Muutke küpsiste eelistusi

E-raamat: Model Predictive Vibration Control: Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures

  • Formaat: PDF+DRM
  • Ilmumisaeg: 05-Mar-2012
  • Kirjastus: Springer London Ltd
  • Keel: eng
  • ISBN-13: 9781447123330
  • Formaat - PDF+DRM
  • Hind: 246,38 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Formaat: PDF+DRM
  • Ilmumisaeg: 05-Mar-2012
  • Kirjastus: Springer London Ltd
  • Keel: eng
  • ISBN-13: 9781447123330

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Real-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry. If the control of lightly damped mechanical structures is assumed, the region of attraction containing the set of allowable initial conditions requires a large prediction horizon, making the already computationally demanding on-line process even more complex. Model Predictive Vibration Control provides insight into the predictive control of lightly damped vibrating structures by exploring computationally efficient algorithms which are capable of low frequency vibration control with guaranteed stability and constraint feasibility. In addition to a theoretical primer on active vibration damping and model predictive control, Model Predictive Vibration Control provides a guide through the necessary steps in understanding the founding ideas of predictive control applied in AVC such as:· the implementation of computationally efficient algorithms· control strategies in simulation and experiment and· typical hardware requirements for piezoceramics actuated smart structures. The use of a simple laboratory model and inclusion of over 170 illustrations provides readers with clear and methodical explanations, making Model Predictive Vibration Control the ideal support material for graduates, researchers and industrial practitioners with an interest in efficient predictive control to be utilized in active vibration attenuation.

With more than 170 illustrations, this volume provides vital insights into the predictive control of lightly damped vibrating structures through efficient algorithms capable of low-frequency vibration control, guaranteed stability and constraint feasibility.
1 Introduction
1(24)
1.1 What is Active Vibration Control?
3(2)
1.2 The Choice of Strategy in Active Vibration Control
5(2)
1.3 The Role of Model Predictive Control in Active Vibration Control
7(1)
1.4 Model Predictive Vibration Control of Flexible and Lightly Damped Mechanical Systems
8(2)
1.5 About the Book
10(15)
1.5.1 Structure of This Book
10(2)
1.5.2 Do I Have to Read the Whole Book?
12(1)
1.5.3 The Scope and Limitations of This Work
13(3)
1.5.4 Assumptions and Objectives of Part III
16(1)
References
17(8)
Part I Vibration Control
2 Basics of Vibration Dynamics
25(40)
2.1 Free Vibration Without Damping
26(4)
2.2 Free Vibration with Damping
30(3)
2.3 Forced Vibration of a Point Mass
33(2)
2.4 Multiple Degree of Freedom Systems
35(5)
2.4.1 The Eigenvalue Problem
37(1)
2.4.2 Modal Decomposition
38(2)
2.5 Distributed Parameter Systems
40(7)
2.5.1 Exact Solution
40(6)
2.5.2 Damping in Distributed Systems Simulated by FEA
46(1)
2.6 Creating Models for Vibration Control
47(10)
2.6.1 Transfer Function Models
47(8)
2.6.2 Experimental Identification Procedures
55(2)
2.7 Identification via Software Packages
57(3)
2.8 FEM-Based Identification
60(5)
References
61(4)
3 Smart Materials in Active Vibration Control
65(40)
3.1 Shape Memory Alloys
67(6)
3.1.1 SMA Materials and Properties
67(2)
3.1.2 Stress, Strain and Temperature
69(2)
3.1.3 SMA in Vibration Control
71(2)
3.2 Magneto- and Electrostrictive Materials
73(4)
3.2.1 Magnetostrictive Materials
74(1)
3.2.2 Electrostrictive Materials
75(1)
3.2.3 Magneto- and Electrostrictive Materials in Vibration Control
75(2)
3.3 Magneto- and Electrorheological Fluids
77(5)
3.3.1 Magnetorheological Fluids
77(1)
3.3.2 Electrorheological Fluids
78(1)
3.3.3 Magneto- and Electrorheological Materials in Vibration Control
79(3)
3.4 Piezoelectric Materials
82(7)
3.4.1 The Piezoelectric Effect and Materials
82(3)
3.4.2 Piezoelectric Transducers in Vibration Control
85(1)
3.4.3 Mathematical Description of the Piezoelectric Effect
86(2)
3.4.4 FEM Formulation for Piezoelectric Transducers
88(1)
3.5 Electrochemical Materials
89(4)
3.5.1 Dielectric EAP
90(1)
3.5.2 Ionic EAP
91(1)
3.5.3 EAP in Vibration Control
92(1)
3.6 Other Types of Materials and Actuators
93(12)
References
94(11)
4 Algorithms in Active Vibration Control
105(36)
4.1 Classical Feedback Methods
107(5)
4.2 Proportional-Integral-Derivative Controllers
112(5)
4.3 Linear Quadratic Control
117(2)
4.4 H2 and H∞ Control
119(4)
4.5 Soft Computing Approaches
123(6)
4.5.1 Neural Networks
123(2)
4.5.2 Genetic Algorithms
125(2)
4.5.3 Fuzzy Control
127(2)
4.6 Other Approaches
129(12)
References
130(11)
5 Laboratory Demonstration Hardware for AVC
141(66)
5.1 Experimental Device
142(8)
5.1.1 The Cantilever Beam as a Dynamic Model for a Class of Real-Life Applications
143(2)
5.1.2 Brief Device Description
145(2)
5.1.3 Functional Scheme of the Device
147(2)
5.1.4 PZT Transducer Configuration and Usage
149(1)
5.2 Identification Procedure
150(7)
5.2.1 Control Model
151(5)
5.2.2 Capacitive Sensor Feedback Model
156(1)
5.2.3 Piezoelectric Sensor Feedback Model
156(1)
5.3 Device Properties
157(15)
5.3.1 Actuator and Sensor Characteristics
157(5)
5.3.2 Noise and Disturbances
162(2)
5.3.3 Mechanical Properties
164(2)
5.3.4 Capacitive and Piezoelectric Sensor-Based Feedback
166(6)
5.4 FEM Analysis
172(10)
5.4.1 Static Loading
173(1)
5.4.2 Modal Analysis
174(2)
5.4.3 Harmonic Analysis
176(4)
5.4.4 Transient Analysis
180(1)
5.4.5 Control Prototyping
180(2)
5.5 Hardware Components
182(25)
5.5.1 Piezoelectric Transducers
182(4)
5.5.2 Beam Material and Base
186(1)
5.5.3 Measurement of the Tip Displacement
187(5)
5.5.4 Real-Time Control Environment
192(2)
5.5.5 Electrodynamic Shaker and Amplifier
194(1)
References
195(12)
Part II Model Predictive Control
6 Basic MPC Formulation
207(46)
6.1 The MPC Idea
209(4)
6.1.1 Historical Overview
211(2)
6.1.2 Nonlinear Model Predictive Control
213(1)
6.2 Prediction
213(4)
6.3 Cost Functions
217(2)
6.3.1 Building a Quadratic Cost Function
218(1)
6.4 State and Input Penalization
219(2)
6.5 Cost of the Future States and Inputs
221(3)
6.6 Unconstrained Model Predictive Control
224(1)
6.7 Constraint Formulation
225(4)
6.7.1 Hard Saturation Versus Constraint Handling
227(2)
6.8 Constrained Quadratic Programming-Based MPC
229(16)
6.8.1 Quadratic Programming
230(14)
6.8.2 MPC and Quadratic Programming
244(1)
6.9 Prediction and Control Horizon
245(1)
6.10 Fixed Target Tracking
246(1)
6.11 Integral Action
247(6)
References
249(4)
7 Stability and Feasibility of MPC
253(34)
7.1 Development of MPC with Stability Guarantees
256(3)
7.1.1 Equality Terminal Constraints
256(1)
7.1.2 Penalty on the Terminal State
257(1)
7.1.3 Target Sets
257(1)
7.1.4 Combination of Target Sets and Terminal Penalties
258(1)
7.1.5 State Contractility and Others
259(1)
7.2 Closed-Loop Stability of the Infinite Horizon MPC Law
259(4)
7.3 Stability Through Terminal Constraints
263(3)
7.4 Maximal Invariant Terminal Set
266(5)
7.4.1 Implementing the Terminal Constraints
269(2)
7.4.2 Horizon Length
271(1)
7.5 Simplified Polyhedral Target Sets
271(4)
7.6 Elliptic Invariant Target Sets
275(4)
7.7 Infeasibility Handling
279(8)
References
283(4)
8 Efficient MPC Algorithms
287(38)
8.1 Newton-Raphson MPC
291(15)
8.1.1 Basic NRMPC Formulation
292(6)
8.1.2 Extension of the Newton-Raphson MPC
298(3)
8.1.3 Optimizing Prediction Dynamics for the Newton-Raphson MPC
301(4)
8.1.4 Warm Starting and Early Termination
305(1)
8.2 Multi-Parametric MPC
306(6)
8.2.1 Optimal Multi-Parametric MPC
306(5)
8.2.2 Multi-Parametric Programming-Based Minimum Time Suboptimal MPC
311(1)
8.3 Approximate Primal-Barrier Interior Point Method-Based MPC
312(1)
8.4 Efficient MPC Based on Pontryagin's Minimum Principle
313(12)
References
318(7)
Part III Model Predictive Vibration Control
9 Applications of Model Predictive Vibration Control
325(36)
9.1 Concept Demonstration Examples
327(2)
9.1.1 Cantilever Beams
327(1)
9.1.2 Plates and Shells
328(1)
9.1.3 Others
328(1)
9.2 Manipulators in Robotics
329(1)
9.3 Optical Systems
330(1)
9.4 Active Noise Control
331(1)
9.5 Automotive Industry
332(2)
9.6 Civil Engineering
334(3)
9.7 Manufacturing, Machinery and Tools
337(6)
9.7.1 Rotor Systems
338(1)
9.7.2 Active Mounts and Production Systems
339(1)
9.7.3 Anti-Sway Control for Cranes
339(3)
9.7.4 Machine Tools
342(1)
9.8 Vibration Control in Aircraft and Spacecraft
343(18)
9.8.1 Aircraft Vibration
343(3)
9.8.2 Spacecraft Vibration
346(3)
References
349(12)
10 MPC Implementation for Vibration Control
361(30)
10.1 Implementation of the QPMPC Algorithm
364(4)
10.2 Implementation of the MPMPC Control Algorithm
368(7)
10.2.1 Optimal Multi-Parametric Programming-Based MPC
369(5)
10.2.2 Multi-Parametric Programming-Based Minimum-Time Suboptimal MPC
374(1)
10.3 Implementation of the NRMPC Control Algorithm
375(16)
10.3.1 SDP Problem Formulation and Solution
377(1)
10.3.2 Cost Transformation
378(2)
10.3.3 The Newton-Raphson Procedure
380(2)
10.3.4 NRMPC Extension
382(1)
10.3.5 Code Implementation
382(3)
References
385(6)
11 Simulation Study of Model Predictive Vibration Control
391(36)
11.1 On the Horizon Length of Stable MPC
392(6)
11.1.1 Simulating Necessary Horizon Lengths
395(2)
11.1.2 NRMPC and Horizon Length
397(1)
11.2 Properties of MPMPC for Active Vibration Control
398(4)
11.2.1 MPMPC Computation Time
399(2)
11.2.2 MPMPC Regions
401(1)
11.2.3 MPMPC Controller Size
402(1)
11.3 Issues with NRMPC Invariance
402(6)
11.3.1 Performance Bounds
405(2)
11.3.2 Solver Precision and Invariance
407(1)
11.4 Issues with NRMPC Optimality
408(6)
11.4.1 Penalization and Optimality
412(2)
11.5 Alternate NRMPC Extension
414(4)
11.6 Comparison of QPMPC, MPMPC and NRMPC in Simulation
418(9)
References
421(6)
12 Experimental Model Predictive Vibration Control
427(40)
12.1 Linear Quadratic Control
429(2)
12.2 Initial Deflection Test
431(5)
12.3 Frequency Domain Tests
436(6)
12.3.1 Disturbance by Modal Shaker
436(3)
12.3.2 Disturbance by PZT Actuation
439(3)
12.4 Random Excitation
442(2)
12.4.1 Random Excitation by a Modal Shaker
442(1)
12.4.2 Pseudo-Random Excitation by a Medium Sized Fan
443(1)
12.5 Algorithm Speed
444(6)
12.5.1 Initial Deflection
445(1)
12.5.2 Chirp Signal
446(2)
12.5.3 Pseudo-Random Binary Signal
448(2)
12.5.4 Possible Improvements on NRMPC Speed
450(1)
12.6 Conclusions
450(4)
12.7 Closing Remarks
454(13)
12.7.1 Summary of Main Points
454(3)
12.7.2 Future Work
457(3)
References
460(7)
Appendix A FE Modeling of the Active Structure 467(12)
Appendix B MPC Code Implementation Details 479(28)
Legal Information 507(4)
Index 511
Gergely Takács is currently a junior research engineer at the Institute of Automation, Measurement and Applied Informatics of the Faculty of Mechanical Engineering of the Slovak University of Technology in Bratislava, where he received his PhD in mechatronics in 2009. His recent doctoral studies and academic career have been fully devoted to the application of computationally efficient model predictive controllers in the active vibration control of lightly damped structures. He has spent time as a visiting researcher in the Control Group of the Department of Engineering Science at the University of Oxford, where he has been studying efficient predictive control algorithms. His research interests include active vibration control, model predictive control and smart materials.

Professor Boris Roha-Ilkiv received his academic degrees from the Slovak University of Technology in Bratislava, the Faculty of Mechanical Engineering, where he currently works as a tenured professor. He has devoted the majority of his academic career to model predictive control, with special attention given to practical real-time controller implementation issues.