Application of dimensional analysis in systems modelling and control design
Provides an introduction to the fundamentals of dimensional analysis for control engineers and shows how they can exploit the benefits of the technique to theoretical and practical control
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
London, UK
Institution of Engineering and Technology
2013
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| Series: | IET control engineering series
90 |
| Subjects: | |
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| 008 | 221104 2013 xxka bi 000 0 eng d | ||
| 020 | |a 9781849196215 (cloth) | ||
| 020 | |a 1849196214 (cloth) | ||
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| 040 | |a UPNM |b eng |c UPNM |e rda | ||
| 090 | |a TA 347.D5 |b B35 2013 | ||
| 100 | 1 | |a Balaguer, Pedro |e author | |
| 245 | 1 | 0 | |a Application of dimensional analysis in systems modelling and control design |c Pedro Balaguer |
| 264 | 1 | |a London, UK |b Institution of Engineering and Technology |c 2013 | |
| 300 | |a x, 152 pages |b illustrations |c 24 cm | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a unmediated |2 rdamedia | ||
| 338 | |a volume |2 rdacarrier | ||
| 490 | 1 | |a IET control engineering series |v 90 | |
| 504 | |a Includes bibliographic references and index | ||
| 505 | 0 | |a 1.Introduction -- 1.1.What is dimensional analysis? -- 1.2.What is dimensional similarity? -- 1.3.Application of dimensional analysis to science in general -- 1.3.1.Structure of physical relations -- 1.3.2.Dimensionless representation -- 1.3.3.Dimensional similarity -- 1.4.Application of dimensional analysis to control problems -- 1.4.1.Identification and model validation -- 1.4.2.Control theory -- 1.4.3.Control engineering -- 1.5.Book contents -- 2.Dimensional analysis and dimensional similarity -- 2.1.Physical quantities, units, and dimensions -- 2.1.1.Physical quantity -- 2.1.2.Units -- 2.1.3.Dimensions: fundamental and derived -- 2.1.4.Arithmetic of dimensions -- 2.2.Systems of units: dependence and independence of dimensions -- 2.2.1.System of units -- 2.2.2.Monomial power law -- 2.2.3.Dependent and independent dimensions -- 2.3.Buckingham pi theorem -- 2.4.Matrix approach for finding the dimensionless numbers -- 2.4.1.The dimensional matrix -- 2.4.2.The dimensional set -- 2.5.Dimensional similarity -- 2.5.1.Scale factors -- 2.5.2.Model law -- 2.6.Exercises -- References -- 3.Dynamical systems: dimensionless representation -- 3.1.Introduction -- 3.2.Transfer function dimensionless representation -- 3.2.1.Transfer function parameter dimensions -- 3.2.2.Transfer function parameters with independent dimensions -- 3.2.3.Transfer function dimensionless numbers -- 3.2.4.Dimensionless transfer function -- 3.3.State space dimensionless representation -- 3.3.1.Interpretation of the state space dimensionless transformation -- 3.4.Comparison between transfer function and state space dimensionless representation -- 3.5.Discrete time models dimensionless representation -- 3.5.1.Discrete time transfer function dimensionless representation -- 3.5.2.Discrete time state space model dimensionless representation -- 3.6.Exercises -- References -- 4.Dynamical systems: dimensional similarity -- 4.1.Introduction -- 4.2.Continuous time dynamical systems similarity -- 4.2.1.Transfer function dimensional similarity -- 4.2.2.State space dimensional similarity -- 4.3.Discrete time dynamical system similarity -- 4.3.1.Discrete time transfer function similarity -- 4.3.2.Sampled-data transfer function similarity -- 4.3.3.Discrete state space similarity -- 4.4.Exercises -- References -- 5.Dimensionless systems identification and model order reduction -- 5.1.Introduction -- 5.2.General procedure -- 5.3.Example 1: Second order inverse response model identification -- 5.3.1.Problem statement -- 5.3.2.Dimensionless representation of second order inverse response model -- 5.3.3.Identification procedure -- 5.3.4.Application examples -- 5.4.Example 2: Reduced effective transfer function reduction for PID decentralized control -- 5.4.1.Problem statement -- 5.4.2.Dimensionless representation of the reduced effective transfer function -- 5.4.3.Inverse response analysis -- 5.4.4.Reduced order model: general case -- 5.4.5.Reduced order model: particular cases -- 5.4.6.Application examples -- References -- 6.Homogeneity of PID tuning rules -- 6.1.Introduction -- 6.2.Homogeneous PID tuning rules -- 6.2.1.Dimensionless controller parameters -- 6.2.2.Homogeneous tuning rules characterization -- 6.2.3.Dimensionless controller representation with homogeneous tuning rules -- 6.3.Closed loop transfer functions -- 6.3.1.Loop transfer function GC -- 6.3.2.Dimensionless closed loop transfer functions -- 6.4.Optimality of homogeneous tuning rules -- 6.4.1.Weighting factors -- 6.5.Homogeneous and nonhomogeneous tuning rules -- References -- 7.Dimensionless PID tuning rules comparison -- 7.1.Introduction -- 7.2.Elements of the comparative framework -- 7.3.Dimensionless comparative framework -- 7.4.Dimensionless elements -- 7.4.1.Loop transfer function GC -- 7.4.2.Dimensionless closed loop transfer functions -- 7.4.3.Dimensionless integral errors -- 7.4.4.Indexes -- 7.5.Application example -- 7.5.1.PID tuning rules dimensionless characterization -- 7.5.2.Dimensionless sensitivity bandwidth comparison Wb -- 7.5.3.Dimensionless sensitivity peak comparison -- 7.5.4.Dimensionless integral absolute error -- 7.5.5.Dimensionless control action variation -- 7.6.PID tuning rules selection -- References -- 8.Control of dimensionally similar systems -- 8.1.Introduction -- 8.2.Control of dimensionally similar systems -- 8.3.Complete similarity -- 8.3.1.Continuous time control -- 8.3.2.Discrete time control -- 8.4.Partial similarity -- 8.5.Experimental case study -- References -- 9.Adaptive systems -- 9.1.Introduction -- 9.2.Actuator limitations and dimensionally similar model reference -- 9.2.1.Control effort -- 9.2.2.Similar model reference adaptive control -- 9.3.SMRAC for first order plants -- 9.4.SMRAC for arbitrary order plants -- 9.4.1.SMRAC control scheme -- 9.4.2.SMRAC stability analysis -- 9.4.3.SMRAC operation modes -- 9.5.Application example | |
| 520 | |a Provides an introduction to the fundamentals of dimensional analysis for control engineers and shows how they can exploit the benefits of the technique to theoretical and practical control | ||
| 592 | |a 00007352/14 |b 22/12/2014 |c RM 370.86 |h AREESH | ||
| 650 | 0 | |a Dimensional analysis | |
| 830 | 0 | |a IET control engineering series |v 90 | |
| 999 | |a vtls000053323 |c 99217 |d 99217 | ||