Optimal modified performance of MIMO networked control systems with multi-parameter constraints

Zhan, Xisheng; Cheng, Lingli; Wu, Jie; Yang, Qingsheng; Han, Tao

doi:10.1016/j.isatra.2018.09.018

Cited by 62 publications

(25 citation statements)

References 32 publications

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“…• The methods proposed in this paper can combine some statistical optimal strategies [47][48][49] to study the parameter estimation algorithms of linear and nonlinear systems [50][51][52][53][54][55][56] and can be extended to other fields. [57][58][59][60][61][62][63][64][65][66][67][68][69]…”

Section: Resultsmentioning

confidence: 99%

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

Ding

2019

Adaptive Control & Signal

180

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For a special class of nonlinear systems (ie, bilinear systems) with autoregressive moving average noise, this paper gives the input-output representation of the bilinear systems through eliminating the state variables in the model. Based on the obtained model and the maximum likelihood principle, a filtering-based maximum likelihood hierarchical gradient iterative algorithm and a filtering-based maximum likelihood hierarchical least squares iterative algorithm are developed for identifying the parameters of bilinear systems with colored noises. The original bilinear systems are divided into three subsystems by using the data filtering technique and the hierarchical identification principle, and they are identified respectively. Compared with the gradient-based iterative algorithm and the multi-innovation stochastic gradient algorithm, the proposed algorithms have higher computational efficiency and parameter estimation accuracy. The simulation results indicate that the proposed algorithms are effective for identifying bilinear systems.

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Section: Resultsmentioning

confidence: 99%

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

Ding

2019

Adaptive Control & Signal

180

View full text Add to dashboard Cite

show abstract

“…The methods proposed in this paper can combine other mathematical tools [52][53][54] to study the parameter identification problems of different systems with colored noise [55][56][57] and can be applied to other fields such as information processing and communication. [58][59][60][61][62][63][64][65][66][67]…”

Section: Discussionmentioning

confidence: 99%

“…principle is an effective method to improve the accuracy of the identification algorithms. [58][59][60][61][62][63][64][65][66][67] [58][59][60][61][62][63][64][65][66][67] …”

Section: M(z)y(l)mentioning

confidence: 99%

Maximum likelihood gradient identification for multivariate equation‐error moving average systems using the multi‐innovation theory

Liu

Ding

Hayat

2019

Adaptive Control & Signal

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For the multivariate equation-error moving average system, a multivariate maximum likelihood multi-innovation extended stochastic gradient (M-ML-MIESG) algorithm is delivered. The key is to decompose the system into several regressive identification subsystems according to the number of the system outputs. Then, a multivariate maximum likelihood extended stochastic gradient algorithm is presented to estimate the parameters of these subsystems. The M-ML-MIESG algorithm has higher parameter estimation accuracy than the multivariate extended stochastic gradient algorithm. The simulation examples indicate that the proposed methods work well.

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“…Furthermore, the proposed approaches in the paper can combine other mathematical tools [50][51][52] and statistical strategies [53][54][55][56][57][58][59][60] to study the parameter estimation problems of linear and nonlinear systems with different disturbance noises [61][62][63][64], and can be applied to other literature [65][66][67][68] such as signal processing [69][70][71][72][73][74] and neural networks [75][76][77].…”

Section: Remarkmentioning

confidence: 99%

Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data

et al. 2019

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It is well-known that mathematical models are the basis for system analysis and controller design. This paper considers the parameter identification problems of stochastic systems by the controlled autoregressive model. A gradient-based iterative algorithm is derived from observation data by using the gradient search. By using the multi-innovation identification theory, we propose a multi-innovation gradient-based iterative algorithm to improve the performance of the algorithm. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed algorithms.

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Optimal modified performance of MIMO networked control systems with multi-parameter constraints

Cited by 62 publications

References 32 publications

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

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

Maximum likelihood gradient identification for multivariate equation‐error moving average systems using the multi‐innovation theory

Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data

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