A novel closed loop identification method and its application of multivariable system

Jin, Qibing; Cheng, Zhiqiang; Dou, Jun; Cao, Liting; Wang, K.W.

doi:10.1016/j.jprocont.2011.09.008

Cited by 29 publications

(12 citation statements)

References 17 publications

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“…Using the above notations a generalized parameter estimation equation can be written as follows. (21) where y [k] (t) denotes the kth order integration of y(t), i.e.,…”

Section: Proposed Methodologymentioning

confidence: 99%

Identification of MIMO Continuous-Time Models Using Simultaneous Step Inputs

Ahmed

Imtiaz

2015

Ind. Eng. Chem. Res.

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A new approach for identification of multi-input multi-output (MIMO) continuous-time transfer function models using simultaneous step input signals is proposed. MIMO processes exhibit directionality which implies that the output gains depend on the input magnitudes as well as the ratios of the inputs. Due to this directionality issue, MIMO models estimated by sequentially changing one input at a time often do not result in satisfactory tracking performance when used for model based control. In the proposed methodology all of the inputs are changed simultaneously to resemble controlled conditions. While the identification tests remain multi-input in its true sense, the parameter estimation steps involve estimation of the parameters of a single transfer function at a time. Moreover, the time delays are estimated in the same way as the coefficients in the model. Simulation results are presented to demonstrate the robustness of the methodology and its applicability to processes with high input−output dimensions. Identification and control results of a simulated distillation column are also presented; a dynamic matrix controller (DMC) results in better control performance with the model estimated using the proposed methodology than with the model estimated using sequential inputs.

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“…Using the above notations a generalized parameter estimation equation can be written as follows. (21) where y [k] (t) denotes the kth order integration of y(t), i.e.,…”

Section: Proposed Methodologymentioning

confidence: 99%

Identification of MIMO Continuous-Time Models Using Simultaneous Step Inputs

Ahmed

Imtiaz

2015

Ind. Eng. Chem. Res.

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“…There is a large number of examples of the usage of PSO for parameter estimation in the literature. One example of PSO's application to process modelling is given in [43]. The plant model developed in this paper has multiple local minima and maxima, therefore the PSO algorithm, being a global optimiser, is effective at finding a global optimum for the problem of fitting the parameters in the model to the plant data.…”

Section: Parameter Estimationmentioning

confidence: 99%

Dynamic modelling of induced draft cooling towers with parallel heat exchangers, pumps and cooling water network

Viljoen

Muller

Craig

2018

Journal of Process Control

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In the process industries, cooling capacity is an important enabler for the facility to manufacture on specification product. The cooling water network is an important part of the overall cooling system of the facility. In this paper a cooling water circuit consisting of 3 cooling towers in parallel, 2 cooling water pumps in parallel, and 11 heat exchangers in parallel, is modelled. The model developed is based on first principles and captures the dynamic, non-linear nature of the plant. The modelled plant is further complicated by continuous, as well as Boolean process variables, giving the model a hybrid nature. Energy consumption is included in the model as it is a very important parameter for plant operation. The model is fitted to real industry data by using a particle swarm optimisation approach. The model is suitable to be used for optimisation and control purposes.

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“…Various techniques using step response data were developed for parameter estimations and include a sequence of step signals (Li et al, 2005a(Li et al, , 2005bPereira et al, 2017). A step response test with particle swarm optimization and search algorithm for the multivariable process was presented by Jin et al (2012). Even though much has been discussed for TITO processes, very few studies have been discussed for FO identification applied for TITO or multivariable processes.…”

Section: Introductionmentioning

confidence: 99%

A single-step identification strategy for the coupled TITO process using fractional calculus

Kothari

Mehta

Prasad

2021

Transactions of the Institute of Measurement and Control

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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 complex and challenging due to cross-coupling. Such a system may require multiple steps and decentralized testing to obtain full system models effectively. In this paper, a direct identification strategy is proposed for the coupled two-input two-output (TITO) system with measurable input–output signals. A well-known closed-loop relay test is utilized to generate a set of inputs–outputs data from a single run. Based on the collected data, four individual fractional-order transfer functions, two for main paths and two for cross-paths, are estimated from single-run test signals. The orthogonal series-based algebraic approach is adopted, namely the Haar wavelet operational matrix, to handle the fractional derivatives of the signal in a simple manner. A single-step strategy yields faster identification with accurate estimation. The simulation and experimental studies depict the efficiency and applicability of the proposed identification technique. The demonstrated 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.

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A novel closed loop identification method and its application of multivariable system

Cited by 29 publications

References 17 publications

Identification of MIMO Continuous-Time Models Using Simultaneous Step Inputs

Identification of MIMO Continuous-Time Models Using Simultaneous Step Inputs

Dynamic modelling of induced draft cooling towers with parallel heat exchangers, pumps and cooling water network

A single-step identification strategy for the coupled TITO process using fractional calculus

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