Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Mathematical Preliminaries
- 3 Dynamic Response
- 4 State-Space Representation
- 5 Analysis of Single-Loop Control Systems
- 6 Design and Tuning of Single-Loop Control Systems
- 7 Stability of Closed-Loop Systems
- 8 Frequency-Response Analysis
- 9 Design of State-Space Systems
- 10 Multiloop Systems
- MATLAB Tutorial Sessions
- Homework Problems
- References
- Index
3 - Dynamic Response
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Mathematical Preliminaries
- 3 Dynamic Response
- 4 State-Space Representation
- 5 Analysis of Single-Loop Control Systems
- 6 Design and Tuning of Single-Loop Control Systems
- 7 Stability of Closed-Loop Systems
- 8 Frequency-Response Analysis
- 9 Design of State-Space Systems
- 10 Multiloop Systems
- MATLAB Tutorial Sessions
- Homework Problems
- References
- Index
Summary
We now derive the time-domain solutions of first- and second-order differential equations. It is not that we want to do the inverse transform, but comparing the time-domain solution with its Laplace transform helps our learning process. What we hope to establish is a better feel between pole positions and dynamic characteristics. We also want to see how different parameters affect the time-domain solution. The results are useful in control analysis and in measuring model parameters. At the end of the chapter, dead time, the reduced-order model, and the effect of zeros are discussed.
What Are We Up to?
Even as we speak of a time-domain analysis, we invariably still work with a Laplace transform. Time domain and Laplace domain are inseparable in classical control.
In establishing the relationship between time domain and Laplace domain, we use only first- and second-order differential equations. That is because we are working strictly with linearized systems. As we have seen in partial-fraction expansion, any function can be “broken up” into first-order terms. Terms of complex roots can be combined together to form a second-order term.
Repeated roots (of multicapacity processes) lead to a sluggish response. Tanks-in-series is a good example in this respect.
With higher-order models, we can construct approximate reduced-order models based on the identification of dominant poles. This approach is used in empirical controller tuning relations in Chap. 6.
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- Process ControlA First Course with MATLAB, pp. 44 - 63Publisher: Cambridge University PressPrint publication year: 2002