Adaptive control of robot manipulators based on passivity

New adaptation algorithm is presented for adaptive control of robot manipulators. The passivity property of the proposed algorithm is first established, then the stability is proved based on the passivity properties of the plant and those of the proposed algorithm. Because of the use of the past information and averaging effect, this algorithm gives a smoother tracking and parameter error and a parameter convergence under a weaker excitation condition     

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.     

时变迭合AR模型的参数估计*

首次提出了时变迭合ＡＲ模型,该模型在实际应用中具有广泛的应用价值．应用两步最小二乘法和限定记忆递推最小二乘法,给出了模型中时变参数的递推估计算法,该算法仅依靠量测数据即能自适应进行．仿真计算及应用结果表明：算法能够自适应地跟踪量测数据模型参数的变化,效果是令人满意的．     

基于最小二乘的UWB信道盲估计算法

针对超宽带系统的离散信道模型,利用接收信号的一阶统计量,提出一种基于最小二乘（LS）的盲信道估计算法。利用接收信号的循环卷积特性,在一个符号间隔内建立模型,最后利用LS算法求解。仿真表明,该算法与基于导频序列的ML估计方法和LS估计方法相比,均方误差（MSE）性能相差不大,但计算复杂度明显降低,同时提高了系统传输效率。     

基于抗差估计方法的WSN节点定位算法研究

针对无线传感器网络在实际应用中,节点间测距结果往往含有粗差,并会严重影响未知节点坐标估计值的准确性和可靠性这一问题,引入抗差估计理论,采用IGGⅢ权因子函数,设计了一种基于抗差估计的节点定位算法。该算法能对含有不同幅度的测量误差分别采取保权、降权和淘汰等相应处理,明显提高定位精度。仿真实验表明:在无粗差的情况下,本文算法与经典最小二乘定位算法的定位效果保持了良好的等效性;在含有粗差的情况下,本文算法借助于选择的阈值,对不同的粗差采取剔除以及降权等适当处理,比经典最小二乘定位取得了更高的定位精度,保证了估计结果的无偏性,体现出良好的抗差性能。     

一种传感网时间同步和定位的联合线性估计方法

采用时间测量以估计节点位置的方法实现简单,在传感网中得到了广泛的使用。然而节点计时时钟存在漂移和偏离,导致时间测量不准确。为此文本以节点时钟漂移和偏离模型为基础,提出了一种时间同步和节点定位的联合线性估计方法,包括最小平方（LS）及权重最小平方（WLS）方法。仿真测试了所设计算法的运行时间,分析了噪声对联合估计方法的估计误差影响。结果表明,LS及WLS线性估计方法运算速度较半正定（SDP）算法快,在低噪声条件下LS及WLS线性估计方法具有较高的稳定性和定位精度。     

离散时间间接型模型参考自适应控制的一种系统化的分析方法

This paper presents the design and analysis of indirect model reference adaptive control (MRAC) with normalized adaptive law for a class of discrete-time systems. The main work includes three parts. Firstly, the constructed plant parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms but also avoids the possibility of division by zero. Secondly, by finding the relationship between the plant parameter estimate and controller parameter estimate and using the properties of plant parameter estimate, the similar properties of controller parameter estimate are also established. Thirdly, based on the relationship properties between the normalizing signal and all the signals in the closed-loop system and on some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and convergence analysis approach to the discrete indirect MRAC scheme is developed rigorously.     

基于最小二乘准则的多传感器参数估计数据融合

为了从含有加性测量噪声的线性测量数据中更加准确地估计未知的常值参数,测量噪声互不相关的多传感器测量系统得到广泛使用。在最小二乘准则下,提出了多传感器测量系统在多次同步测量时的集中式和分布式参数估计数据融合算法,两种算法完全等价,且都是全局最优的。数值仿真实验的结果表明,新算法可以明显改善传感器测量参数的估计精度。     

A revisit to block and recursive least squares for parameter estimation

In this paper, the classical least squares (LS) and recursive least squares (RLS) for parameter estimation have been re-examined in the light of the present day computing capabilities. It has been demonstrated that for linear time-invariant systems, the performance of blockwise least squares (BLS) is always superior to that of RLS. In the context of parameter estimation for dynamic systems, the current computational capability of personal computers are more than adequate for BLS. However, for time-varying systems with abrupt parameter changes, standard blockwise LS may no longer be suitable due to its inefficiency in discarding “old” data. To deal with this limitation, a novel sliding window blockwise least squares approach with automatically adjustable window length triggered by a change detection scheme is proposed. Two types of sliding windows, rectangular and exponential, have been investigated. The performance of the proposed algorithm has been illustrated by comparing with the standard RLS and an exponentially weighted RLS (EWRLS) using two examples. The simulation results have conclusively shown that: (1) BLS has better performance than RLS; (2) the proposed variable-length sliding window blockwise least squares (VLSWBLS) algorithm can outperform RLS with forgetting factors; (3) the scheme has both good tracking ability for abrupt parameter changes and can ensure the high accuracy of parameter estimate at the steady-state; and (4) the computational burden of VLSWBLS is completely manageable with the current computer technology. Even though the idea presented here is straightforward, it has significant implications to virtually all areas of application where RLS schemes are used.     

Parameter estimation with scarce measurements

In this paper, the problems of parameter estimation are addressed for systems with scarce measurements. A gradient-based algorithm is derived to estimate the parameters of the input–output representation with scarce measurements, and the convergence properties of the parameter estimation and unavailable output estimation are established using the Kronecker lemma and the deterministic version of the martingale convergence theorem. Finally, an example is provided to demonstrate the effectiveness of the proposed algorithm.     

A Computationally Efficient Received Signal Strength Based Localization Algorithm in Closed-Form for Wireless Sensor Network

Ranging error is known to degrade significantly the target node localization accuracy. This paper investigates the use of computationally efficient positioning solution of least square (LS) in closed-form, to reduce localization accuracy loss caused by ranging error. For range-based node localization, the LS solution based on least square criterion has been confirmed to exhibit capability of optimum estimation but extensively achieve at a very complex calculation. In this paper we consider the problem how to acquire such LS solution provided with estimation performance at low complex calculation. In this paper, we use the Gauss noise model and use the weighted least squares criterion and the effective calculation method to solve the linearized equation derived from the RSS measurement, and put forward a new approach to estimate the performance of the target node location estimation. Based on the Fisher information matrix, the Cramér–Rao lower bound of target position estimation is derived based on received signal strength. We obviously indicate that the proposed algorithm can approximately achieve the LS solution in estimation performance at a markedly low complex calculation. Simulations are performed to show the improvement of the proposed algorithm.     

A new bias-compensating LS method for continuous system identification in the presence of coloured noise

A new bias-compensating least squares (LS) method is presented for the parameter estimation of linear single-input single-output (SISO) continuous-time systems. A discrete-time model obtained by using the linear integral filter is augmented by introducing a pre-filter on the input and then the parameters of the augmented model are estimated by the conventional LS method. The distinct characteristic roots of the pre-filter are used to estimate the bias in the LS estimate. The pre-filter should be chosen so that its frequency bandwidth is wider than those of the system and the input signals. Since the new method requires minimal information on the noise characteristics, it is easily applicable to the case of coloured noise.     

基于小波去噪与离散余弦变换相结合的正交频分复用系统信道估计算法

针对传统的基于离散余弦变换(DCT)信道估计算法没有处理循环前缀之内噪声的问题,提出了一种基于小波去噪与DCT插值相结合的正交频分复用(OFDM)系统信道估计方法。首先,采用最小二乘(LS)法对接收到的导频信号进行信道初步估计;然后,对LS法估计出的结果进行离散小波阈值去噪处理;最后,利用DCT插值对循环前缀内的噪声再次处理,以进一步减小噪声的影响。在Matlab 2012平台上仿真,与传统的基于DCT信道估计算法相比较,误码率相同的条件下,所提算法的信噪比(SNR)性能提升了1 dB左右;均方误差相同的条件下,所提算法的SNR性能提升2 dB左右。仿真实验结果表明,该算法能够较好地减小加性高斯白噪声(AWGN)的影响,并有效提高信道估计的准确度,其总体性能较基于DCT的信道估计算法更优。     

An adaptive least squares channel estimation based on conjugate gradient approach for multipath fading CDMA systems

Multiple access interference (MAI) is the main factor affecting the performance of channel estimation techniques for code division multiple access (CDMA) systems. Although, several multi-user channel estimation algorithms have been proposed to mitigate MAI, these algorithms require high computational complexities. In this paper, we address the problem of iterative least squares (LS) mobile channel estimation at high channel efficiency that requires a short training sequence along with the spreading sequences. We employ an efficient iterative method based on conjugate gradient (CG) algorithm to reduce the computational complexity of the estimation method. Computer simulations illustrate that the proposed method performs almost identical to the exact LS estimate for reasonable training lengths.     

Parameter Identification and Intersample Output Estimation for Dual-Rate Systems

In this paper, we derive a mathematical model for dual-rate systems and present a stochastic gradient identification algorithm to estimate the model parameters and an output estimation algorithm to compute the intersample outputs based on the dual-rate input-output data directly. Moreover, we investigate convergence properties of the parameter and intersample estimation, and we test the proposed algorithms with example systems, including an experimental water-level system.     

An efficient sparse channel estimator combining time-domain LS and iterative shrinkage for OFDM systems with IQ-imbalances

A time-domain(TD) least square(LS) channel estimator is first proposed to estimate channel parameters of OFDM system with IQ imbalances at both transmitter and receiver.Then,an iterative shrinkage(IS) algorithm from compressed sensing is adopted to further improve the estimation performance by using the TD-LS solution as the initial value of IS in the case of sparse channel.Simulation shows that our algorithm combining TD-LS and IS performs better on bit error rate than the frequency-domain LS and matching pursuit in sparse Hilly Terrain channel when the same LS equalizer is adopted.     

用岭估计研究以RS和GIS为基础的森林蓄积预报

在分析最小二乘（LS）估计预报森林蓄积可能存在缺陷的基础上,提出采用岭估计研究蓄积估测,并利用样地总蓄积预报偏差相对误差最小的方法,通过计算机仿真确定岭参数。实例计算表明,当影响蓄积估测的RS和GIS信息间存在复共线性时,用岭估计预报森林蓄积将优于LS估计。     

一种新的基于参数估计的故障诊断方法

传统的参数估计方法利用历史数据对参数进行估计,形成一次估计值序列,基于参数估计的故障诊断方法就是利用这个序列对故障进行诊断和分离的,但是,一次估计值序列跟踪真实参数变化存在明显的滞后,本文提出一种边估边修正参数序列的方法,从而克服了参数估计滞后,提高了参数估计的准确度,以此我们为故障检测和分离设计了一种新的补偿最小二乘算法,仿真表明这种算法用于故障诊断是有效的。     

Blind maximum likelihood estimation of traffic matrices under long-range dependent traffic

A new method, based on the maximum likelihood principle, through the numerical Expectation–Maximization algorithm, is proposed to estimate traffic matrices when traffic exhibits long-range dependence. The methods proposed so far in the literature do not account for long-range dependence. The method proposed in the present paper also provides an estimate of the Hurst parameter. Simulation results show that: (i) the estimate of the traffic matrix is more efficient than those obtained via existing techniques; (ii) the estimation error of the traffic matrix is lower for larger values of the true traffic intensity; (iii) the estimate of the Hurst parameter is slightly negatively biased.     

一种适用于DVB-T/H系统的自适应信道估计算法

提出了一种基于导频的自适应信道估计算法,该算法在最小平方误差(LS)算法的基础上采用两维插值方法估计出无线信道的频率响应:在时域上进行线性插值,在频域上根据系统软判决信噪比自适应地进行线性插值或维纳滤波插值。在地面数字视频广播(DVB-T)系统中的仿真结果表明,该算法具有较高的性能,而且在信噪比较高的情况下,它的复杂度很低,可以进一步应用于手持数字视频广播(DVB-H)系统中。