Dynamic Modeling and Neural Network-Based Intelligent Control of Flexible Systems E-Book
Hejia Gao
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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: IEEE Press Series on Control Systems Theory and Applications
- Sprache: Englisch
Comprehensive treatment of several representative flexible systems, ranging from dynamic modeling and intelligent control design through to stability analysis
Fully illustrated throughout, Dynamic Modeling and Neural Network-Based Intelligent Control of Flexible Systems proposes high-efficiency modeling methods and novel intelligent control strategies for several representative flexible systems developed by means of neural networks. It discusses tracking control of multi-link flexible manipulators, vibration control of flexible buildings under natural disasters, and fault-tolerant control of bionic flexible flapping-wing aircraft and addresses common challenges like external disturbances, dynamic uncertainties, output constraints, and actuator faults.
Expanding on its theoretical deliberations, the book includes many case studies demonstrating how the proposed approaches work in practice. Experimental investigations are carried out on Quanser Rotary Flexible Link, Quanser 2 DOF Serial Flexible Link, Quanser Active Mass Damper, and Quanser Smart Structure platforms.
The book starts by providing an overview of dynamic modeling and intelligent control of flexible systems, introducing several important issues, along with modeling and control methods of three typical flexible systems. Other topics include:
- Foundational mathematical preliminaries including the Hamilton principle, model discretization methods, Lagrange’s equation method, and Lyapunov’s stability theorem
- Dynamic modeling of a single-link flexible robotic manipulator and vibration control design for a string with the boundary time-varying output constraint
- Unknown time-varying disturbances, such as earthquakes and strong winds, and how to suppress them and use MATLAB and Quanser to verify effectiveness of a proposed control
- Adaptive vibration control methods for a single-floor building-like structure equipped with an active mass damper (AMD)
Dynamic Modeling and Neural Network-Based Intelligent Control of Flexible Systems is an invaluable resource for researchers and engineers seeking high-efficiency modeling methods and neural-network-based control solutions for flexible systems, along with industry engineers and researchers who are interested in control theory and applications and students in related programs of study.
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Seitenzahl: 339
Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
About the Authors
Preface
Acknowledgments
Acronyms
1 Introduction
1.1 Background and Motivation
1.2 Modeling and Control Strategies of Flexible Robotic Manipulators
1.3 Vibration Control Technologies of Flexible Building-like Structures
1.4 Modeling and Control Approaches of Bionic Flexible Flapping-wing Aircraft
1.5 Outline of the Book
2 Mathematical Preliminaries
2.1 Mathematical Preliminaries
3 Fuzzy Neural Network Control of the Single-Link Flexible Robotic Manipulator
3.1 Introduction
3.2 Problem Formulation
3.3 Fuzzy Neural Network Control
3.4 Numerical Simulations
3.5 Experimental Investigation
3.6 Summary
4 High-Gain Observer-Based Neural Network Control of the Two-Link Flexible Robotic Manipulator
4.1 Introduction
4.2 Problem Formulation
4.3 High-Gain Observer-Based Neural Network Control
4.4 Numerical Simulations
4.5 Experimental Investigation
4.6 Summary
5 Robust Adaptive Vibration Control for a String with Time-Varying Output Constraint
5.1 Introduction
5.2 Problem Formulation
5.3 Control Design
5.4 The Solvability of the Inequality Equations
5.5 Numerical Simulations
5.6 Summary
6 Neural Network Vibration Control of a Stand-Alone Tall Building-Like Structure with an Eccentric Load
6.1 Introduction
6.2 Dynamic Modeling
6.3 Neural Network Vibration Control
6.4 Numerical Simulations
6.5 Experimental Investigation
6.6 Summary
7 Adaptive Vibration Control of a Flexible Structure Based on Hybrid Learning Controlled Active Mass Damping
7.1 Introduction
7.2 Dynamic Modeling
7.3 Hybrid Learning Control
7.4 Simulation Verification and Comparative Analysis
7.5 Experimental Investigation
7.6 Summary
8 Reinforcement Learning Control of a Single-Floor Building-Like Structure with Active Mass Damper
8.1 Introduction
8.2 Problem Formulation
8.3 Reinforcement Learning Control
8.4 Experimental Investigation
8.5 Summary
9 Disturbance Observer-Based Neural Network Control of a Flexible Flapping-Wing System
9.1 Introduction
9.2 Problem Formulation
9.3 Disturbance Observer-Based Neural Network Control
9.4 Summary
10 Adaptive Finite-Time Control of a Bionic Flexible Flapping-Wing Aircraft with Actuator Failures
10.1 Introduction
10.2 Problem Formulation
10.3 Adaptive Finite-Time Control
10.4 Numerical Simulations
10.5 Summary
11 Adaptive Vibration Control for Two-Stage Bionic Flapping Wings Based on Neural Network Algorithm
11.1 Introduction
11.2 Problem Formulation
11.3 Adaptive Vibration Control
11.4 Numerical Simulations
11.5 Summary
12 Boundary Vibration Control of a Floating Wind Turbine System with Mooring Lines
12.1 Introduction
12.2 System Modeling and Preliminaries
12.3 Controller Design
12.4 Numerical Simulations
12.5 Summary
13 Conclusions
References
Index
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Related modeling parameters and their descriptions of the single-l...
Table 3.2 Parameters of the flexible manipulator.
Table 3.3 Parameters of the Quanser test platform.
Chapter 4
Table 4.1 Properties of the two-link flexible robotic manipulator.
Table 4.2 Parameters of the Quanser test platform.
Chapter 5
Table 5.1 Parameters of the string.
Chapter 6
Table 6.1 Parameters of the Quanser test platform.
Chapter 7
Table 7.1 Nomenclatures and symbols used in dynamic modeling.
Table 7.2 Specifications of single-floor building-like structure with an act...
Table 7.3 Comparison of passive mode, PV position control, and hybrid learni...
Chapter 8
Table 8.1 Specifications of one-floor AMD system.
Table 8.2 Comparison of passive mode, PV and RL control.
Chapter 10
Table 10.1 Model parameters of the flexible flapping wing.
Table 10.2 Mode frequencies and amplitudes.
Table 10.3 Comparison of PSF, FNN and AFT control.
Chapter 11
Table 11.1 The bionics and statistics-based parameters of the flapping-wing ...
Chapter 12
Table 12.1 Parameters of the FWT system.
List of Illustrations
Chapter 1
Figure 1.1 Canadarm 2, the Canadian robotic arm on the space station.
Figure 1.2 The Shanghai Tower.
Figure 1.3 The bionic flapping wing aircraft.
Figure 1.4 A single-link flexible manipulator system.
Figure 1.5 A two-link flexible manipulator system.
Figure 1.6 One-floor active mass damper system.
Chapter 2
Figure 2.1 Structure diagram of the RBF neural network.
Chapter 3
Figure 3.1 System representation of the single-link flexible robotic manipul...
Figure 3.2 Neural network control with fuzzy logic systems.
Figure 3.3 Design procedure of the fuzzy neural network controller.
Figure 3.4 The tip position with kg and
Figure 3.5 The tip position with kg and
Figure 3.6 Effect of on the dynamic response with and m
Figure 3.7 Effect of on the dynamic response with and kg
Figure 3.8 Tip position for PD control with
Figure 3.9 Tip position for PD control with
Figure 3.10 The error of
Figure 3.11 PD control inputs
Figure 3.12 Trajectory, control torques, and tracking errors under full-stat...
Figure 3.13 Tip position with under full-state feedback
Figure 3.14 Tip position with under full-state feedback
Figure 3.15 Trajectory and control torques under full-state feedback
Figure 3.16 Tip position with under full-state feedback
Figure 3.17 Tip position with under full-state feedback
Figure 3.18 Trajectory and control inputs under output feedback
Figure 3.19 Tip position with under output feedback
Figure 3.20 Tip position with under output feedback
Figure 3.21 Trajectory and control inputs under output feedback
Figure 3.22 Tip position with under output feedback
Figure 3.23 Tip position with under output feedback
Figure 3.24 Control framework of the experimental flexible link
Figure 3.25 Open-loop performance of the experimental flexible link
Figure 3.26 PD control of the experimental flexible link
Figure 3.27 Fuzzy neural network control of the experimental flexible link
Chapter 4
Figure 4.1 The diagram of the two-link flexible manipulator.
Figure 4.2 The diagram of full-state feedback.
Figure 4.3 The diagram of output feedback.
Figure 4.4 Tracking trajectory and control torque for open-loop system.
Figure 4.5 Tracking trajectory and control torque for PD control.
Figure 4.6 Tip position for PD control.
Figure 4.7 Tracking trajectory and control torque for NN control.
Figure 4.8 Tip position for NN control.
Figure 4.9 Control framework of the experimental flexible link.
Figure 4.10 Diagram of the experimental flexible link.
Figure 4.11 Tracking trajectory with PD and NN control.
Figure 4.12 Elastic vibration with PD and NN control.
Figure 4.13 Control torques with PD and NN control.
Chapter 5
Figure 5.1 The model of the string.
Figure 5.2 Time-varying output constraints on the payload.
Figure 5.3 The solvability of the equations (based on exact model control wi...
Figure 5.4 The solvability of the equations (based on adaptive control with
Figure 5.5 The solvability of the equations (based on adaptive control with
Figure 5.6 The solvability of the equations (based on exact model control wi...
Figure 5.7 The solvability of the equations (based on exact model control wi...
Figure 5.8 The solvability of the equations (based on adaptive control with
Figure 5.9 The solvability of the equations (based on exact model control wi...
Figure 5.10 The solvability of the equations (based on adaptive control with...
Figure 5.11 Displacement of string without control.
Figure 5.12 Displacement of string with PD control.
Figure 5.13 Displacement of string with exact model control.
Figure 5.14 Displacement of string with adaptive control.
Figure 5.15 Displacement of payload.
Chapter 6
Figure 6.1 Stand-alone tall building-like structure in the laboratory. (a) S...
Figure 6.2 Coordinate axes and symbols in dynamic model.
Figure 6.3 Compounded disturbance observer-based adaptive neural network con...
Figure 6.4 Simulation comparison of the angle of the pendulum.
Figure 6.5 Simulation comparison of the deflection of the structure.
Figure 6.6 Simulation comparison of control inputs.
Figure 6.7 Simulation comparison of the accelerations of the flexible struct...
Figure 6.8 Frequency analysis of the deflections (simulation) of the flexibl...
Figure 6.9 Flexible tall building-like structure with an eccentric load. (a)...
Figure 6.10 Experimental comparison of the angle of the pendulum.
Figure 6.11 Experimental comparison of the deflection of the structure.
Figure 6.12 Experimental comparison of control inputs.
Figure 6.13 Experimental comparison of accelerations of flexible structure....
Figure 6.14 Frequency analysis of the deflections (experimental) of the flex...
Chapter 7
Figure 7.1 System dynamic modeling. (a) Stationary state. (b) System represe...
Figure 7.2 Diagram of NN control strategy.
Figure 7.3 Vibration of the flexible floor in simulation verification.
Figure 7.4 Acceleration of the flexible floor in simulation verification.
Figure 7.5 Control inputs in simulation verification.
Figure 7.6 Single-floor building-like structure with an active mass damper. ...
Figure 7.7 Position of the linear cart in passive mode.
Figure 7.8 Vibration of the flexible floor in passive mode.
Figure 7.9 Frequency analysis of the vibration in passive mode.
Figure 7.10 Acceleration of the flexible floor in passive mode.
Figure 7.11 Position of the linear cart under PV position control.
Figure 7.12 Vibration of the flexible floor under PV position control.
Figure 7.13 Frequency analysis of the vibration under PV position control.
Figure 7.14 Acceleration of the flexible floor under PV position control.
Figure 7.15 Position of the linear cart under hybrid learning control.
Figure 7.16 Vibration of the flexible floor under hybrid learning control.
Figure 7.17 Frequency analysis of the vibration under hybrid learning contro...
Figure 7.18 Acceleration of the flexible floor under hybrid learning control...
Chapter 8
Figure 8.1 One-floor active mass damper system.
Figure 8.2 The RL control design diagram.
Figure 8.3 Experimental setup of one-floor AMD system.
Figure 8.4 Comparison results of the acceleration of the flexible floor.
Figure 8.5 Comparison results of the displacement of the flexible floor.
Figure 8.6 Comparison results of the position of the linear cart.
Chapter 9
Figure 9.1 A typical flexible cantilevered beam system.
Figure 9.2 without control.
Figure 9.3 with PD control.
Figure 9.4 Control input of PD control.
Figure 9.5 DO error of PD control.
Figure 9.6 with NN control under full-state feedback.
Figure 9.7 Control input of NN control under full-state feedback.
Figure 9.8 DO error of NN control under full-state feedback.
Figure 9.9 with NN control under output feedback.
Figure 9.10 Control input of NN control under output feedback.
Figure 9.11 DO error of NN control under output feedback.
Chapter 10
Figure 10.1 3D model of a flexible flapping wing system.
Figure 10.2 System representation and notations.
Figure 10.3 Kinematic model of flapping wing using IRFE method. (a) Front vi...
Figure 10.4 Mode visualization results (a) Frist order mode. (b) Second orde...
Figure 10.5 Control design diagram.
Figure 10.6 Flapping angle trajectory under PSF control.
Figure 10.7 Tracking error of the flapping angle under PSF control.
Figure 10.8 Position and vibration of the wing tip under PS control.
Figure 10.9 PSF control input of the flexible flapping wing.
Figure 10.10 Flapping angle trajectory under FNN control.
Figure 10.11 Tracking error of the flapping angle under FNN control.
Figure 10.12 Position and vibration of the wing tip under FNN control.
Figure 10.13 FNN control input of the flexible flapping wing.
Figure 10.14 Flapping angle trajectory under AFT control.
Figure 10.15 Tracking error of the flapping angle under AFT control.
Figure 10.16 Position and vibration of the wing tip under AFT control.
Figure 10.17 AFT control input of the flexible flapping wing.
Chapter 11
Figure 11.1 System representation and notations. (a) The bionic rigid-flexib...
Figure 11.2 Mode visualization results. (a) 1st mode. (b) 2nd mode. (c) 3rd ...
Figure 11.3 The diagram of the adaptive vibration control.
Figure 11.4 Flapping angle trajectory of the rigid wing.
Figure 11.5 Flapping angle trajectory of the flexible wing.
Figure 11.6 Tracking error of the rigid flapping-wing angle.
Figure 11.7 Tracking error of the flexible flapping-wing angle.
Figure 11.8 Wing-tip vibration of the bionic flapping-wing aircraft.
Figure 11.9 Control input of the rigid flapping wing.
Figure 11.10 Control input of the flexible flapping wing.
Chapter 12
Figure 12.1 The floating wind turbine system.
Figure 12.2 Displacement of the FWT system: without control.
Figure 12.3 Displacement of the FWT system movements: with the PD control.
Figure 12.4 Displacement of the FWT system movements: with the boundary cont...
Figure 12.5 , vibration of the tower without control.
Figure 12.6 , vibration of the tower with the PD control.
Figure 12.7 , vibration of the tower with the proposed control.
Figure 12.8 , vibration of the left mooring line without control.
Figure 12.9 , vibration of the left mooring line with PD control.
Figure 12.10 , vibration of the left mooring line with the proposed control...
Figure 12.11 , vibration of the right mooring line without control.
Figure 12.12 , vibration of the right mooring line with PD control.
Figure 12.13 , vibration of the right mooring line with the proposed contro...
Figure 12.14 PD control inputs.
Figure 12.15 Control inputs.
Guide
Cover
Table of Contents
Title Page
Copyright
About the Authors
Preface
Acknowledgments
Acronyms
Begin Reading
References
Index
End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor-in-Chief
Moeness Amin
Jón Atli Benediktsson
Adam Drobot
James Duncan
Ekram Hossain
Brian Johnson
Hai Li
James Lyke
Joydeep Mitra
Desineni Subbaram Naidu
Tony Q. S. Quek
Behzad Razavi
Thomas Robertazzi
Diomidis Spinellis
Dynamic Modeling and Neural Network-Based Intelligent Control of Flexible Systems
Hejia Gao
Anhui University, China
Wei He
University of Science and Technology Beijing, China
Changyin Sun
Southeast University, China
IEEE Press Series on Control Systems Theory and Applications
Maria Domenica Di Benedetto, Series Editor
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About the Authors
Hejia Gao received the BEng degree in intelligence science and technology from the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China, in 2016, and the PhD degree in control science and engineering from the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China, in 2021. She has been working as a visiting researcher for fifteen months at the Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Canada, in 2019. Currently, she is working as an associate professor in the School of Artificial Intelligence, Anhui University, Hefei, China. She has published eight top international journal papers and four international conference papers, and two papers have been ranked as ESI Highly Cited Paper. She has been an associate editor of the 2019 IEEE International Conference on Advanced Robotics and Mechatronics and the co-chair of IEEE 37st Youth Academic Annual Conference of Chinese Association of Automation. At present, she is serving as a reviewer of IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and IEEE/CAA Journal of Automatica Sinica. Her research interests include neural networks, reinforcement learning, flexible systems, and vibration control.
Wei He received the BEng and MEng degrees from the College of Automation Science and Engineering, South China University of Technology, China, in 2006 and 2008, respectively, and the PhD degree from Department of Electrical and Computer Engineering, the National University of Singapore, Singapore, in 2011. He is currently working as a full professor in School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China. He has co-authored three books published in Springer and published over 100 international journal and conference papers. Professor He was awarded a Newton Advanced Fellowship from the Royal Society, UK, in 2017. He was a recipient of the IEEE SMC Society Andrew P. Sage Best Transactions Paper Award in 2017. He is serving as the chair of the IEEE SMC Society Beijing Capital Region Chapter. Since 2018, he has been the chair of Technical Committee on Autonomous Bionic Robotic Aircraft, IEEE Systems, Man and Cybernetics Society. He is serving as an associate editor of IEEE Transactions on Robotics, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Control Systems Technology, IEEE Transactions on Systems, Man, and Cybernetics: Systems, SCIENCE CHINA Information Sciences, IEEE/CAA Journal of Automatica Sinica, Neurocomputing, and an editor of Journal of Intelligent and Robotic Systems.
Changyin Sun received the BEng degree in applied mathematics from the College of Mathematics, Sichuan University, Chengdu, China, in 1996, and the MS and PhD degrees in electrical engineering from Southeast University, Nanjing, China, in 2001 and 2004, respectively. He is currently a professor with the School of Automation, Southeast University, Nanjing, China. He has co-authored four books and published over 160 international journal papers. Professor Sun is a Chinese Association of Automation Fellow. He is serving as an associate editor of IEEE Transactions on Neural Networks and Learning Systems, Neural Processing Letters, and IEEE/CAA Journal of Automatica Sinica. His research interests include intelligent control, flight control, pattern recognition, and optimal theory.
Preface
The flexible system covers many different objects such as flexible robotic manipulators, bionic flexible flapping wing aircraft, and flexible buildings. With a large number of applications of flexible systems, its control theory and method issues have become a prospective high-tech research direction, which attracts concerns from both academic and industrial fields. At present, the control theory and method of flexible systems, such as the tracking and vibration control of multi-link flexible manipulators, the constraint control of flexible buildings under natural disasters, and the fault-tolerant control of bionic flexible flapping-wing robots, has developed into a common scientific problem, which is extremely challenging. In order to solve the technical problems of dynamic modeling and intelligent control of uncertain flexible systems with environmental adaptability, the book makes a systematic and detailed study on modeling mechanism and control strategy of several flexible systems.
Chapter 1 provides an overview of dynamic modeling and intelligent control of flexible systems, introducing several important issues in the study of flexible systems. The modeling and control methods of three typical flexible systems are discussed separately.
Chapter 2 provides the corresponding mathematical preliminaries of subsequent chapters, including the Hamilton principle, model discretization methods, Lagrange’s equation method, neural networks, and Lyapunov stability theorem.
Chapter 3 develops the dynamic model of the single-link flexible robotic manipulator, which overcomes the challenge from the system dynamics being infinite dimensional. The fuzzy neural network control with uniform approximation performance is designed to solve the system uncertainties. Numerical simulations and extensive experiments have been investigated to verify the effectiveness of the proposed methods.
Chapter 4 establishes a finite-dimensional dynamic model of the two-link flexible robotic manipulator. A high-gain observer-based neural network control strategy is proposed to estimate the immeasurable states in practice. The semi-globally uniformly ultimate boundedness (SGUUB) of the closed-loop system is guaranteed via Lyapunov’s stability theory. The simulation and experimental results demonstrate the effectiveness of the proposed control strategy.
Chapter 5 present the vibration control design for a string with the boundary time-varying output constraint. The dynamics of the string is a distributed parameter system described by a partial differential equation and two ordinary differential equations. A barrier Lyapunov function with a logarithmic function is adopted to prevent the time-varying constraint violations. Adaptive control is designed to handle the system parametric uncertainties. Stability analysis and the solvability of the inequality equations are provided. Numerical simulations are provided to illustrate the effectiveness of the proposed control design.
Chapter 6 focuses on a stand-alone tall building-like structure with an eccentric load. A neural network control approach is proposed to suppress vibrations caused by unknown time-varying disturbances (earthquake, strong wind, etc.). The output constraint on the angle of the eccentric load is also considered, and such angle can be ensured within the safety limit by incorporating a barrier Lyapunov function. Simulations and experiments based on MATLAB and Quanser are carried out to verify the feasibility and effectiveness of the proposed control.
Chapter 7 discusses an adaptive vibration control method for a single-floor building-like structure equipped with an active mass damper (AMD). The method uses a hybrid learning control strategy to suppress vibrations caused by unknown time-varying disturbances such as earthquakes or strong winds. The effectiveness of the proposed control approach is demonstrated through experimental investigation on a Quanser Active Mass Damper. The research results aim to bring new ideas and methods to the field of disaster reduction for engineering development.
Chapter 8 investigates a single-floor building-like structure equipped with an active mass damper (AMD). Optimal vibration control, while dealing with system uncertainties, is realized by the reinforcement learning technique. When the unexpected natural disasters occur, the proposed controller applying to the active mass damper can compensate the increase of the system vibration caused by external disturbances. The experimental results in the form of graphics and tables have shown the effectiveness of the proposed control algorithm.
Chapter 9 develops the visualization model of the rigid-flexible coupled bionic flapping wing by the advanced system-level modeling software MapleSim. A novel neural network controller based on disturbance observer technology is proposed to compensate the system uncertainties. The proposed method can successfully suppress the vibration of the flapping wing while accurately track the desired trajectory. Co-simulation results from MapleSim and Matlab/Simulink validate the effectiveness of the proposed method.
Chapter 10 focuses on the flexible wings of the aircraft, which has great advantages, such as being lightweight, having high flexibility, and offering low energy consumption. A novel adaptive finite-time controller based on the fuzzy neural network and nonsingular fast terminal slidingmode scheme are proposed for tracking control and vibration suppression of the flexible wings, while successfully addressing the issues of system uncertainties and actuator failures. Co-simulations through MapleSim and MATLAB/Simulink are carried out to verify the performance of the proposed controller.
Chapter 11 discusses the importance of vibration control for bionic flapping-wing robotic aircraft and autonomous ornithopter applications. A visualization model of the rigid-flexible coupled bionic flapping wing is established using MapleSim software. A novel adaptive vibration controller based on neural network (NN) algorithm is proposed to compensate for system uncertainties. The proposed method can successfully suppress the vibration of the flapping wing while accurately tracking the desired trajectory. The effectiveness of the proposed method is validated through co-simulation results from MapleSim and Matlab/Simulink.
Chapter 12 investigate dynamic modeling, active boundary control design, and stability analysis for a coupled floating wind turbine (FWT) system, which is connected with two flexible mooring lines. It is a coupled beam-strings structure, and we design two boundary controllers to restrain the vibrations of this flexible system caused by external disturbances based on the coupled partial differential equations and ordinary differential equations (PDEs-ODEs) model. Meanwhile, significant performance of designed boundary controllers and system’s stability are theoretically analyzed, and a set of simulation results are provided to show efficacy of the proposed approach.
Chapter 13 summarizes the practical significance in the application of neural network-based intelligent control and proposes some future research directions in this field.
In summary, this book proposes high-efficiency modeling methods and novel intelligent control strategies for several representative flexible systems developed by means of neural networks. The book discusses the tracking control of multi-link flexible manipulators, the vibration control of flexible buildings under natural disasters, and the fault-tolerant control of bionic flexible flapping-wing aircraft. Expanding on its theoretical deliberations, the book includes many case studies demonstrating how the proposed approaches work in practice. The most important features of the book include:
a comprehensive review of modeling and control theory for flexible systems;
detailed presentation of the modeling methods and the neural network-based control strategies;
successful addressing of external disturbances, dynamic uncertainties, output constrains, and actuator faults;
abundance case studies illustrating the important steps in designing the neural network-based control; and
performance analysis of the described control approaches by a large number of figures and tables.
This book can be regarded as an authoritative reference for the field (studies) of dynamics and control of flexible systems. Interested readers will gain a systematic understanding of the flexible systems as well as the technical details involved. The material presented in this book will be useful for researchers and engineers who wish to avoid excessive time in searching high-efficiency modeling methods and neural-network-based control solutions for flexible systems. It is written for industry engineers and researchers who are interested in control theory and the applications. This book is also good for postgraduate students engaged in self-study of adaptive control for the flexible systems.
Hejia Gao
Anhui University, China
Wei He
University of Science and Technology Beijing, China
Changyin Sun
Southeast University, China
Acknowledgments
The completion of this book would not have been achieved without many help and efforts from the group members of the Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, China. Over the course of our researching and writing this book, we hereby express our sincere thanks to all those who have helped us.
First of all, we express our sincere appreciation to our co-workers and colleagues who have contributed to the collaborative research. In particular, we thank Youmin Zhang, from Concordia University, Canada; Xiuyu He, Xinbo Yu, Zhijie Liu from University of Science and Technology Beijing; Juqi Hu, from Anhui University and their research groups for their excellent works, and helpful advice on our research.
Appreciations must be made to Zele Yu, Junjie Zhao, Chuanfeng He, Xiang Wang, Liang Zhang, Jiayi Hou, Yu Liu, Zhijia Zhao, Zhe Jing, Tingting Meng, Linghuan Kong, and Yuhua Song for the interesting discussions and constructive suggestions. Their nice comments contribute to improvements of the book in terms of both the presentation and the organization.
Last but not the least, we want to show our deep gratitude to our families for their invaluable love, support and sacrifices over the years. Their long-term support and care give us a warm mood in writing this book.
This work was supported, in part, by National Natural Science Foundation of China under Grant 62225304, 61933001, 62303010, in part, by the Anhui Natural Science Foundation under Grant 2208085QF207, 2208085QF209, and, in part, by the Anhui Provincial Key Research Program of Universities under Grant 2022AH050087.
Hejia Gao Wei He Changyin Sun
Acronyms
ADP
Adaptive dynamic programming
AFT
An adaptive finite-time
AMM
Assumed mode method
ARV
Acceleration response value
ASM
Absolute coordinate method
AVC
Adaptive vibration controller
DO
Disturbance observer
DOF
Degree of freedom
DC
Direct current
DiffServ
Differentiated Services
DPS
Distributed parameter system
FSM
Finite segment method
FSF
Full-state feedback
FDM
Finite difference method
FEM
Finite element method
FWMAV
Flapping wing micro aerial vehicle
FWT
Floating wind turbine
GB
Global balance
HL
Hybrid learning
LPM
Lumped parameter method
MSM
Modes synthesis method
MIMO
Multi-input and multi-output
MR
Magneto rheological
NFTSM
Nonsingular fast terminal sliding mode
NN
Neural network
ODEs
Ordinary differential equations
PC
Personal computer
Probability distribution function
PDEs
Partial differential equations
PSF
Partial state feedback
PV
Proportional-velocity
QAMD
Quanser One-Floor Active Mass Damper
RBFNN
Radial basis function neural network
SGUUB
Semi-global global uniform ultimate boundedness
TDC
Time delay control
TMD
Tuned mass damper
UDP
User Datagram Protocol
UUB
Uniform ultimate boundedness
VRV
Vibration response value
1Introduction
1.1 Background and Motivation
Flexible systems refer to those structures that can be bent or folded without breaking [286]. Flexible systems have attracted considerable research and OpenDocument efforts due to their multi-faceted advantages such as lightweight and low energy consumption [300]. The significant advances in material science, computing, and artificial intelligence technologies, particularly during the last two decades, have further inspired the society’s expectation and passion for commercially viable flexible systems [368]. However, flexible systems might generate unexpected deformation and vibration during the execution of the task. The vibration will degrade the system performance, even shorten the lifespan of the flexible systems. In addition, the complex environment and sudden failures also bring challenges to the research and application of flexible systems. Therefore, designing an effective control method for suppressing the vibration of the flexible systems is significant in practice.
In recent years, with the increasing maturity of intelligent manufacturing technology and the continuous breakthrough of scientific and technological research results, flexible systems have been widely used and developed in various fields such as aerospace, advanced manufacturing, medical health, and social services. Basically, most mechanical structures in practical engineering can be regarded as flexible systems, in other words, large-span mechanical structures can be regarded as having non-negligible deformation and vibration. The flexible systems mainly have three types of applications, which are briefly described below:
Flexible robotic manipulators
: Flexible robotic manipulators are the most typical representative flexible systems, which have the properties of light structure, low energy consumption, and high load/self-weight ratio. Compared with the traditional rigid manipulators, the flexible manipulators have a larger operating space, higher work efficiency, and faster response. Flexible manipulators have many potential advantages and play a very important role in industrial, national defense, and other application fields
[113]
. Their applications have gradually penetrated into aerospace, medical, and military fields
[188]
, as shown in
Figure 1.1
. With the rapid development of the aerospace industry and the robotics industry, the traditional rigid system dynamic analysis methods and control strategies cannot meet the requirements of practical engineering
[283]
. In recent years, the application research of flexible robotic manipulators has received extensive attention, attracting the interest of many scholars and experts.
The main control goal of the flexible robotic manipulators is to achieve accurate trajectory tracking, and damping vibration (also known as vibration suppression) due to low stiffness is an urgent problem to be solved. The traditional rigid manipulator has a thick base, short arm, limited operating space, and poor flexibility, which cannot meet the requirements of modern industrial automation and high-precision industries. The flexible robotic manipulator has gradually occupied an increasingly important position in the manufacturing, aerospace, and other industries due to its advantages of low energy consumption, flexible operation, and fast response. The flexible robotic manipulator is composed of flexible unit components, mainly including flexible joints and flexible links. These flexible components will produce twisting deformation, elastic deformation, and shear deformation during the movement of the manipulator. The flexible robotic manipulator is thus a rigid–flexible coupled, nonlinear infinite-dimensional distributed parameter system, and its dynamic model is more complex than that of the rigid manipulator. Moreover, the traditional dynamic modeling is too complicated and not suitable for the control design of the high-performance flexible robotic manipulator. Therefore, how to explore efficient dynamic modeling theory for flexible manipulators with dynamic uncertainties is a hot and difficult issue in current research [5].
Figure 1.1 Canadarm 2, the Canadian robotic arm on the space station.
Source: NASA/Public domain.
Flexible building structures
: With the continuous increase of building height, high-rise, and ultra-high-rise buildings can be regarded as flexible building structures. Under external excitations such as earthquakes and strong winds, the vibration of high-rise building structures cannot be ignored. Due to the unpredictability of natural disasters such as earthquakes and strong winds, the seismic and wind-resistant design of structures faces severe challenges. Vibration and displacement control are of critical importance for both high-rise and ultra-high-rise building systems, as shown in
Figure 1.2
. Since the concept of structural control was first proposed in 1972, the research and application of structural vibration control have received more and more attention. At present, it has become the most cutting-edge development directions in the field of structural engineering and structural mechanics. The high-rise structure system can not only provide residents with a comfortable working and living environment can also relieve the huge pressure caused by population growth and shortage of land resources in large cities. Due to the large number of residents and the huge construction cost of the high-rise structure, when it is subjected to natural disasters such as earthquakes and strong winds, once the structure is damaged or collapsed, its influence and destructive power will be huge. Therefore, it is particularly important to effectively suppress or reduce these vibrations caused by earthquakes and winds, so that the safety, usability, and comfort of high-rise structures can be guaranteed.
The main control goal of flexible building structures is to achieve vibration suppression under unexpected disturbances. The existing vibration control methods of flexible building structures mostly employed traditional passive control strategies. However, the installation and maintenance as well as the replacement of the dampers are time-consuming and non-trivial work. Compared with passive control, active control can select control objectives, such as structural response (displacement, velocity, and acceleration response) and structural displacement, improving the control effectiveness. Actually, the complex flexible building structures, whose degrees of freedom are close to infinity, are distributed parameter systems with many dynamic uncertainties [137]. Most importantly, the dynamic response cannot be predicted accurately. Therefore, how to study the vibration control method of flexible building structures under natural disasters is an urgent problem to be solved.
Figure 1.2 The Shanghai Tower.
Source: Baycrest/Wikimedia Commons/CC BY SA 2.5.
Flexible bionic flapping-wing systems
: The flexible bionic flapping-wing robotic aircraft is inspired by the flight of birds or insects, which has great advantages, such as being lightweight, having high flexibility, and offering low energy consumption. The bionic flapping-wing robotic aircraft has attracted the special attention of many researchers in recent years
[316]
. The aircraft can stabilize the fuselage in the horizontal position or glide in the sky through flapping wings. The bionic flapping-wing aircraft can generate lift force with high efficiency. In addition, with the advantage of low energy consumption, the bionic flapping-wing aircraft is suitable for flight missions without energy replenishment under long-distance conditions. The bionic flapping-wing aircraft is therefore widely used in both the military tasks (low-altitude surveillance, urban combat, accurate delivery, etc.) and civilian applications (disaster monitoring and relief, field exploration, etc.)
[244]
, as shown in
Figure 1.3
. Compared with fixed wings and rotary wings, flapping wings have the advantage in flight efficiency [
227
,
363
