Multi-innovation auto-constructed least squares identification for 4 DOF ship manoeuvring modelling with full-scale trial data

This research is concerned with the problem of 4 degrees of freedom (DOF) ship manoeuvring identification modelling with the full-scale trial data. To avoid the multi-innovation matrix inversion in the conventional multi-innovation least squares (MILS) algorithm, a new transformed multi-innovation least squares (TMILS) algorithm is first developed by virtue of the coupling identification concept. And much effort is made to guarantee the uniformly ultimate convergence. Furthermore, the auto-constructed TMILS scheme is derived for the ship manoeuvring motion identification by combination with a statistic index. Comparing with the existing results, the proposed scheme has the significant computational advantage and is able to estimate the model structure. The illustrative examples demonstrate the effectiveness of the proposed algorithm, especially including the identification application with full-scale trial data.     

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.     

基于辅助模型的多新息广义增广随机梯度算法

将辅助模型辨识思想与多新息辨识理论相结合,利用系统可测信忠建立一个辅助模型.分别用辅助模型输出和噪声估计值代替辨识模型信忠向量中未知真实输出变量和不可测噪声项,并引入新忠长度扩展标量新息为新息向量,提出了Box-lenkins模型的辅助模型多新忠广义增广随机梯度辨识方法.所提出方法重复使用系统数据,能够改善参数估计精度,加快算法的收敛速度.     

多个未知时延的MISO系统的递推辨识

对于未知时延的多输入单输出（ＭＩＳＯ）系统,借助分离性原理,推导出迭代的可分离的非线性最小二乘（ＳＮＬＳ）辨识方法．为降低收敛于局部最小的可能性,利用全局优化理论,推导了全局可分离的非线性最小二乘（ＧＳＮＬＳ）辨识方法;为消除强观测噪声所引起的参数估计的偏差,将ＧＳＮＬＳ方法调整为一新颖的全局可分离的非线性多新息递推最小二乘（ＧＳＮＭＩＲＬＳ）辨识方法,仿真实验验证了算法的有效性．     

Grid index subspace constructed locally weighted learning identification modeling for high dimensional ship maneuvering system

For off-line locally weighted learning (LWL), all training data points need to be stored in memory, which would lead to a heavy computational burden, especially for large amount of training data. To avoid heavy computational burden in LWL, the grid index subspace constructed algorithm is presented for high dimensional ship maneuvering system in this study. First, high dimensional training data can be encoded and stored in equal interval grid, and training data are divided into grids. Second, query point is encoded by using the same strategy as in the first step, and the grid number which belongs to the query point is obtained. Third, the subspace would be per-allocated to the query point by using the grid index which has a light computational complexity. Different from the general cluster algorithm, a subspace rather than a neighborhood is assigned to query point. This way, LWL is carried out in a subspace, and the computational complexity is significantly reduced. As a consequence, real-time performance is effectively guaranteed. Finally, theoretical calculations and simulation examples are given to validate the effectiveness of the proposed scheme.     

Joint state and multi-innovation parameter estimation for time-delay linear systems and its convergence based on the Kalman filtering

This paper studies the joint state and parameter estimation problem for a linear state space system with time-delay. A multi-innovation gradient algorithm is developed based on the Kalman filtering principle. To improve the convergence rate, a filtering based multi-innovation gradient algorithm is proposed by using the filtering technique. The analysis indicates that the parameter estimates given by the proposed algorithms converge to their true values under the persistent excitation conditions. A simulation example is given to confirm that the proposed algorithms are effective.     

Auxiliary model-based interval-varying multi-innovation least squares identification for multivariable OE-like systems with scarce measurements

The identification problem of multivariable OE-like systems with scarce measurements is considered in this paper. By replacing the unknown inner variables in the information matrix with the outputs of the auxiliary model and by expanding the scalar innovation to an innovation vector, an auxiliary model-based multi-innovation least squares (AM-MILS) algorithm is proposed. In order to deal with the scarce measurement pattern, the algorithm takes the form of interval-varying recursive computation to skip the unavailable measurements including outliers. The introduction of the multi-innovation concept improves the parameter estimation accuracy and makes the identification algorithm more efficient. The convergence analysis shows that for the proposed algorithm, the parameter estimates can converge to their true values in the scarce output measurement pattern. Illustrative examples are given to demonstrate the effectiveness and accuracy of the proposed method.     

Performance analysis of multi-innovation stochastic Newton recursive algorithms

The stochastic Newton recursive algorithm is studied for system identification. The main advantage of this algorithm is that it has extensive form and may embrace more performance with flexible parameters. The primary problem is that the sample covariance matrix may be singular with numbers of model parameters and (or) no general input signal; such a situation hinders the identification process. Thus, the main contribution is adopting multi-innovation to correct the parameter estimation. This simple approach has been proven to solve the problem effectively and improve the identification accuracy. Combined with multi-innovation theory, two improved stochastic Newton recursive algorithms are then proposed for time-invariant and time-varying systems. The expressions of the parameter estimation error bounds have been derived via convergence analysis. The consistence and bounded convergence conclusions of the corresponding algorithms are drawn in detail, and the effect from innovation length and forgetting factor on the convergence property has been explained. The final illustrative examples demonstrate the effectiveness and the convergence properties of the recursive algorithms.     

A novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems

The identification of nonlinear systems is a hot topic in the identification fields. In this paper, a data filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein nonlinear controlled autoregressive moving average systems by adopting the key-term separation principle and the data filtering technique. The proposed algorithm provides a reference to improve the identification accuracy of the nonlinear systems with colored noise. The simulation results show that the new algorithm can more effectively estimate the parameters of the Hammerstein nonlinear systems than the multi-innovation stochastic gradient algorithm.     

Two recursive least squares parameter estimation algorithms for multirate multiple-input systems by using the auxiliary model

This paper considers identification problems of multirate multiple-input output error systems, derives the input-output representations by using the state space models of the multirate systems, and presents two auxiliary model based recursive least squares algorithms for the corresponding output error models with each subsystem having different or same denominator polynomials. The simulation results show the effectiveness of the proposed algorithms.