非匹配不确定高阶非线性系统的滑模控制新方法

对具有非匹配不确定的高阶非线性系统,采用改进的高阶滑模微分器获取已知状态的任意阶微分估计值,再以恰当阶次的状态微分估计值之差,得到非匹配不确定项及其微分的估计值,证明了其误差任意小.为避免奇异性和抖振,采用两种方案设计了滑模控制器,并设计鲁棒项提高系统鲁棒性.基于Lyapunov~论证明了系统稳定性.同现有其他方法相比,该方法具有适用范围更广、收敛速度快、控制精度高、运算量小、保守性低等优点.最后仿真证明了本文所有结论.     

非周期时变非线性系统自适应迭代学习控制

对一类含有非周期时变不确定性的非线性系统的控制问题进行了研究。该系统具有严格反馈形式且控制增益未知。在控制器设计中,用一种具有迭代特性的神经网络消除了非周期时变不确定性的影响,并综合应用反演技术和鲁棒自适应控制技术消除了严格反馈结构和未知控制增益带来的设计问题。稳定性分析结果表明:系统所有状态量有界且输出量在积分意义下收敛到期望轨迹。仿真试验证明了所设计控制器的有效性。     

An Intelligent Robust Tracking Control for a Class of Electrically Driven Mobile Robots

This paper addresses the problem of designing robust tracking control for a class of uncertain wheeled mobile robots actuated by brushed direct current motors. This class of electrically‐driven mechanical systems consists of the robot kinematics, the robot dynamics, and the wheel actuator dynamics. Via the backstepping technique, an intelligent robust tracking control scheme that integrates a kinematic controller and an adaptive neural network‐based (or fuzzy‐based) controller is developed such that all of the states and signals of the closed‐loop system are bounded and the tracking error can be made as small as possible. Two adaptive approximation systems are constructed to learn the behaviors of unknown mechanical and electrical dynamics. The effects of both the approximation errors and the unmodeled time‐varying perturbations in the input and virtual‐input weighting matrices are counteracted by suitably tuning the control gains. Consequently, the robust control scheme developed here can be employed to handle a broader class of electrically‐driven wheeled mobile robots in the presence of high‐degree time‐varying uncertainties. Finally, a simulation example is given to demonstrate the effectiveness of the developed control scheme.     

ON THE PARTIAL SUMS OF RESIDUALS IN AUTOREGRESSIVE AND MOVING AVERAGE MODELS

Abstract. The limiting process of partial sums of residuals in stationary and invertible autoregressive moving-average models is studied. It is shown that the partial sums converge to a standard Brownian motion under the assumptions that estimators of unknown parameters are root- n consistent and that innovations are independent and identically distributed random variables with zero mean and finite variance or, more generally, are martingale differences with moment restrictions specified in Theorem 1. Applications for goodness-of-fit and change-point problems are considered. The use of residuals for constructing nonparametric density estimation is discussed.     

Optimal design of a distribution-free quality control scheme for cost-efficient monitoring of unknown location

Traditionally, a cost-efficient control chart for monitoring product quality characteristic is designed using prior knowledge regarding the process distribution. In practice, however, the functional form of the underlying process distribution is rarely known a priori. Therefore, the nonparametric (distribution-free) charts have gained more attention in the recent years. These nonparametric schemes are statistically designed either with a fixed in-control average run length or a fixed false alarm rate. Robust and cost-efficient designs of nonparametric control charts especially when the true process location parameter is unknown are not adequately addressed in literature. For this purpose, we develop an economically designed nonparametric control chart for monitoring unknown location parameter. This work is based on the Wilcoxon rank sum (hereafter WRS) statistic. Some exact and approximate procedures for evaluation of the optimal design parameters are extensively discussed. Simulation results show that overall performance of the exact procedure based on bootstrapping is highly encouraging and robust for various continuous distributions. An approximate and simplified procedure may be used in some situations. We offer some illustration and concluding remarks.     

Topology optimization of continuum structures subjected to uncertainties in material properties

This work addresses the use of the topology optimization approach to the design of robust continuum structures under the hypothesis of uncertainties with known second‐order statistics. To this end, the second‐order perturbation approach is used to model the response of the structure, and the midpoint discretization technique is used to discretize the random field. The objective function is a weighted sum of the expected compliance and its standard deviation. The optimization problem is solved using a traditional optimality criteria method. It is shown that the correlation length plays an important role in the obtained topology and statistical moments when only the minimization of the standard deviation is considered, resulting in more and thinner reinforcements as the correlation length decreases. It is also shown that the minimization of the expected value is close to the minimization of the deterministic compliance for small variations of Young's modulus. Copyright © 2016 John Wiley & Sons, Ltd.     

基于鲁棒右互质分解的直流伺服系统精确跟踪控制

对含有不确定性的直流伺服控制系统,通过应用鲁棒右互质分解方法,设计了一种基于鲁棒右互质分解的精确跟踪控制系统;通常情况下,直流伺服系统中存在诸如非线性特性及参数辨识引起的模型误差和外界扰动,在设计的控制系统中,未知模型误差和外界扰动对系统性能的影响都被看作直流伺服系统的不确定性;在考虑到这些不确定性情况下,设计了一种基于鲁棒右互质分解理论的精确跟踪控制;首先在考虑未知的不确定模型影响系统性能指标的情况下,设计了一种基于演算子理论的反馈控制结构,此结构可以消除不确定模型的影响,在此基础上,设计了基于鲁棒右互质分解的鲁棒精确跟踪控制系统,得出精确跟踪条件;仿真结果表明使用提出的方法可以有效地消除不确定性,使得伺服系统具有很强的鲁棒性和精确跟踪能力。     

含输入死区系统的滤波误差初始修正迭代学习控制

针对一类输入环节含死区非线性特性且误差初值非零的非参数不确定系统,提出滤波误差初始修正学习控制方案,分别解决死区斜率下限可知与未知两种情形下的轨迹跟踪问题.给出了两种修正滤波误差信号构造方法,并根据Lyapunov综合方法设计学习控制器,采用鲁棒学习策略处理非参数不确定性和死区非线性特性.经过足够多次迭代后,实现滤波误差在预设的作业区间也收敛于零.文中所提出的控制方案,具有构造简单与实施方便的特点,仿真结果表明了本文所提控制方法的有效性.     

基于改进的FNNs移动机械臂输出反馈跟踪控制

针对包含电机动态模型的移动机械臂系统,提出一种鲁棒自适应输出反馈控制方法.将误差符号函数鲁棒积分反馈与神经网络前馈结构相结合用于控制器的设计,然后利用神经网络去逼近机器人和电机系统的不确定项,设计鲁棒项实时补偿网络误差.通过Lyapunov稳定性分析证明闭环系统所有信号半全局一致有界.最后仿真实验表明,控制方法对系统动态不确定性和外界干扰有很好的鲁棒性,可实现移动机械臂的输出反馈跟踪控制.