Journal ArticleDOI
Recursive and Iterative Least Squares Parameter Estimation Algorithms for Multiple-Input–Output-Error Systems with Autoregressive Noise
TLDR
Simulation results indicate that the least squares-based iterative algorithm can generate more accurate parameter estimates than the auxiliary model-based recursive generalized least squares algorithm.Abstract:
This paper considers the parameter estimation of a multiple-input–output-error system with autoregressive noise. In order to solve the problem of the information vector containing unknown inner variables, an auxiliary model-based recursive generalized least squares algorithm and a least squares-based iterative algorithm are proposed according to the auxiliary model identification idea and the iterative search principle. The simulation results indicate that the least squares-based iterative algorithm can generate more accurate parameter estimates than the auxiliary model-based recursive generalized least squares algorithm. Two examples are given to test the proposed algorithms.read more
Citations
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Journal ArticleDOI
Hierarchical Parameter Estimation for the Frequency Response Based on the Dynamical Window Data
TL;DR: In this paper, a hierarchical multi-innovation stochastic gradient estimation method is derived through parameter decomposition, and the forgetting factor and the convergence factor are introduced to improve the performance of the algorithm.
Journal ArticleDOI
A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation
TL;DR: This paper considers the parameter identification for Hammerstein controlled autoregressive systems by using the key term separation technique to express the system output as a linear combination of the system parameters, and then a hierarchical least squares algorithm is developed for estimating all parameters involving in the subsystems.
Journal ArticleDOI
The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle
TL;DR: A filtering‐based maximum likelihood hierarchical gradient iterative algorithm and a filtering‐ basedmaximum likelihood hierarchical least squares iterative algorithms are developed for identifying the parameters of bilinear systems with colored noises.
Journal ArticleDOI
State filtering-based least squares parameter estimation for bilinear systems using the hierarchical identification principle
TL;DR: This study presents a combined parameter and state estimation algorithm for a bilinear system described by its observer canonical state-space model based on the hierarchical identification principle to reduce the computation burden and improve the parameter tracking capability.
Journal ArticleDOI
Recursive coupled projection algorithms for multivariable output-error-like systems with coloured noises
TL;DR: This study develops a partially coupled generalised extended projection algorithm and a partially coupling generalisation extended stochastic gradient algorithm to estimate the parameters of a multivariable output-error-like system with autoregressive moving average noise from input–output data.
References
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Journal ArticleDOI
Robust adaptive finite-time parameter estimation and control for robotic systems
TL;DR: In this article, the adaptive parameter estimation and control for nonlinear robotic systems based on parameter estimation errors is studied, where three adaptive laws driven by the estimation error are presented, where exponential error convergence is proved under the conventional persistent excitation (PE) condition; the direct measurement of the time derivatives of the system states are avoided.
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A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems
TL;DR: A filtering based extended stochastic gradient algorithm and a filtering based multi-innovation ESG algorithm for improving the parameter estimation accuracy for a multivariable system with moving average noise.
Journal ArticleDOI
The damping iterative parameter identification method for dynamical systems based on the sine signal measurement
TL;DR: A damping parameter estimation algorithm for dynamical systems based on the sine frequency response is proposed and a damping factor is introduced in the proposed iterative algorithm in order to overcome the singular or ill-conditioned matrix during the iterative process.
Journal ArticleDOI
Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration
Ling Xu,Lei Chen,Weili Xiong +2 more
TL;DR: In this article, a new Newton iterative identification method is presented for estimating the parameters of a second-order dynamic system utilizing the obtained data from the step response, in order to obtain the desired dynamic performance, a controller design method based on the root locus is presented to meet the requirement of the dynamic performance of the overshoot.
Journal ArticleDOI
Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model
Yanjiao Wang,Feng Ding +1 more
TL;DR: For a MIMO system whose outputs are contaminated by an ARMA noise process, an auxiliary model based recursive least squares parameter estimation algorithm is presented through filtering input-output data, which has higher estimation accuracy than the existing multivariable identification algorithm.