Control-relevant input excitation for system identification of ill-conditioned  n × n  systems with  n &gt; 2

Ghosh, Ram Krishna; Häggblom, Kurt E.; Böling, Jari M.

doi:10.3182/20140824-6-za-1003.02447

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…In this study, as well as in some previous ones for systems of other sizes [9,11], experiment designs that explicitly excited the various gain directions of the system outperformed more standard designs. A directional PRBS design, where each gain direction was excited sequentially, outperformed all other designs in terms of both MSE and MSI.…”

Section: As Shown By Table VIII the Directional Prbs Designsupporting

confidence: 63%

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Evaluation of experiment designs for MIMO system identification by model predictive control

Häggblom

2015

2015 IEEE Conference on Control Applications (CCA)

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Section: As Shown By Table VIII the Directional Prbs Designsupporting

confidence: 63%

“…Control-oriented identification of 22  systems has been studied quite extensively [10], but detailed studies of larger systems are hard to find, an exception being a 44  system studied in [11].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of experiment designs for MIMO system identification by model predictive control

Häggblom

2015

2015 IEEE Conference on Control Applications (CCA)

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“…In this work, the RGA approach is adopted. For a typical system represented by (2). Each of the outputs Y 1 and Y 2 are affected by both inputs U 1 and U 2 .…”

Section: Manipulated-variable Controlled-variable (Mv-cv) Pairingmentioning

confidence: 99%

“…The challenges associated with identifying and controlling ill-conditioned systems mean that unusual steps are necessary for the development of plant excitation signals, the identification process and controller design [1][2][3]. Typical systems require that the excitation signals used for identification be uncorrelated for the proper excitation of the plant to obtain informative data for identification.…”

Section: Introductionmentioning

confidence: 99%

Distributed Control of an Ill-Conditioned Non-Linear Process Using Control Relevant Excitation Signals

Sha’aban

2023

Processes

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Efficient control schemes for ill-conditioned systems, such as the high-purity distillation column, can be challenging and costly to design and implement. In this paper, we propose a distributed control scheme that utilizes well-designed excitation signals to identify the system. Unlike traditional systems, we found that a summation of correlated and uncorrelated signals can yield better excitation of the plant. Our proposed distributed model predictive control (MPC) scheme uses a shifted input sequence to address loop interactions and reduce the computational load. This approach deviates from traditional schemes that use iteration, which can increase complexity and computational load. We initially tested the proposed method on the linear model of a highly coupled 2 × 2 process and compared its performance with decentralized proportional-integral-derivative (PID) controllers and centralized MPC. Our results show improved performance over PID controllers and similar results to centralized MPC. Furthermore, we compared the performance of the proposed approach with a centralized MPC on a nonlinear model of a distillation column. The results for the second study also demonstrated comparable performance between the two controllers with the decentralised control slightly outperforming the centralised MPC in some cases. These findings are promising and may be of interest to practitioners that are more comfortable with tuning decentralised loops.

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“…The subspace identification‐based N4SID algorithm used a combination of deterministic–stochastic systems [9]. The different excitations are evaluated for ill‐conditioned systems based on the crest factor, normalized root mean square error and performance index for perturbation signal and obtained the two inputs: (i) step transforms in gain guidelines and (ii) sequential PRBS disturbance of all the inputs, which gave the best normalized root mean square error of cross‐validations [10]. An optimal input spectrum was designed for continuous‐time system identification using Laguerre functions [11].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Validation of a Four‐Tank System for Level Control Process Using Black Box and White Box Model Approaches

Subramanian¹,

C²,

Sharmila³

2020

IEEJ Transactions Elec Engng

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Mathematical modeling of a system plays a vital role in controller design, tuning to meet the desired specifications and process parameter optimization. In this paper, different identification algorithms are implemented in the four-tank system to demonstrate the superiority of the recommended subspace system identification method (Numeric Algorithms for Subspace State Space System Identification [N4SID]) for the different input design. A system identification method was utilized to identify the model of a dynamic system. It is modeled using the First-Principle Method (FPM), Prediction Error Method (PEM) and Numeric Algorithms for N4SID method. The identification is performed through MATLAB. The plant input and output is acquired through LabVIEW. The developed models are validated with a new dataset based on the model performance specifications: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Best Fit Rate (BFR). The results show that the proposed N4SID method with the amplitude-modulated pseudorandom binary signal (APRBS) signal is better than the other methods with different inputs.

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Control-relevant input excitation for system identification of ill-conditioned n × n systems with n > 2

Cited by 4 publications

References 13 publications

Evaluation of experiment designs for MIMO system identification by model predictive control

Evaluation of experiment designs for MIMO system identification by model predictive control

Distributed Control of an Ill-Conditioned Non-Linear Process Using Control Relevant Excitation Signals

Modeling and Validation of a Four‐Tank System for Level Control Process Using Black Box and White Box Model Approaches

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