给出相关控制器的设计方法.结论 通过控制器的设计,使具有扰动的网络控制系统性能有很大的改善,通过Matlab仿真证明该推导方法和结果行之有效,并且结果具有更小的保守性.     

时滞不稳定过程PID控制器直接设计法

直接在时域上设计时滞不稳定过程比例积分微分（PID）控制器。采取双回路三环节的控制结构,目标是优化两个时域指标-阻尼系数和积分增益。然后,合并三个环节得到设定值加权PID控制器。仿真表明该方法是有效的,具有好的鲁棒稳定性和性能。     

不确定性线性系统的H_∞输出反馈鲁棒重复控制

为了抑制乘性不确定性线性系统中的外部扰动,提出了一种重复控制器与输出反馈控制器参数同时优化的方法.引入一个假想摄动将系统的扰动抑制问题归结为具有结构型摄动的系统鲁棒稳定性问题.根据小增益定理得到该结构型摄动系统鲁棒稳定性的充分条件.引入一个稳定的定标阵,得到该结构型摄动系统低保守性的鲁棒稳定性充分条件.基于该充分条件和线性矩阵不等式方法(LMI)、锥补线性化方法(CCL),提出的迭代算法可以同时计算出重复控制器中低通滤波器剪切频率的最大值及其对应的输出反馈控制器的参数.最后,低频线振动台系统验证了该方法的有效性。     

范数有界不确定采样系统的鲁棒控制

将工业生产中过程控制系统考虑为一类范数有界不确定采样系统.采用矩阵不等式方法,从数字再设计的角度,进行了鲁棒状态反馈控制器的设计.该方法保证了受扰采样系统的稳定性,在满足采样系统与相应连续系统的状态输出近似相等的目标要求下,给出了满足特定条件的鲁棒控制律.鲁棒状态反馈控制器的求取借助于求解一类具有线性矩阵不等式(LMI)约束的优化问题.仿真验证了方法的可行性,表明本文设计的鲁棒状态反馈控制器的性能优于采用常规方法所设计的控制器.     

不确定性表示及在高性能鲁棒飞行控制器设计中的应用研究

针对采用H∞鲁棒控制方法设计出的控制器一般较为保守的问题，分析得到主要原因是对象不确定的描述方式过于保守，限制了系统性能指标的提高，通过对不确定性产生的物理机理分析，给出了一种基于对象物理方程分析的保守性较小的不确定描述方法。研究表明，对于不确定性认识清楚，数学描述恰当给控制问题求解带来很大方便，以某型号飞机的大包线姿态角度控制为算例，采用鲁棒混合灵敏度方法设计控制器，由于不确定的描述保守性较小，系统不仅在大包线范围内具有鲁棒稳定性，而且具有鲁棒性能。     

基于H_∞控制的多变量系统PID控制器设计

研究了基于环路成形Ｈ∞控制的多变量系统ＰＩＤ控制器设计。为了能方便地在实际工程中实现复杂的控制，提出了采用多变量ＰＩＤ控制器逼近所得高阶控制器的方法，并给出了如何选择影响闭环系统性能的预补偿器方法。这种设计方法的主要特点是，只通过调整反映闭环系统的鲁棒稳定性与时域性能的一个设计参数，便可以得到ＰＩＤ控制器。仿真结果表明，所得的多变量ＰＩＤ控制器具有较好的鲁棒稳定性和鲁棒性能。     

基于有限时间干扰观测器的鲁棒积分跟踪控制

针对电液位置伺服系统同时存在的参数不确定和不确定非线性（统称为干扰）,导致传统非线性控制精度不高、跟踪性能不好等问题,提出基于有限时间干扰观测器（FTDO）的鲁棒积分跟踪控制策略. 通过将误差符号鲁棒积分（RISE）控制策略与FTDO融合,实现对未观测干扰的抑制. 考虑到实际系统中噪声对跟踪精度的影响,该控制策略结合期望补偿手段,提高跟踪精度. 通过Lyapunov稳定性理论,证明了闭环系统的全局渐进稳定性. 对比实验结果显示,利用该方法能够有效提高电液位置伺服系统在干扰作用下的跟踪性能,在相同的测试工况下,与速度前馈PI控制器相比,跟踪精度提高了25%左右.     

Robust L2 -L∞ filtering with pole constraint in a disk via parameter-dependent Lyapunov functions

Addresses the design problems of robust L2 - L∞ filters with pole constraint in a disk for uncertain continuous-time linear systems. The uncertain parameters are assumed to belong to convex bounded domains.The aim is to determine a stable linear filter such that the filtering error system possesses a prescribed L2 - L∞ noise attenuation level and expected poles location. The filtering strategies are based on parameter-dependent Lyapunov stability results to derive new robust L2 - L∞ performance criteria and the regional pole placement con-ditions. From the proposed multi-objective performance criteria, we derive sufficient conditions for the existence of robust L2 - L∞ filters with pole constraint in a disk, and cast the filter design into a convex optimization prob-lem subject to a set of linear matrix inequality constraints. This filtering method exhibits less conservativeness than previous results in the quadratic framework. The advantages of the filter design procedures are demonstrated by means of numerical examples.     

Method of Designing Missile Controller Based on Multi-Objective Opti mization

Generally,the process of designing missile con-trol systeminvolves a great deal manual modificationsand trials in order to meet all the design specifica-tions ,such as robust stability, noise rejection, dy-namic response , steady tracking error , etc . So thedesignis an opti mization problemin nature . Becausegenetic algorithm(GA) is a search algorithmbased onthe mechanismof natural selection and natural genet-ics and is different from conventional opti mization.GAsearches for a population of…     

柔性交流输电系统控制器的多目标协调设计

为提高远距离输电系统的线路传输能力和暂态稳定性，采用可控串联补偿器（TCSC）和静态无功补偿器（SVC）2种柔性交流输电系统（FACTS）元件联合运行来同时保证线路功率传输和多机间功角稳定.为实现TCSC和SVC之间的协调控制，应用多目标进化规划（MOEP）建立了多目标优化模型，同时优化2个控制器的参数.对一个典型四机两区域系统进行三相故障仿真，并与分别设计的FACTS控制器进行比较，结果表明，基于此方法设计的控制器明显提高了系统的暂态稳定性.     

Cross-coupling integral adaptive robust posture control of a pneumatic parallel platform

A pneumatic parallel platform driven by an air cylinder and three circumambient pneumatic muscles was considered. Firstly, a mathematical model of the pneumatic servo system was developed for the MIMO nonlinear model-based controller designed. The pneumatic muscles were controlled by three proportional position valves, and the air cylinder was controlled by a proportional pressure valve. As the forward kinematics of this structure had no analytical solution, the control strategy should be designed in joint space. A cross-coupling integral adaptive robust controller(CCIARC) which combined cross-coupling control strategy and traditional adaptive robust control(ARC) theory was developed by back-stepping method to accomplish trajectory tracking control of the parallel platform. The cross-coupling part of the controller stabilized the length error in joint space as well as the synchronization error, and the adaptive robust control part attenuated the adverse effects of modelling error and disturbance. The force character of the pneumatic muscles was difficult to model precisely, so the on-line recursive least square estimation(RLSE) method was employed to modify the model compensation. The projector mapping method was used to condition the RLSE algorithm to bound the parameters estimated. An integral feedback part was added to the traditional robust function to reduce the negative influence of the slow time-varying characteristic of pneumatic muscles and enhance the ability of trajectory tracking. The stability of the controller designed was proved through Laypunov's theory. Various contrast controllers were designed to testify the newly designed components of the CCIARC. Extensive experiments were conducted to illustrate the performance of the controller.     

A robust multi-objective and multi-physics optimization of multi-physics behavior of microstructure

A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization(IMOCO) and its extension improved multi-objective robust collaborative(IMORCO).In this work,the proposed IMORCO approach combined the IMOCO method,the worst possible point(WPP) constraint cuts and the Genetic algorithm NSGA-II type as an optimizer in order to solve the robust optimization problem of multi-physics of microstructures with uncertainties.The optimization problem is hierarchically decomposed into two levels: a microstructure level,and a disciplines levels.For validation purposes,two examples were selected: a numerical example,and an engineering example of capacitive micro machined ultrasonic transducers(CMUT) type.The obtained results are compared with those obtained from robust non-distributed and distributed optimization approach,non-distributed multi-objective robust optimization(NDMORO) and multi-objective collaborative robust optimization(McRO),respectively.Results obtained from the application of the IMOCO approach to an optimization problem of a CMUT cell have reduced the CPU time by 44% ensuring a Pareto front close to the reference non-distributed multi-objective optimization(NDMO) approach(mahalanobis distance,D_M~2=0.9503 and overall spread,S_o=0.2309).In addition,the consideration of robustness in IMORCO approach applied to a CMUT cell of optimization problem under interval uncertainty has reduced the CPU time by 23% keeping a robust Pareto front overlaps with that obtained by the robust NDMORO approach(D_M~2=10.3869 and S_o=0.0537).     

采用极限梯度提升算法的电力系统电压稳定裕度预测

将极限梯度提升树（XGBoost）算法应用于电力系统电压稳定评估问题. 根据电压稳定问题特点,提出能够反映电力系统运行状态的特征集;把电压稳定裕度绝对值作为映射目标,并介绍生成样本集的方法. 在介绍XGBoost算法基本原理的基础上,研究该算法的技术细节. 在IEEE-39节点系统上进行验证,结果表明,XGBoost算法在R方值和平均绝对百分误差2项回归指标上均优于其他几类机器学习算法,且模型的计算速度最快,可以满足在线应用要求. 同时,XGBoost算法具有良好的数值错误和数值缺失容错性,并可以针对预测偏差较大的样本进行数据补充,实现模型的更新,使得模型表现趋于稳定.     

Robust Topology Optimization of Vehicle Suspension Control Arm

A robust topology optimization design framework is developed to solve lightweight structural design problems under uncertain conditions. To enhance the calculation accuracy and flexibility of the statistical moments of robust analysis, number theory integral method is applied to sample point selection and weight assignment. Both the structure topology optimization and number theory integral methods are combined to form a new robust topology optimization method. A suspension control arm problem is provided as a demonstration of robust topology optimization methods under loading uncertainties. Based on the results of deterministic and robust topology optimization, it is demonstrated that the proposed robust topology optimization method can produce a more robust design than that obtained by deterministic topology optimization. It is also found that this new approach is easy to apply in the existing commercial topology optimization software and thus feasible in practical engineering problems.     

基于层次分析粒子群算法的可靠性稳健优化设计

利用多目标优化策略,将机械零部件的可靠性稳健优化设计转化为多目标问题。为提高零部件可靠性稳健优化设计多目标模型的求解效率,将所提出的基于层次分析法的多目标粒子群算法应用于可靠性稳健优化设计的多目标模型求解中。该方法简便易行,能迅速准确地得到机械零部件扭杆的可靠性稳健优化设计信息。     

Optimal Tuning of Plant-Friendly PID Controllers

A plant-friendly proportional-integral-derivative (PID) controller optimization framework is proposed to make tradeoffs among set-point response, controller output variations and robustness. The objective function is chosen as the weighted sum of the integral of squared time-weighted error and the integral of squared time-weighted derivative of the control variable with respect to set-point response, while the robustness of the system is guaranteed by constraints on gain and phase margins. Due to the complex structure of the constraints, the problem is solved by genetic algorithms. Simulation analysis show the proposed method could efficiently reduce the controller output variations while maintaining a short settling time. Based on the simulation results, iterative tuning rules for the weighting factor in the objective function are obtained, which allows efficient simple proportional-integral(PI) tuning formulae to be derived.