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Journal ArticleDOI

Centralised Multi-input Multi-output Controllers for Non-minimum Phase Systems

09 May 2014-Indian Chemical Engineer (Taylor & Francis)-Vol. 56, Iss: 2, pp 106-122
TL;DR: In this paper, a centralised controller for non-minimum-phase quadruple tank systems is designed based on the direct synthesis method, which is further improved by using equivalent transfer functions derived from relative normalised gain array and relative average residence time array as process inverse transfer function matrix.
Abstract: In the present work, the method of designing the centralised controllers for the minimum phase multivariable systems proposed by Vijay Kumar et al. is extended to non-minimum phase systems. The controller is designed based on the direct synthesis method. Inverse of process transfer function matrix in the direct synthesis method is approximated based on relative gain array concept. Maclaurin's series is applied to reduce it to a standard proportional and integral form. The method is further improved by using equivalent transfer function matrix derived from relative normalised gain array and relative average residence time array as process inverse transfer function matrix. Effective transfer function is the equivalent transfer function of gij(s) when all other loops are closed. The desired closed-loop transfer function should contain the process right half plane zero. Quadruple tank process with non-minimum phase behaviour is considered to analyse the performance of the proposed centralised controll...
References
More filters
Book
16 Aug 1989
TL;DR: This book discusses the development of Empirical Models from Process Data, Dynamic Behavior of First-Order and Second-Order Processes, and Dynamic Response Characteristics of More Complicated Processes.
Abstract: PART ONE: INTRODUCTORY CONCEPTS.1. Introduction to Process Control.2. Theoretical Models of Chemical Processes.PART TWO: DYNAMIC BEHAVIOR OF PROCESSES.3. Laplace Transforms.4. Transfer Function and State-Space Models.5. Dynamic Behavior of First-Order and Second-Order Processes.6. Dynamic Response Characteristics of More Complicated Processes.7. Development of Empirical Models from Process Data.PART THREE: FEEDBACK AND FEEDFORWARD CONTROL.8. Feedback Controllers.9. Control System Instrumentation.10. Overview of Control System Design.11. Dynamic Behavior and Stability of Closed-Loop Control Systems.12. PID Controller Design, Tuning, and Troubleshooting.13. Frequency Response Analysis.14. Control System Design Based on Frequency Response Analysis.15. Feedforward and Radio Control.PART FOUR: ADVANCED PROCESS CONTROL.16. Enhanced Single-Loop Control Strategies.17. Digital Sampling, Filtering, and Control.18. Multiloop and Multivariable Control.19. Real-Time Optimization.20. Model Predictive Control.21. Process Monitoring.22. Batch Process Control.23. Introduction to Plantwide Control.24. Plantwide Control System Design .Appendix A: Digital Process Control Systems: Hardware and Software.Appendix B: Review of Thermodynamics Concepts for Conservation Equations.Appendix C: Use of MATLAB in Process Control.Appendix D: Contour Mapping and the Principle of the Argument.Appendix E: Dynamic Models and Parameters Used for Plantwide Control Chapters.

2,285 citations


"Centralised Multi-input Multi-outpu..." refers methods in this paper

  • ...[5] proposed two methods to design centralised controller for minimum phase multivariable systems by direct synthesis approach [6, 7]....

    [...]

Journal ArticleDOI
TL;DR: The quadruple-tank process is ideal for illustrating many concepts in multivariable control, particularly performance limitations due toMultivariable right half-plane zeros, which have an appealing physical interpretation.
Abstract: A multivariable laboratory process that consists of four interconnected water tanks is presented. The linearized dynamics of the system have a multivariable zero that is possible to move along the real axis by changing a valve. The zero can be placed in both the left and the right half-plane. In this way the quadruple-tank process is ideal for illustrating many concepts in multivariable control, particularly performance limitations due to multivariable right half-plane zeros. The location and the direction of the zero have an appealing physical interpretation. Accurate models are derived from both physical and experimental data and decentralized control is demonstrated on the process.

960 citations

Book
01 Jan 1955

325 citations


"Centralised Multi-input Multi-outpu..." refers methods in this paper

  • ...[5] proposed two methods to design centralised controller for minimum phase multivariable systems by direct synthesis approach [6, 7]....

    [...]

Journal ArticleDOI
TL;DR: In this article, the authors describe two model-based methods students can implement for control of this interacting four-tank system, using step tests and Aspen software for use with dynamic matrix control.

137 citations


Additional excerpts

  • ...IAE values in Method used Change in set point Y1 Y2 Decentralised [8] Y1 137 88 Y2 104 147 Centralised (RGA) Y1 187 50 Y2 48 207 Centralised (RNGA) Y1 205 55 Y2 71 218...

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  • ...Decentralised [8] Centralised (RGA) Centralised (RNGA) IAE 651 481 606 ISE 244 262 290 ITAE 990 600 820 TV 12....

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  • ...Decentralised [8] Centralised (RGA) Centralised (RNGA) IAE 475 493 540 ISE 213 290 292 ITAE 510 591 690 TV 6....

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  • ...Centralised controller matrix based on RGA using Equations (21) and (22) is given by: GC2ðsÞ ¼ 0:3058 0:0022s 0:4497þ 0:0030s 0:4116þ 0:0037s 0:3321 0:0020s 2 4 3 5 ð36Þ Centralised controller matrix based on RNGA–RARTA using Equations (30) and (31) is given by: GC3ðsÞ ¼ 0:3068 0:0018s 0:5905þ 0:0027s 0:4884þ 0:0033s 0:4605 0:0016s 2 4 3 5 ð37Þ Decentralised controller matrix is proposed by Gatzke et al. [8] as: GðsÞ ¼ 0:94þ 0:0050 s 0 0 0:99þ 0:0050s ð38Þ These controllers are tuned for γ = 0.75 by adjusting filter time constant (λi)....

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  • ...[8] as: GðsÞ 1⁄4 0:94þ 0:0050 s 0 0 0:99þ 0:0050 s ð38Þ...

    [...]