Model predictive control of resonant systems using Kautz model

被引：2

作者：

Misra S. ^{[1
]}

Reddy R. ^{[1
]}

Saha P. ^{[1
]}

机构：

[1] Department of Chemical Engineering, Indian Institute of Technology Guwahati, Assam

来源：

International Journal of Automation and Computing | 2016年 / 13卷 / 5期

关键词：

internal model control; Kautz model; Model predictive control; orthonormal basis function; resonant systems;

D O I：

10.1007/s11633-016-0954-x

中图分类号：

学科分类号：

摘要：

The scope of this paper broadly spans in two areas: system identification of resonant system and design of an efficient control scheme suitable for resonant systems. Use of filters based on orthogonal basis functions (OBF) have been advocated for modelling of resonant process. Kautz filter has been identified as best suited OBF for this purpose. A state space based system identification technique using Kautz filters, viz. Kautz model, has been demonstrated. Model based controllers are believed to be more efficient than classical controllers because explicit use of process model is essential with these modelling techniques. Extensive literature search concludes that very few reports are available which explore use of the model based control studies on resonant system. Two such model based controllers are considered in this work, viz. model predictive controller and internal model controller. A model predictive control algorithm has been developed using the Kautz model. The efficacy of the model and the controller has been verified by two case studies, viz. linear second order underdamped process and a mildly nonlinear magnetic ball suspension system. Comparative assessment of performances of these controllers in those case studies have been carried out. © 2016, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.

引用

页码：501 / 515

页数：14

共 15 条

[1]

Huang C.T., Chou C.J., Estimation of the under damped second-order parameters from the system transient, Industrial and Engineering Chemistry Research, 33, 1, pp. 174-176, (1994)

[2]

Astrom K.J., Hagglund T., PID Controllers: Theory, Design, and Tuning, (1995)

[3]

Ho W.K., Hang C.C., Zhou J.H., Self tuning PID control of a plant with under damped response with specifications on gain and phase margins, IEEE Transactions on Control System Technology, 5, 4, pp. 446-452, (1997)

[4]

Brosilow C., Joseph B., Techniques of Model-based Control, (2002)

[5]

Riveara D.E., Morari M., Skogestad S., Internal model control: PID controller design, Industrial and Engineering Chemistry Process Design and Development, 25, 1, pp. 252-265, (1986)

[6]

Bequette B.W., Process Control: Modeling, Design, and Simulation, (2003)

[7]

Holkar K.S., Waghmare L.M., An overview of model predictive control, International Journal of Control and Automation, 3, 4, pp. 47-63, (2010)

[8]

Rivals I., Personnaz L., Black-box modeling with statespace neural networks, Neural Adaptive Control Technology I, pp. 237-264, (1996)

[9]

da Rosa A., Campello R.J.G.B., Amral W.C., Exact search directions for optimization of linear and nonlinear models based on generalized orthonormal functions, IEEE Transactions on Automatic Control, 54, 12, pp. 2757-2772, (2009)

[10]

Mi W., Qian T., Wan F., A fast adaptive model reduction method based on Takenaka-Malmquist systems, Systems & Control Letters, 61, 1, pp. 223-230, (2012)

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