Parameter estimation of linear MIMO systems using sequential relay feedback test

Panda, Rames C.; Sujatha, V.

doi:10.1002/aic.14362

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…The FOPDT structure of the Wood and Berry model (eq 4) is obtained as effective transfer function models given in eqs 5…”

Section: Mathematical Models and Controlmentioning

confidence: 99%

“…Sujatha and Panda presented a brief idea of interaction of loops in the MIMO process and how to overcome this problem by using the control configuration selection among the loops. They also implemented a sequential relay test for the parameter estimation MIMO process . Although there exist many papers on estimating the plant model in terms of FOPDT, most of the papers neglect the estimation of dead time. , Luyben worked on the identification of FOPDT and SOPDT models by using an autotuned relay method only when the steady-state gain was known a priori.…”

Section: Introductionmentioning

confidence: 99%

“…They also implemented a sequential relay test for the parameter estimation MIMO process . Although there exist many papers on estimating the plant model in terms of FOPDT, most of the papers neglect the estimation of dead time. , Luyben worked on the identification of FOPDT and SOPDT models by using an autotuned relay method only when the steady-state gain was known a priori.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Energy Consumption of the Distillation Process and Its Product Purity Analysis Using Ultraviolet Spectroscopy

2020

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This paper addresses the energy consumption of distillation process via an actuator, which is a challenging problem in process industries. Precise control action would enhance energy consumption and improve the productivity. This paper is an experimental validation of EPC-PI control algorithm and analysis of distillate purity of a lab-scale distillation column. The PI control scheme uses closed-loop data of extended predictive controller (EPC) that has been performed through off-line simulation. The performance of control method is compared with different schemes such as Hägglund’s one-third rule and Skogestad’s overshoot method. The issue of integral windup in the multivariable process is addressed in the aspect of optimal energy consumption. The energy consumption calculations are made with respect to power utility of actuators throughout the process. The distillate product of post-controller implementation is processed to qualitative analysis using UV spectroscopy. Performance index is carried out via integral time absolute error (ITAE) by perturbing plant parameters up to 30% uncertainty.

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“…The FOPDT structure of the Wood and Berry model (eq 4) is obtained as effective transfer function models given in eqs 5…”

Section: Mathematical Models and Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Energy Consumption of the Distillation Process and Its Product Purity Analysis Using Ultraviolet Spectroscopy

2020

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“…For decades, much research has been performed on the multivariable systems [11,12], and some typical approaches for the parameter estimation of the multivariable systems have been reported [13], such as the canonical approach [14], the iterative methods [15,16], and the least squares methods [17]. Recently, Panda and Vijayaraghavan adopted the sequential relay feedback test to estimate the parameter of the linear multivariable systems [18]. Jafari et al presented an iterative least squares algorithm to identify the multivariable nonlinear systems with colored noises [19].…”

Section: Introductionmentioning

confidence: 99%

The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Ding

2017

Complexity

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This paper considers the identification problem of multi-input-output-error autoregressive systems. A hierarchical gradient based iterative (H-GI) algorithm and a hierarchical least squares based iterative (H-LSI) algorithm are presented by using the hierarchical identification principle. A gradient based iterative (GI) algorithm and a least squares based iterative (LSI) algorithm are presented for comparison. The simulation results indicate that the H-LSI algorithm can obtain more accurate parameter estimates than the LSI algorithm, and the H-GI algorithm converges faster than the GI algorithm.

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“…Parameter estimation has become a hot issue [7, 8], especially in system control [9], signal processing [10] and system identification [11, 12]. For multivariable systems, Mobayen [13] provided a robust tracking controller for multivariable delayed systems with input saturation via composite non‐linear feedback; Panda and Vijayaraghavan [14] solved the parameter estimation problems of linear multivariable systems using the sequential relay feedback test; Ding [15] presented the coupled least squares identification algorithm for multivariable systems.…”

Section: Introductionmentioning

confidence: 99%

Filtering‐based iterative identification for multivariable systems

Wang

Ding

2016

IET Control Theory & Applications

106

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This study applies the filtering technique to system identification to study the data filtering-based parameter estimation methods for multivariable systems, which are corrupted by correlated noise -an autoregressive moving average process. To solve the difficulty that the identification model contains the unmeasurable variables and noise terms in the information matrix, the authors present a hierarchical gradient-based iterative (HGI) algorithm by using the hierarchical identification principle. To improve the convergence rate, they apply the filtering technique to derive a filtering-based HGI algorithm and a filtering-based hierarchical least squares-based iterative (HLSI) algorithm. The simulation examples indicate that the filtering-based HLSI algorithm has the highest computational efficiency among these three algorithms. 1 Introduction System modelling is the foundation of control [1, 2], and multivariable systems exist widely in process industries [3], such as the distillation column [4], the internal combustion engine [5] and the industrial wastewater treatment [6]. Parameter estimation has become a hot issue [7, 8], especially in system control [9], signal processing [10] and system identification [11, 12]. For multivariable systems, Mobayen [13] provided a robust tracking controller for multivariable delayed systems with input saturation via composite non-linear feedback; Panda and Vijayaraghavan [14] solved the parameter estimation problems of linear multivariable systems using the sequential relay feedback test; Ding [15] presented the coupled least squares identification algorithm for multivariable systems.The main feature of multivariable systems is that the input and output variables of the systems are relevant and coupled, and the difficulty of parameter estimation for such systems is that the system model both contains parameter vectors and parameter matrices, and the unmeasurable variables as well. The hierarchical identification is based on decomposition and can be applied to obtain the parameter estimates of the complex multivariable systems [16]. In this literature, Schranz et al. [17] studied the hierarchical parameter identification problems in models of respiratory mechanics; Zhang et al. [18] used the auxiliary model identification idea and the hierarchical gradient-based iterative (HGI) method for solving the parameter estimation problem of the multivariable output-error moving average (MA) systems; Han et al. [19] presented a hierarchical least squares-based iterative algorithm for multivariable systems with additive MA noises by using the decomposition technique.The iterative algorithms update the parameter estimates using the bath data and can be applied to scalar systems and multivariable systems [20], linear systems and non-linear systems [21,22]. The filtering technique can extract the useful information from noisy measurement data for parameter estimation [23,24] and has been used in signal processing and communication [25][26][27], and neural network [28,29]. The filtering techn...

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Parameter estimation of linear MIMO systems using sequential relay feedback test

Cited by 6 publications

References 19 publications

Optimal Energy Consumption of the Distillation Process and Its Product Purity Analysis Using Ultraviolet Spectroscopy

Optimal Energy Consumption of the Distillation Process and Its Product Purity Analysis Using Ultraviolet Spectroscopy

The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Filtering‐based iterative identification for multivariable systems

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