Identification of Centralised Controlled Multivariable Systems
TL;DR: In this paper, the model parameters of a centralised (PI) controlled multivariable system are identified by a closed-loop optimisation method using a non-linear least square minimisation method.
Abstract: FOPTD model parameters of a centralised (PI) controlled multivariable system are identified by a closed-loop optimisation method. The model parameters are obtained by a non-linear least square minimisation method. An easy procedure is given for the guess values of the parameters. For arbitrary guess values, the minimisation method does not converge. The simulation studies are given for several transfer function matrices.
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TL;DR: In this article, an identification technique for two-input-two-output (2o2o) MIMO processes is proposed. But the identification of multi-input multi-multi-output processes is always challenging due to significant loop interactions.
Abstract: The identification of multi-input-multi-output process is always challenging due to significant loop interactions. This article proposes an identification technique for two-input-two-output process...
3 citations
26 Mar 2021
TL;DR: In this paper, a method is proposed to identify the First Order Plus Time Delay (FOPTD) transfer function parameters of multivariable system by closed loop optimization method Davison's method (1976) to design the centralized controller settings are modified to obtain the Steady State Gain Matrix (SSGM) from which the initial guess values of process gain (Kp) for each FOPTD model in the multi-ivariable transfer function is obtained.
Abstract: A method is proposed to identify the First Order Plus Time Delay (FOPTD) transfer function parameters of multivariable system by closed loop optimization method Davison’s method (1976) to design the centralized controller settings are modified to obtain the Steady State Gain Matrix (SSGM) of multivariable system, from which the initial guess values of process gain (Kp) for each FOPTD model in the multivariable transfer function is obtained The initial guess values of time constant (τp) and time delay (θ) are obtained by the method suggested by Rajapandiyan and Chidambaram (2012) A standard least-square optimization method is formulated to minimize the sum of square of the errors between the model and the process main and interaction closed loop step responses Three simulation examples are demonstrated (two 2x2 FOPTD and a higher order model systems) to evaluate the efficiency of the present method Effect of measurement noise and different controller settings are also studied The proposed method is very simple compared to the method reported by Dhanya and Chidambaram (2016) to calculate the initial guess values of process gains (Kp) of the transfer function matrix
TL;DR: Results on the twin rotor multiple-input multiple-output (MIMO) system (TRMS) clearly reveal that the presented idea works well with the highly coupled system even in the presence of measurement noise.
Abstract: The reliable performance of a complete control system depends on accurate model information being used to represent each subsystem. The identification and modelling of multivariable systems are com...
Cites methods from "Identification of Centralised Contr..."
...…method (Broman et al., 1999), curve fitting and genetic algorithm-based method (Viswanathan et al., 2001), a closed-loop reaction curve method (Ram and Chidambaram, 2014) and nonlinear least square minimization method (Ram and Chidambaram, 2016) have been well explained in the literature....
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References
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Book•
01 Jan 1996
TL;DR: This book presents a rigorous, yet easily readable, introduction to the analysis and design of robust multivariable control systems and provides the reader with insights into the opportunities and limitations of feedback control.
Abstract: From the Publisher:
This is a book on practical feedback control and not on system theory in general. Feedback is used in control systems to change the dynamics of the system and to reduce the sensitivity of the system to both signal and model uncertainty. The book presents a rigorous, yet easily readable, introduction to the analysis and design of robust multivariable control systems. It provides the reader with insights into the opportunities and limitations of feedback control. Its objective is to enable the engineer to design real control systems. Important topics are: extensions and classical frequency-domain methods to multivariable systems, analysis of directions using the singular value decomposition, performance limitations and input-output controllability analysis, model uncertainty and robustness including the structured singular value, control structure design, and methods for controller synthesis and model reduction. Numerous worked examples, exercises and case studies, which make frequent use of MATLAB, are included. MATLAB files for examples and figures, solutions to selected exercises, extra problems and linear state-space models for the case studies are available on the Internet.
6,279 citations
"Identification of Centralised Contr..." refers background in this paper
...Introduction Many industrial systems are multi-input multi-output processes [1,2]....
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TL;DR: In this article, a pilot scale binary distillation column operated under the control of an IBM 1800 digital computer has been studied for disturbances in feed flow rate, and two alternate control systems, namely a noninteracting control system and a ratio control system were evaluated.
644 citations
Additional excerpts
...Example 2 Consider the system studied by Wood and Berry [15] and by Rajapandian and Chidambaram [5,7]: GðsÞ ¼ 12:8e s 16:7sþ1 18:9e 3s 21sþ1 6:6e 7s 10:9sþ1 19:4e 3s 14:4sþ1 " # ð16Þ The relative gain λ11 is calculated as 2.0094 and hence λ12 = −1.0094; λ21 = λ12; λ22 = λ11....
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...Example 2 Consider the system studied by Wood and Berry [15] and by Rajapandian and Chidambaram [5,7]: GðsÞ 1⁄4 12:8e s 16:7sþ1 18:9e 3s 21sþ1 6:6e 7s 10:9sþ1 19:4e 3s 14:4sþ1 " # ð16Þ The relative gain λ11 is calculated as 2....
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TL;DR: In this paper, the authors proposed an approach to determine an input-output pairing configuration and controller settings guaranteeing a practically satisfactory disturbance attenuation for linear, interacting multivariable control systems.
340 citations
TL;DR: It is shown that, when the controller is a smooth function of the input-output dynamics and the disturbance spectrum, the best controller performance is achieved by performing the identification in closed loop with an operating controller that is the ideal controller.
261 citations
TL;DR: In this article, a simple tuning method for multiloop PID controllers is presented, which is suited for PID algorithms with no proportional and derivative kick, and is derived from a controller synthesis method with a control performance specification of 5% overshoot on servo response.
Abstract: A simple tuning method for multiloop PID controllers will be presented in this article. The method is suited for PID algorithms with no proportional and derivative kick. This tuning method is derived from a controller synthesis method with a control performance specification of 5% overshoot on servo response. Depending on the interaction natures of the multiloop systems, tuning based on the diagonal elements of the model or further detuning may be necessary. For systems with a relative gain array [RGA(λii)] < 1, a detuning factor based on this information is proposed. The information needed for controller tuning purposes is the dynamic model parameters of the diagonal elements and the process gain information of the off-diagonal elements. The tuning method procedure is very simple and straightforward, utilizing only nth identification tests with n as the number of the interacting control loops. The tuning method can easily be applied to various industrial situations with almost no need for a priori proces...
117 citations
"Identification of Centralised Contr..." refers background in this paper
...Example 1 Consider the transfer function matrix [13,5,7]:...
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