基于观测器摩擦补偿的机电系统高精度控制

针对机电作动系统在低速阶段摩擦非线性明显且同时存在其他干扰,易导致系统跟踪精度、稳定性下降这一问题,设计基于非线性观测器摩擦补偿的自适应鲁棒控制器. 针对摩擦非线性,利用LuGre摩擦模型描述系统的摩擦现象,提出非线性观测器对模型的内部摩擦状态进行观测. 针对系统摩擦系数、转动惯量及其他不确定性参数,设计参数自适应律进行估计. 利用前馈补偿的方法,对摩擦非线性和参数不确定性进行补偿,设计鲁棒项克服系统的其他扰动. 利用Lyapunov稳定性定理证明了提出的控制器在存在扰动的情况下可以实现系统的有界稳定性. 实验结果表明,提出的控制器具有较高的控制精度与较强的鲁棒性,跟踪精度较传统的PID控制器提高了一个数量级.     

不确定离散系统的鲁棒Gl2滤波器设计

对于凸多面体不确定离散系统,研究了满足误差估计系统鲁棒稳定且满足一定Gl₂性能指标的线性全阶稳定滤波器设计方法.分别给出了确定性和不确定性离散系统具有Gl₂性能的线性矩阵不等式（linear matrix inequality, LMI)充要条件和充分条件.在此基础上,通过使用滤波器参数化方法,分别推导出了以LMI描述的确定性和不确定性系统的Gl₂滤波器存在的充要条件和充分条件,进而可以使用LMI凸优化程序求解滤波器.通过对算例的计算,结果表明,在噪声信号为独立范数有界情况下,鲁棒Gl₂滤波比H_∞滤波具有更少的保守性.     

多类型源储协调互动的配电网分布鲁棒优化调度

分布式光伏、风电等可再生能源出力具有较强的随机性和不确定性,其大量并网给配电网的运行带来了极大的挑战。本文提出了一种多类型源储协调互动的配电网分布鲁棒优化调度方法,基于分布鲁棒优化理论,对配电网中传统离散设备、可再生能源以及储能决策进行协调优化,提高配电网运行的经济性和安全性,实现了配电网运行决策保守性与鲁棒性的有效平衡。首先,考虑多类型源储资源的协调互动,建立配电网优化调度模型,并将其转化为混合整数二阶锥规划的形式;其次,利用可再生能源出力场景集进行不确定性刻画,建立分布鲁棒优化模型,并通过列和约束生成算法进行求解;最后,在PG&E69节点系统上进行算例分析,验证了所提方法的可行性与准确性。     

板料成形优化技术进展与质量工程研究

板料成形过程的优化研究对于改进模具设计、提高产品质量具有重要意义。综述了板料成形工艺优化的研究现状。考虑到在有约束的设计优化中,最优结果常位于约束边界附近,随机因素的波动易导致产品质量不稳定甚至失效,难以保持产品的可靠性和稳健性。为此,本文应用质量工程学中的6σ理念,提出了基于数值模拟的板料成形6σ稳健设计优化方法。     

SOME PROPERTIES OF THE MAXIMUM LIKELIHOOD ESTIMATOR IN THE SIMULTANEOUS SWITCHING AUTOREGRESSIVE MODEL

Abstract. The simultaneous switching autoregressive (SSAR) model proposed by Kunitomo and Sato (A non-linearity in economic time series and disequilibrium econometric models. In Theory and Application of Mathematical Statistics (ed. A. Takemura). Tokyo:University of Tokyo Press (in Japanese), 1994; Asymmetry in economic time series and simultaneous switching autoregressive model. Struct. Change Econ. Dyn. , forthcoming (1994).) is a Markovian non-linear time series model. We investigate the finite sample as well as the asymptotic properties of the least squares estimator and the maximum likelihood (ML) estimator. Due to a specific simultaneity involved in the SSAR model, the least squares estimator is badly biased. However, the ML estimator under the assumption of Gaussian disturbances gives reasonable estimates.     

Consistent estimation of the memory parameter for nonlinear time series

Abstract. For linear processes, semiparametric estimation of the memory parameter, based on the log‐periodogram and local Whittle estimators, has been exhaustively examined and their properties well established. However, except for some specific cases, little is known about the estimation of the memory parameter for nonlinear processes. The purpose of this paper is to provide the general conditions under which the local Whittle estimator of the memory parameter of a stationary process is consistent and to examine its rate of convergence. We show that these conditions are satisfied for linear processes and a wide class of nonlinear models, among others, signal plus noise processes, nonlinear transforms of a Gaussian process ξ_t and exponential generalized autoregressive, conditionally heteroscedastic (EGARCH) models. Special cases where the estimator satisfies the central limit theorem are discussed. The finite‐sample performance of the estimator is investigated in a small Monte Carlo study.     

基于鲁棒一步集的Tube不变集鲁棒模型预测控制

针对一类干扰有界的输入和状态受约束线性离散系统,提出了一种基于鲁棒一步集的Tube不 变集鲁棒模型预测控制方法.首先采用多面体不变集离线设计方法得到基于多面体不变集序列的扩 展终端约束集;然后为了扩大鲁棒模型预测控制的初始状态允许区域,并提高系统的鲁棒性,在扩展终端约束集的基础上,通过引入鲁棒一步集并借助Tube不变集控制策略,设计了基于鲁棒一步集的鲁棒模型预测控制方法,并给出了算法的存在性和稳定性证明. 该方法不仅极大地扩大了初始状态允许区域,而且对有界干扰具有有效的抑制作用,使得受扰系统收敛到以原点为中心的最小鲁棒正不变集内.最后仿真验证了算法的有效性.     

混杂系统的一致输入输出对状态稳定性

混杂系统的输入输出对状态稳定性是混杂控制系统领域极富挑战性的课题之一. 为了观测混杂系统的状态,本文提出了一类混杂系统的一致输入输出对状态稳定的充分条件,分析了混杂系统的一致输入输出对状态稳定性、光滑Lyapunov函数存在性和状态模估计器存在性三者之间的关系. 借助状态模估计器将混杂系统化为受扰动系统,获得了受扰动系统一致输入输出对状态稳定性的结果,并进一步证明了混杂系统的一致输入输出对状态稳定性.     

带观测滞后和不确定噪声方差的多智能体传感网络鲁棒序贯协方差交叉融合Kalman滤波

针对带观测滞后和不确定噪声方差的分簇多智能体传感网络系统,研究鲁棒序贯协方差交叉融合Kalman滤波器的设计问题.应用最邻近法则,传感网络被分成簇.应用极大极小鲁棒估计原理,基于带噪声方差最差保守上界的最差保守传感网络系统,提出了两级序贯协方差交叉(SCI)融合鲁棒稳态Kalman滤波器,可减小通信和计算负担并节省能量,且保证实际滤波误差方差有一个最小保守上界.一种Lyapunov方程方法被提出用于证明局部和融合滤波器的鲁棒性.提出了鲁棒精度的概念且证明了局部和融合鲁棒Kalman滤波器的鲁棒精度关系.证明全局SCI融合器的鲁棒精度高于每簇SCI融合器的精度且两者的鲁棒精度都高于每个局部鲁棒滤波器的精度.一个跟踪系统的仿真例子证明了鲁棒性和鲁棒精度关系.     

Reliability-based robust design optimization: A general methodology using genetic algorithm

In this paper, we present an improved general methodology including four stages to design robust and reliable products under uncertainties. First, as the formulation stage, we consider reliability and robustness simultaneously to propose the new formulation of reliability-based robust design optimization (RBRDO) problems. In order to generate reliable and robust Pareto-optimal solutions, the combination of genetic algorithm with reliability assessment loop based on the performance measure approach is applied as the second stage. Next, we develop two criteria to select a solution from obtained Pareto-optimal set to achieve the best possible implementation. Finally, the result verification is performed with Monte Carlo Simulations and also the quality improvement during manufacturing process is considered by identifying and controlling the critical variables. The effectiveness and applicability of this new proposed methodology is demonstrated through a case study.