Multivariable system identification and generic model control of a laboratory scale packed distillation column

Karacan, Süleyman; Hapoğlu, Hale; Alpbaz, Mustafa

doi:10.1016/j.applthermaleng.2006.07.032

Cited by 15 publications

(9 citation statements)

References 18 publications

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“…This is the identification model for the multivariable system in (1). The goal is to obtain the estimates of the parameter vector α and parameter matrix θ from the input-output information {y(t), ψ(t), ϕ(t)}.…”

Section: The Model Descriptionmentioning

confidence: 99%

“…The identification of multivariable systems or multi-input multi-output (MIMO) systems is a very important topic and can find many practical applications in electrical, mechanical and chemical engineering [1][2][3][4][5], since a large number of industrial systems in these areas are of the MI-MO type. The system identification is the first step of a model-based control problem, for example, the adaptive control [6], the model predictive control [7] and the network control [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Partially coupled estimation algorithm for discrete-time multiple-input multiple-output systems

Liu

Ding

2012

2012 24th Chinese Control and Decision Conference (CCDC)

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In this paper, a partially coupled recursive least squares (PC-RLS) algorithm is proposed to estimate the parameters of a class of multivariable systems. The first step is to decompose the identification model into submodels or subsystems, resulting in the subsystem least squares (S-RLS) algorithm. For the same parameters contained in each submodel, the PC-RLS algorithm is to couple the estimation of the common parameters of each submodel one by one, while the estimates of the common parameters are mutually independent for the S-RLS algorithm. The analysis shows that the PC-RLS algorithm requires less computational burden than the standard recursive least squares method and the S-RLS method. The simulation tests verify the effectiveness of the proposed algorithm.

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Section: The Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Partially coupled estimation algorithm for discrete-time multiple-input multiple-output systems

Liu

Ding

2012

2012 24th Chinese Control and Decision Conference (CCDC)

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“…In Wang et al [3], a novel control strategy based on GMC was proposed for internal thermally coupled distillation column. Karacan et al [4] has proposed a multivariable GMC structure to control of the packed distillation column. Compared with the traditional linear control method, GMC can track the target stably and effectively eliminate the impact of disturbance [5].…”

Section: Introductionmentioning

confidence: 99%

Research on generic model control method for manipulator based on neural networks

Zhang¹,

Chu²

2012

2012 IEEE International Conference on Mechatronics and Automation

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The trajectory tracking control problem based on generic model control method for manipulator is addressed in this paper. Aiming at the problem that the relative degree of the input and output of manipulator system is not equal to 1, the sliding mode switching function is established according to the trajectory tracking error, and a method using sliding mode switching function as system virtual output is presented for generic model control law design, so that the trajectory tracking error moves to the sliding mode switching surface and eventually tends to zero. Since it is difficult to establish the precise dynamics model for the manipulator, the adaptive identification method is put forward based on RBF neural networks, which introduces sliding mode switching item to compensate for the approximation error of neural networks and ensure the system stability. For the sliding mode switching item is easy to cause control system chattering, the sliding mode switching gain adjustment method based on the exponential function is proposed. Finally, the effectiveness of the proposed methods is verified by simulation results.

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“…Lundberg and Bezanson [22] proposed an enhanced robust GMC by using derivative feedback to take care of process/ model mismatch. Suleyman et al [23] presented the multivariable system identification and generic model control of a laboratory-scale packed distillation column.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Generic Model Control of High‐Purity Internal Thermally Coupled Distillation Column

2010

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The internal thermally coupled distillation column (ITCDIC) is a frontier in energy-saving distillation research. The process inside a high-purity ITCDIC is of great nonlinear dynamics, which trouble the conventional control schemes. A multivariable adaptive robust generic model control (ARGMC) is presented to solve the difficulties, where an ARX model is derived and a recursive least squares estimation (RLSE) method is introduced. The benzene/toluene system is studied as an illustrative example. The results of ARGMC are compared with the robust generic model control (RGMC) and traditional PID control in detail. The performances in both servo control and regulatory control confirm the accuracy and validity of ARGMC for the high-purity ITCDIC process.

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Multivariable system identification and generic model control of a laboratory scale packed distillation column

Cited by 15 publications

References 18 publications

Partially coupled estimation algorithm for discrete-time multiple-input multiple-output systems

Partially coupled estimation algorithm for discrete-time multiple-input multiple-output systems

Research on generic model control method for manipulator based on neural networks

Adaptive Robust Generic Model Control of High‐Purity Internal Thermally Coupled Distillation Column

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