Ling Xu

Bio: Ling Xu is an academic researcher from Jiangnan University. The author has contributed to research in topics: Estimation theory & Iterative method. The author has an hindex of 29, co-authored 54 publications receiving 2776 citations. Previous affiliations of Ling Xu include Qingdao University of Science and Technology & Chinese Ministry of Education.

Hierarchical Parameter Estimation for the Frequency Response Based on the Dynamical Window Data

Abstract: This paper studies the problem of parameter estimation for frequency response signals For a linear system, the frequency response is a sine signal with the same frequency as the input sine signal When a multi-frequency sine signal is applied to a system, the system response also is a multi-frequency sine signal The signal modeling for multi-frequency sine signals is very difficult due to the highly nonlinear relations between the characteristic parameters and the model output In order to obtain the parameter estimates of the multi-frequency sine signal, the signal modeling methods based on statistical identification are proposed by means of the dynamical window discrete measured data By constructing a criterion function with respect to the model parameters to be estimated, a hierarchical multi-innovation stochastic gradient estimation method is derived through parameter decomposition Moreover, the forgetting factor and the convergence factor are introduced to improve the performance of the algorithm The simulation results show the effectiveness of the proposed methods

Decomposition based least squares iterative identification algorithm for multivariate pseudo-linear ARMA systems using the data filtering

Abstract: This paper develops a decomposition based least squares iterative identification algorithm for multivariate pseudo-linear autoregressive moving average systems using the data filtering. The key is to apply the data filtering technique to transform the original system to a hierarchical identification model, and to decompose this model into three subsystems and to identify each subsystem, respectively. Compared with the least squares based iterative algorithm, the proposed algorithm requires less computational efforts. The simulation results show that the proposed algorithms can work well.

Partially-coupled least squares based iterative parameter estimation for multi-variable output-error-like autoregressive moving average systems

Abstract: This study considers the parameter estimation of a multi-variable output-error-like system with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares-based iterative algorithm is proposed by using the iterative search. The original system is divided into several subsystems by using the decomposition technique. However, the subsystems contain the same parameter vector, which poses a challenge for the identification problem, the approach taken here is to use the coupling identification concept to cut down the redundant parameter estimates. In addition, the recursive least squares algorithm is provided for comparison. The simulation results indicate that the proposed algorithms are effective.

A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems

Abstract: For a multivariable system with moving average noise (i.e., a multivariable controlled autoregressive moving average system), this paper proposes a filtering based extended stochastic gradient (ESG) algorithm and a filtering based multi-innovation ESG algorithm for improving the parameter estimation accuracy. The key is using the filtering technique and the multi-innovation identification theory. The proposed algorithms can identify the parameters of the system model and the noise model. The filtering based multi-innovation ESG algorithm can give more accurate parameter estimates. The numerical simulation results demonstrate that the proposed algorithms work well.

Control Algorithms of Magnetic Suspension Systems Based on the Improved Double Exponential Reaching Law of Sliding Mode Control

Abstract: This paper proposes an improved double power reaching law integral SMC algorithm to overcome the chattering, large overshoot, slow response. This improved algorithm has two advantages. Firstly, the designed control law can reach the approaching equilibrium point quickly when it is away from or close to the sliding surface. The chattering and response speed problems can be resolved. Secondly, the proposed algorithm has a good anti-jamming performance, and can maintain a good dynamic quality under the condition of the uncertain external disturbance. Finally, the proposed algorithm is applied to the open-loop unstable magnetic suspension system. Theoretical analysis and Matlab simulation results show that the improved algorithm has better control performances than the traditional SMC and the power reaching law integral SMC algorithm, such as less chattering, smaller overshoots, and faster response speed.

State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors

Abstract: This paper considers the state estimation problem of bilinear systems in the presence of disturbances. The standard Kalman filter is recognized as the best state estimator for linear systems, but it is not applicable for bilinear systems. It is well known that the extended Kalman filter (EKF) is proposed based on the Taylor expansion to linearize the nonlinear model. In this paper, we show that the EKF method is not suitable for bilinear systems because the linearization method for bilinear systems cannot describe the behavior of the considered system. Therefore, this paper proposes a state filtering method for the single-input–single-output bilinear systems by minimizing the covariance matrix of the state estimation errors. Moreover, the state estimation algorithm is extended to multiple-input–multiple-output bilinear systems. The performance analysis indicates that the state estimates can track the true states. Finally, the numerical examples illustrate the specific performance of the proposed method.

Hierarchical Parameter Estimation for the Frequency Response Based on the Dynamical Window Data

Abstract: This paper studies the problem of parameter estimation for frequency response signals For a linear system, the frequency response is a sine signal with the same frequency as the input sine signal When a multi-frequency sine signal is applied to a system, the system response also is a multi-frequency sine signal The signal modeling for multi-frequency sine signals is very difficult due to the highly nonlinear relations between the characteristic parameters and the model output In order to obtain the parameter estimates of the multi-frequency sine signal, the signal modeling methods based on statistical identification are proposed by means of the dynamical window discrete measured data By constructing a criterion function with respect to the model parameters to be estimated, a hierarchical multi-innovation stochastic gradient estimation method is derived through parameter decomposition Moreover, the forgetting factor and the convergence factor are introduced to improve the performance of the algorithm The simulation results show the effectiveness of the proposed methods