Noncertainty equivalent adaptive control of robot manipulators without velocity measurements

This paper presents a noncertainty equivalent adaptive motion control scheme for robot manipulators in the absence of link velocity measurements. A new output feedback adaptation algorithm, based on the attractive manifold design approach, is developed. A proportional-integral adaptation is selected for the adaptive parameter estimator to strengthen the passivity of the system. In order to relieve velocity measurements, an observer is designed to estimate the velocities. The controller guarantees semiglobal asymptotic motion tracking and velocity estimation, as well as L_∞ and L₂ bounded parameter estimation error. The effectiveness of the proposed controller is verified by simulations for a two-link robot manipulator and a four-bar linkage. The results are further compared with the earlier certainty-equivalent adaptive partial and full state feedback controller to highlight potential closed-loop performance improvements.     

PARAMETER ESTIMATION IN EXPONENTIAL MODELS

Abstract. In this paper the problems of parameter estimation and order determination of an exponential (EX) model are studied in the time domain. In order to estimate the parameters, the parameter equations of an EX model are given in terms of the autocorrelation function, which is similar to the Yule-Walker equations of an autoregressive moving-average model. Estimates of parameters are obtained with the aid of the parameter equations and theorems are proved relating the convergence rate and asymptotic distribution of the estimates. We present two kinds of methods for estimating the order and prove that the estimates of the order are consistent.     

SIMULATION AND ESTIMATION OF LONG MEMORY CONTINUOUS TIME MODELS

Abstract. Some general properties of long memory continuous time processes are recalled or proved. Methods of simulation are studied. A comparison with the usual discrete time autoregressive fractionally integrated moving-average filter is made and illustrations are provided. Then, two methods of estimation of the parameters of such a model from a discrete sample are studied, both theoretically and empirically, with Monte Carlo experiments.     

PERIODIC CORRELATION IN STRATOSPHERIC OZONE DATA

Abstract. A 50-year time series of monthly stratospheric ozone readings from Arosa, Switzerland, is analyzed. The time series exhibits the properties of a periodically correlated (PC) random sequence with annual periodicities. Spectral properties of PC random sequences are reviewed and a test to detect periodic correlation is presented. An autoregressive moving-average (ARMA) model with periodically varying coefficients (PARMA) is fitted to the data in two stages. First, a periodic autoregressive model is fitted to the data. This fit yields residuals that are stationary but non-white. Next, a stationary ARMA model is fitted to the residuals and the two models are combined to produce a larger model for the data. The combined model is shown to be a PARMA model and yields residuals that have the correlation properties of white noise.     

自适应混合多目标分布估计进化算法

针对多目标分布估计算法全局收敛性较弱的缺陷,提出了一种自适应混合多目标分布估计进化算法。其基本思想是:在多目标分布估计算法中引入全局收敛性较强的差分进化算法,当函数变化率较大时,用分布估计算法产生新种群;当函数变化率较小即算法可能陷入局部收敛时,用差分进化算法产生新种群。理论分析和数值实验结果表明,这种混合算法不仅具有良好的全局收敛性,而且解的分布性和均匀性较没有考虑目标函数变化率的混合多目标分布估计算法也有了一定程度的提高。     

序贯抗差估计在旋转调制陀螺寻北中的应用

基于序贯抗差估计方法针对简化了的旋转调制陀螺寻北仪输出信号进行了寻北仿真研究.介绍了该型寻北仪的基本原理,在经典充贯平差的基础上,结合抗差M估计的原理,得到了序贯抗差估计的计算流程与公式.将寻北仪输出信号划分为2个阶段,对上述方法进行了仿真应用.仿真结果表明：通过该算法所得寻北结果误差均值与标准差均优于其它算法.     

RECURSIVE GENERALIZED M ESTIMATES FOR AUTOREGRESSIVE MOVING-AVERAGE MODELS

Abstract. Outliers in time series seriously affect conventional parameter estimates. In this paper a robust recursive estimation procedure for the parameters of auto-regressve moving-average models with additive outliers is proposed. Using 'cleaned' residuals from an initial robust fit of an autoregression of high order as input, bounded influence regression is applied recursively. The proposal follows certain ideas of Hannan and Rissanen, who suggested a three-stage procedure for order and parameter estimation in a conventional setting.
A Monte Carlo study is performed to investigate the robustness properties of the proposed class of estimates and to compare them with various other suggestions, including least squares, M estimates, residual autocovariance and truncated residual autocovariance estimates. The results show that the recursive generalized M estimates compare favourably with them. Finally, possible modifications to master even vigourous situations are suggested.     

FAST LINEAR ESTIMATION METHODS FOR VECTOR AUTOREGRESSIVE MOVING-AVERAGE MODELS

Abstract. Three linear methods for estimating parameter values of vector auto-regressive moving-average (VARMA) models which are in general at least an order of magnitude faster than maximum likelihood estimation are developed in this paper. Simulation results for different model structures with varying numbers of component series and observations suggest that the accuracy of these procedures is in most cases comparable with maximum likelihood estimation. Procedures for estimating parameter standard error are also discussed and used for identification of nonzero elements in the VARMA polynomial structures. These methods can also be used to establish the order of the VARMA structure. We note, however, that the primary purpose of these estimates is to generate initial estimates for the nonzero parameters in order to reduce subsequent computational time of more efficient estimation procedures such as exact maximum likelihood.     

多传感器数据采集系统中的数据融合研究

针对多传感器数据采集系统的准确性与可靠性的要求,提出了单传感器分批估计融合与模糊理论中的相关性函数与加权自适应算法相结合的数据融合方法。首先,对每一只传感器进行分批估计融合,得到了较为可靠的局部决策值。然后,利用模糊理论中的相关性函数,计算出每只传感器与其它传感器的相关程度,并剔除掉那些相关程度较低的传感器的局部决策值。最后,在权的最优分配原则下,利用加权自适应算法对剩下的局部决策值进行数据融合。该方法能在数据先验知识未知的情况下,客观地反映各个传感器的可靠程度。数据分析结果表明:与同类融合方法相比,该方法具有更高的精度。     

Experimental and theoretical study of membrane-aerated biofilm reactor behavior under different modes of oxygen supply for the treatment of synthetic wastewater

This study compares, experimentally and theoretically, five different modes of supplying oxygen to a membrane-aerated biofilm reactor (MABR), and search for the more efficient ways of treating wastewaters. A single-tube MABR was used to measure the decrease of an organic substrate (sodium acetate) in water by supplying oxygen in different modes, namely: (1) by feeding the membrane tube either with oxygen or air (or none of them); (2) in some cases by simultaneous sparging air to the residual water. The dynamics of the substrate and oxygen consumption were measured during the batch experiment, and two mathematical models are developed to predict their transient responses using a Monod kinetic with dual substrate limitation. The models predict biomass growth and the production of extracellular polymer substances (EPS), which in turn causes the biofilm to grow; they account for the counter-diffusion of substrate and oxygen within the EPS structure that contains the cells, and one of them incorporates the mass transport by convection and diffusion in the surrounding liquid contained inside the interconnected pores and channels within the biofilm. Transport and kinetic parameters are estimated from experiments, and both models successfully predict concentration measurements in some of the set of experiments. It was found that all of the modes of oxygen supplied in a MABR were more efficient than the traditional suspended cell process.