Geometrical Parameter Estimation for Industrial Manipulators Using Two-step Estimation Schemes

This paper proposes a new solution for estimating the kinematic parameters of a manipulator, using a two-step linear estimator. The estimator is based on the recursive least-squares (RLS) method. The main characteristics of the method are that one singularity in estimation is overcome and parameters estimates approach the true values more quickly and smoothly than for algorithms using a one-step estimator. The algorithm can be applied for the estimation of parameters of both serial-link robots and robots having a kinematic closed-loop. Simulation results are given, comparing the proposed method with conventional approaches.     

Frequency domain identification of Wiener models

This paper proposes a frequency domain algorithm for Wiener model identifications based on exploring the fundamental frequency and harmonics generated by the unknown nonlinearity. The convergence of the algorithm is established in the presence of white noise. No a priori knowledge of the structure of the nonlinearity is required and the linear part can be nonparametric.     

An addendum and correction to: ‘On identification and adaptive estimation for systems with interrupted observations’

An error in the proof of the Theorem 1 of a previous paper of the author's is corrected. It is also pointed out that an important class of system/signal models, called composite sources, can be cast into the framework of the model considered in the referenced paper. Thus, the parameter estimation/system identification results of the paper are applicable to the composite source system model.     

An auto-programming system of MAG welding parameters for vision-based robot

The on-line auto-programming of MAG welding parameters for a vision-based robot mainly aims at improving the adaptability of a welding robot in a complex welding environment, and it is a new kind of intelligent control method of weld quality. The authors developed an experimental system of an MAG welding robot with 3-D vision according to the requirements of real-time auto-programming of welding parameters. In the system, the scam geometry parameters, i.e., root gap and root face in a single V-groove, are used as input variables, while the welding process parameters, i.e., welding current and speed, are used as output variables. The relation between input and output variables is described by a fuzzy model. Experimental results show that the system can result in increased weld quality.     

Frequency-domain subspace system identification using non-parametric noise models

In the general case of non-uniformly spaced frequency-domain data and/or arbitrarily coloured disturbing noise, the frequency-domain subspace identification algorithms described in McKelvey, Akçay, and Ljung (IEEE Trans. Automatic Control 41(7) (1996) 960) and Van Overschee and De Moor (Signal Processing 52(2) (1996) 179) are consistent only if the covariance matrix of the disturbing noise is known. This paper studies the asymptotic properties (strong convergence, convergence rate, asymptotic normality, strong consistency and loss in efficiency) of these algorithms when the true noise covariance matrix is replaced by the sample noise covariance matrix obtained from a small number of independent repeated experiments. As an additional result the strong convergence (in case of model errors), the convergence rate and the asymptotic normality of the subspace algorithms with known noise covariance matrix follows.     

Iterative identification of Hammerstein systems

Convergences of iterative algorithms have been established for identification of Hammerstein systems in the case that the unknown nonlinearities are odd. Then, the results are further extended to nonsmooth nonlinearities.     

A finite element formulation for dynamic parameter identification of robot manipulators

In this paper a finite element based approach is described for the automatic generation of models suitable for dynamic parameter identification. The method involves a nonlinear finite element formulation in which both links and joints are considered as specific finite elements [6, 7]. Since the identification procedure considers rigid-link robot models, the inertial properties of the link elements are described using a lumped mass formulation. The parameters to be identified are masses, first-order moments and inertial tensor components of the links. The equations of motion are written in a form which is linear in the dynamic parameters. This formulation is obtained by employing Jourdain’s principle of virtual power. The parameters are estimated using a linear least squares technique. Singular value decomposition of the regression matrix is used to find the minimum parameter set. Simulation results obtained from the 6 DOF PUMA 560 robot based on the estimated parameters show that the method yields accurate responses.     

A revised Durbin-Wu-Hausman test for industrial robot identification

This paper addresses the topic of robot identification. The usual identification method makes use of the inverse dynamic model (IDM) and the least squares (LS) technique while robot is tracking exciting trajectories. Assuming an appropriate bandpass filtering, good results can be obtained. However, the users are in doubt whether the columns of the observation matrix (the regressors) are uncorrelated (exogenous) or correlated (endogenous) with the error terms. The exogeneity condition is rarely verified in a formal way whereas it is a fundamental condition to obtain unbiased LS estimates. In Econometrics, the Durbin-Wu-Hausman test (DWH-test) is a formal statistic for investigating whether the regressors are exogenous or endogenous. However, the DWH-test cannot be straightforwardly used for robot identification because it is assumed that the set of instruments is valid. In this paper, a Revised DWH-test suitable for robot identification is proposed. The revised DWH-test validates/invalidates the instruments chosen by the user and validates the exogeneity assumption through the calculation of the QR factorization of the augmented observation matrix combined with a F-test if required. The experimental results obtained with a 6 degrees-of-freedom (DOF) industrial robot validate the proposed statistic.     

基于遗传算法的蛇形机器人CPG模型参数优化

针对蛇形机器人采用的循环抑制CPG模型,为解决CPG控制模型中参数整定效率低、不稳定的问题,阐述基于CPG模型的蛇形搜救机器人控制系统总体方案的设计,提出一种基于遗传算法的CPG控制模型参数优化方法,实现链式CPG网络的节律输出。仿真实现蛇形搜救机器人各关节控制信号的有效输出,仿真结果表明,该方法具有高效、准确、稳定等优点,可有效应用于蛇形搜救机器人的步态控制。     

A VSS identification scheme for time-varying parameters

Based on variable structure system theory and sliding mode, we develop an identification scheme suitable for time-varying parameters. The new identification scheme, working in closed-loop, addresses several key issues in system identification simultaneously: unstable process, highly nonlinear and uncertain dynamics, fast time varying parameters and rational nonlinear in the parametric space. Other important issues associated with identification, such as the persistent excitation property and insensitivity to measurement noise, are also discussed. A robotic manipulator is used as the illustrative example and the basis for comparison.     

Decoupled delay estimation in the identification of differential delay systems

Based on the variable projection functional in nonlinear least-squares theory, the estimation of pure time delay is decoupled from the determination of the remaining system parameters for a class of differential delay models. The approach utilizes input-output data on a fixed finite time interval and avoids estimating unknown initial conditions. Results of a simulation study are summarized for several examples.     

Optimal and suboptimal smoothing algorithms for identification of time-varying systems with randomly drifting parameters

Noncausal estimation algorithms, which involve smoothing, can be used for off-line identification of nonstationary systems. Since smoothing is based on both past and future data, it offers increased accuracy compared to causal (tracking) estimation schemes, incorporating past data only. It is shown that efficient smoothing variants of the popular exponentially weighted least squares and Kalman filter-based parameter trackers can be obtained by means of backward-time filtering of the estimates yielded by both algorithms. When system parameters drift according to the random walk model and the adaptation gain is sufficiently small, the properly tuned two-stage Kalman filtering/smoothing algorithm, derived in the paper, achieves the Cramér-Rao type lower smoothing bound, i.e. it is the optimal noncausal estimation scheme. Under the same circumstances performance of the modified exponentially weighted least-squares algorithm is often only slightly inferior to that of the Kalman filter-based smoother.     

On the identification of non-linear models of unmanned underwater vehicles

This documentation presents a comparison between two identification methods for the off-line identification of non-linear models of unmanned underwater vehicles (UUVs), one based on the minimization of the acceleration prediction error (direct method) and another one based on the minimization of the velocity one step prediction error (integral method). The direct method has already been used in UUV's identification (IFAC Conference CAMs’ 2001, Control Application in Marine Systems, Glasgow, Scotland, UK, 2001). Our new proposal, the integral method, can be applied to a quite general class of non-linear multivariable models and is characterized by an excellent numerical performance. Both methods are compared through their application to the identification of the dynamic model of URIS UUV. Results suggest that better models can be obtained using the proposed method (integral method).     

Performance analysis of multi-innovation gradient type identification methods

It is well-known that the stochastic gradient (SG) identification algorithm has poor convergence rate. In order to improve the convergence rate, we extend the SG algorithm from the viewpoint of innovation modification and present multi-innovation gradient type identification algorithms, including a multi-innovation stochastic gradient (MISG) algorithm and a multi-innovation forgetting gradient (MIFG) algorithm. Because the multi-innovation gradient type algorithms use not only the current data but also the past data at each iteration, parameter estimation accuracy can be improved. Finally, the performance analysis and simulation results show that the proposed MISG and MIFG algorithms have faster convergence rates and better tracking performance than their corresponding SG algorithms.     

六维腕力传感器惯性参数的在线识别

腕力传感器的动态特性对机器人系统的动态特性、精确运动和控制有不可忽视的影响。将六维腕力传感器置于机器人系统中 ,基于腕力传感器坐标系内的微分运动 ,结合机器人运动学和动力学知识 ,研究了在线识别六维腕力传感器惯性参数的方法     

A geometric approach for the analysis and computation of the intrinsic camera parameters

The authors of this paper adopted the projected characteristics of the absolute conic in terms of the Pascal's theorem to propose an entirely new camera calibration method based on purely geometric thoughts. The use of this theorem in the geometric algebra framework allows us to compute a projective invariant using the conics of only two images which expressed using brackets helps us to set enough equations to solve the calibration problem. The method requires restricted controlled camera movements. Our method is less sensitive to noise as the Kruppa's-equation-based methods. Experiments with simulated and real images confirm that the performance of the algorithm is reliable.     

Importance analysis for models with correlated input variables by the state dependent parameters method

For clearly exploring the origin of the variance of the output response in case the correlated input variables are involved, a novel method on the state dependent parameters (SDP) approach is proposed to decompose the contribution by correlated input variables to the variance of output response into two parts: the uncorrelated contribution due to the unique variations of a variable and the correlated one due to the variations of a variable correlated with other variables. The correlated contribution is composed by the components of the individual input variable correlated with each of the other input variables. An effective and simple SDP method in concept is further proposed to decompose the correlated contribution into the components, on which a second order importance matrix can be solved for explicitly exposing the contribution components of the correlated input variable to the variance of the output response. Compared with the existing regression-based method for decomposing the contribution by correlated input variables to the variance of the output response, the proposed method is not only applicable for linear response functions, but is also suitable for nonlinear response functions. It has advantages both in efficiency and accuracy, which are demonstrated by several numerical and engineering examples.     

Maximum likelihood based identification methods for rational models

ABSTRACT

In a rational model, some terms of the information vector are correlated with the noise, which makes the traditional least squares based iterative algorithms biased. In order to overcome this shortcoming, this paper develops two recursive algorithms for estimating the rational model parameters. These two algorithms, based on the maximum likelihood principle, have three integrated key features: (1) to establish two unbiased maximum likelihood recursive algorithms, (2) to develop a maximum likelihood recursive least squares (ML-RLS) algorithm to decrease the computational efforts, (3) to update the parameter estimates by the ML-RLS based particle swarm optimisation (ML-RLS-PSO) algorithm when the noise-to-output ratio is large. Comparative studies demonstrate that (1) the ML-RLS algorithm is only valid for rational models when the noise-to-output ratio is small, (2) the ML-RLS-PSO algorithm is effective for rational models with random noise-to-output ratio, but at the cost of heavy computational efforts. Furthermore, the simulations provide cases for potential expansion and applications of the proposed algorithms.     

Parameter identification for models with bounded errors in all variables

In this paper, the problem of parameter identification for models with bounded measurement errors both on the input and on the output is addressed and some corrections to previously published results are presented. In particular, it is shown that only parameter overbounds can in general be computed for systems of the form y = (φ + δφ)θ + δy when the bounded measurement errors δφ and δy are correlated. Since ARMAX and bilinear systems can be represented in this form, it turns out that tight parameter bounds are in general not available for these systems. Finally, we show that it is possible to check a posteriori whether the obtained bounds are tight or not.     

On closed-loop system identification using polyspectral analysis given noisy input–output time-domain data

The problem of closed-loop system identification given noisy input–output measurements is considered. It is assumed that the closed-loop system operates under an external non-Gaussian input which is not measured. If the external input has non-vanishing integrated bispectrum (IB) and data IB is used for identification, then the various disturbances/noise processes affecting the system are assumed to be zero-mean stationary with vanishing IB. If the external input has non-vanishing integrated trispectrum (IT) and data IT is used for identification, then the various disturbances/noise processes affecting the system are assumed to be zero-mean stationary Gaussian. Noisy measurements of the (direct) input and output of the plant are assumed to be available. The closed-loop system must be stable but it is allowed to be unstable in open loop. Parametric modeling of the various noise sequences affecting the system is not needed. First the open-loop transfer function is estimated using the integrated polyspectrum and cross-polyspectrum of the time-domain input–output measurements. Then two existing techniques for parametric system identification given consistent estimates of the underlying transfer function, are exploited. The parameter estimators are strongly consistent. Asymptotic performance analysis is also carried out. A computer simulation example using an unstable open-loop system is presented to illustrate the proposed approach.