Observer-Based Dynamic Event-Triggered Semiglobal Bipartite Consensus of Linear Multi-Agent Systems With Input Saturation

SummaryMultiple‐input multiple‐output (MIMO) models are widely used in practical engineering. This article derives a new identification model of the MIMO system by decomposing the MIMO system into several multiple‐input single‐output subsystems. By means of the auxiliary model identification idea, an auxiliary model‐based recursive least squares (AM‐RLS) algorithm is derived for identifying the MIMO systems. In order to reduce the computational burden for identifying MIMO systems, this article presents a hierarchical identification model for the MIMO systems. By applying the hierarchical identification principle, an auxiliary model‐based hierarchical least squares (AM‐HLS) algorithm is proposed for improving the computational efficiency. The computational efficiency analysis indicates that the AM‐HLS algorithm is effective in reducing the calculation amount compared with the AM‐RLS algorithm. Moreover, this article analyzes the convergence of the AM‐HLS algorithm. The simulation example shows that the AM‐RLS and AM‐HLS algorithms studied in this article are effective.

Section: Discussionmentioning

confidence: 99%

Hierarchical recursive least squares parameter estimation methods for multiple‐input multiple‐output systems by using the auxiliary models

Xing,

Ding,

Pan

et al. 2023

“…The proposed parameter estimation algorithms in this paper are based on the identification model in (13). Some identification methods are derived based on the identification models of the systems [54][55][56][57][58][59] and these methods can be used to estimate the parameters of other linear systems and nonlinear systems [60][61][62][63][64][65] and can be applied to other fields [66][67][68][69][70][71] such as chemical process control systems.…”

Section: The Model Descriptionmentioning

confidence: 99%

Joint two‐stage multi‐innovation recursive least squares parameter and fractional‐order estimation algorithm for the fractional‐order input nonlinear output‐error autoregressive model

2023

Summary This paper mainly investigates the issue of parameter identification for the fractional‐order input nonlinear output error autoregressive (IN‐OEAR) model. In order to avoid the problem of large computation of redundant parameter estimation, the output form of the system can be expressed by a linear combination of unknown parameters through the key term separation. Through employing the hierarchial identification principle, the fractional‐order IN‐OEAR model is decomposed into two sub‐models with a smaller number of parameters. On the basis of the recursive identification methods, a recursive least squares sub‐algorithm and a gradient stochastic sub‐algorithm are proposed to estimate the parameters and the fractional‐order, respectively. With the aim of achieving more accurate parameter estimates, a two‐stage multi‐innovation least recursive algorithm is proposed by means of the multi‐innovation identification theory. The numerical simulation results test the effectiveness of the proposed methods.

“…The proposed parameter estimation algorithms in this paper are based on the identification model in (4). Many identification methods are derived based on the identification models of the systems [41][42][43][44][45][46][47] and these methods can be used to estimate the parameters of other linear systems and nonlinear systems [48][49][50][51][52][53] and can be applied to other fields [54][55][56][57][58][59] such as chemical process control systems.…”

Section: System Description and Identification Modelmentioning

confidence: 99%

Filtered multi‐innovation‐based iterative identification methods for multivariate equation‐error ARMA systems

Sun

Ding

et al. 2023

This paper focuses on the parameter estimation issues of multivariate equation-error autoregressive moving average systems. By applying the gradient search and the multi-innovation theory, we derive a multi-innovation gradient based iterative (MI-GI) algorithm. In order to improve the computational efficiency and the parameter estimation accuracy, a filtering and decomposition based gradient iterative (F-D-GI) algorithm is presented by using the data filtering technique and the decomposition technique. The key is to choose an appropriate filter to filter the input-output data and to transform an original system into several subsystems. Compared with the MI-GI algorithm, the F-D-GI algorithm can generate more accurate parameter estimates. Finally, an illustrative example is provided to indicate the effectiveness of the proposed algorithms.