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Nonlinear Control of Robots and Unmanned Aerial Vehicles: An Integrated Approach [Kõva köide]

(School of Engineering and Material Science, Queen Mary, University of London, England, UK)
  • Formaat: Hardback, 544 pages, kõrgus x laius: 280x210 mm, kaal: 2020 g, 17 Tables, black and white; 28 Illustrations, color; 122 Illustrations, black and white
  • Ilmumisaeg: 23-Aug-2016
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1498767044
  • ISBN-13: 9781498767040
Teised raamatud teemal:
Nonlinear Control of Robots and Unmanned Aerial Vehicles: An Integrated ApproachSuurem pilt
  • Formaat: Hardback, 544 pages, kõrgus x laius: 280x210 mm, kaal: 2020 g, 17 Tables, black and white; 28 Illustrations, color; 122 Illustrations, black and white
  • Ilmumisaeg: 23-Aug-2016
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1498767044
  • ISBN-13: 9781498767040
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781498767040.html
Märksõnad:
Nonlinear Control of Robots and Unmanned Aerial Vehicles: An Integrated Approachprovides a step-by step-approach to designing control systems for robot manipulators and unmanned aerial vehicles (UAVs). Integration of sophisticated control technology is vital to the design of new emerging mobile vehicles, such as personal air vehicles (PAVs) and the next generation of UAVs. Similar technologies are used in robotic manipulators, such as the PUMA range now used in automated manufacturing systems. This book shows the similarities within unmanned aerial vehicle and robotic system design, and will serve as a vital resource in the design of new products and systems.

Arvustused

"In one volume Vepa has done a very good job of concisely presenting methodologies and theories for realising the control of unmanned aerial vehicles and robots. It is written in such a way that is easy to understand and hence apply. It is like a toolkit of methodologies and equations to understand various Robot platform problems and challenges as well as control theories and approaches one could bring to bear to solve them in various scenarios. It is also a good book for those who are interested in model based design of control systems. The way the book is written enables a reader to read each chapter independently thereby making it suited for a quick read to learn a concept or brush up on ones knowledge. I believe this book will appeal to a wide readership of industrial engineers as well as academics interested in extending the frontiers of control theory on UAVs and Robots." The Aeronautical Journal, May 2018 Issue

Preface xv
Author xvii
1 Lagrangian methods and robot dynamics
1(62)
Introduction
1(1)
1.1 Constraining kinematic chains: Manipulators
2(1)
Manipulator kinematics: The Denavit and Hartenberg (DH) parameters
2(1)
Velocity kinematics: Jacobians
3(1)
Degrees of freedom: The Gruebler criterion and Kutzbach's modification
3(1)
1.2 The Lagrangian formulation of dynamics
3(4)
Principle of virtual work
4(1)
Principle of least action: Hamilton's principle
5(1)
Generalized coordinates and holonomic dynamic systems
6(1)
Euler--Lagrange equations
6(1)
1.3 Application to manipulators: Parallel and serial manipulators
7(4)
Three-degree-of-freedom parallel manipulator
7(2)
Cartesian and spherical manipulators
9(2)
1.4 Dynamics of planar manipulators: Two-link planar manipulators
11(6)
Euler--Lagrange equations
14(3)
1.5 The SCARA manipulator
17(1)
1.6 A two-link manipulator on a moving base
18(4)
1.7 A planar manipulator: The two-arm manipulator with extendable arms
22(4)
1.8 The multi-link serial manipulator
26(4)
1.9 The multi-link parallel manipulator: The four-bar mechanism
30(3)
1.10 Rotating planar manipulators: The kinetic energy of a rigid body in a
Moving frame of reference
33(1)
1.11 An extendable arm spherical manipulator
34(4)
Adding a point mass at the tip
35(1)
Adding a spherical 3--2--1 sequence wrist at the tip
36(2)
1.12 A rotating planar manipulator: The PUMA 560 four-link model
38(6)
1.13 Spatial manipulators: Manipulator dynamics in terms of DH parameters
44(8)
Application to the Stanford manipulator
48(4)
1.14 Application to mobile vehicles
52(11)
Exercises
56(5)
References
61(2)
2 Unmanned aerial vehicle dynamics and Lagrangian methods
63(36)
2.1 Flight dynamics of UAVs
63(1)
2.2 Newton--Euler equations of a rigid aircraft
64(5)
Lagrangian and Hamiltonian formulations
69(1)
2.3 Euler--Lagrange equations of motion in quasi-coordinates
69(10)
Transformation to centre of mass coordinates
73(2)
Application of the Lagrangian method to a rigid aircraft
75(4)
2.4 Complete equations of motion of UAV
79(14)
Equations of motion in wind axis coordinates, VT, α and β
83(5)
Forces and moments due to engine thrust
88(1)
Equations of motion in velocity axes
88(5)
2.5 Direct inversion of attitude dynamics
93(6)
Exercises
96(2)
References
98(1)
3 Feedback linearization
99(40)
Introduction
99(1)
3.1 Lie derivatives, Lie brackets and Lie algebra
99(1)
3.2 Feedback linearization: Pure feedback systems
100(2)
3.3 Input--output feedback linearization
102(2)
3.4 Partial state feedback linearization
104(1)
3.5 Input to state feedback linearization
105(1)
3.6 Applications of feedback linearization
105(10)
3.7 Feedback decoupling
115(8)
3.8 Dynamic feedback linearization
123(3)
3.9 Partial feedback linearization of the ACROBOT
126(13)
Evolution of the humanoid robot model
126(1)
Dynamic models of the ACROBOT
126(1)
Partial feedback linearization
127(2)
Defining the transformations of the state vector
129(4)
The relative degree with T1 ≡ 0, the output and zero dynamics
133(1)
An alternate approach to feedback linearization
133(1)
Exercises
134(2)
References
136(3)
4 Linear and phase plane analysis of stability
139(64)
Introduction
139(1)
4.1 The phase plane
139(1)
4.2 Equilibrium and stability: Lyapunov's first method
140(21)
Regular and singular points
147(2)
The saddle point
149(2)
Sinks or attractors: Focus, spiral, node and improper node
151(1)
Centre
151(1)
Sources or repellers
151(1)
Limit cycles
152(2)
Stability analysis of nonlinear vibrating systems with linear damping
154(7)
4.3 Response of nonlinear vibrating systems: Geometric and algebraic approaches
161(14)
Non-numerical geometric methods
161(2)
Numerically oriented geometric methods
163(2)
The perturbation method
165(6)
Variation of parameters
171(3)
Harmonic balance and describing functions
174(1)
4.4 Examples of nonlinear systems and their analysis
175(20)
Undamped free vibration of a simple pendulum
175(9)
Duffing oscillator: Approximate analysis of the forced vibration of a nonlinear oscillator
184(9)
Van der Pol oscillator: The occurrence of periodic oscillations in a nonlinear oscillator with nonlinear dissipation
193(2)
4.5 Features of nonlinear system responses
195(8)
Superharmonic response
195(1)
Jump phenomenon
195(1)
Subharmonic resonance
196(1)
Combination resonance
196(1)
Self-excited oscillations
196(2)
Exercises
198(4)
References
202(1)
5 Robot and UAV control: An overview
203(24)
Introduction
203(2)
5.1 Controlling robot manipulators
205(1)
5.2 Model-based and biomimetic methods of control
206(1)
5.3 Artificial neural networks
207(3)
5.4 Boolean logic and its quantification
210(1)
5.5 Fuzzy sets
211(4)
Operations on fuzzy sets
212(2)
Relations between fuzzy sets
214(1)
5.6 Fuzzy logic and the implications of a rule
215(1)
5.7 Fuzzy reasoning
216(2)
5.8 Fuzzy logic control
218(2)
5.9 Typical application
220(7)
Exercises
224(2)
References
226(1)
6 Stability
227(14)
6.1 Stability concepts
227(1)
6.2 Input/output stability
228(1)
6.3 Internal stability
228(1)
6.4 Input to state stability
228(1)
6.5 Advanced stability concepts
228(1)
6.6 Passive systems
229(1)
6.7 Linear systems: The concept of passivity and positive real systems
230(2)
6.8 Nonlinear systems: The concepts of hyperstability
232(1)
6.9 Lure's problem
233(1)
6.10 Kalman--Yakubovich (KY) and other related lemmas
234(1)
6.11 Small-gain theorem
235(1)
6.12 Total stability theorem
236(5)
Exercises
237(1)
References
238(3)
7 Lyapunov stability
241(12)
Introduction
241(1)
7.1 Lyapunov, asymptotic, exponential, uniform, local and global stability
242(1)
7.2 Lyapunov's stability theorem
243(1)
7.3 Lyapunov's second or direct method
243(2)
The positive definite function
244(1)
The Lyapunov function and its application to the synthesis of L1 controllers
244(1)
The control Lyapunov function
244(1)
Relationship to the ∞-norm
245(1)
7.4 Lyapunov's direct method: Examples
245(1)
7.5 LaSalle's invariant set theorems
246(1)
7.6 Linear time-invariant (LTI) systems
247(1)
7.7 Barbalat's lemma and uniform ultimate boundedness
248(5)
Exercises
249(2)
References
251(2)
8 Computed torque control
253(16)
Introduction
253(1)
8.1 Geometric path generation
254(3)
8.2 Motion control of a robot manipulator
257(1)
8.3 Computer simulation of robotic manipulators in MATLAB/Simulink
258(3)
8.4 The computed torque control concept
261(3)
8.5 Proportional--derivative (PD) and proportional--integral--derivative (PID) auxiliary control laws
264(1)
8.6 Choosing the demanded joint angles
265(2)
8.7 Simulation of robot dynamics and the feedback controller
267(2)
Exercises
267(1)
References
267(2)
9 Sliding mode control
269(36)
Introduction
269(1)
9.1 Design example
270(3)
9.2 Phase plane trajectory shaping
273(4)
9.3 Sliding line and sliding mode
277(1)
9.4 The Lyapunov approach: Choosing the control law
278(1)
9.5 Closed-loop system: The general case
279(2)
9.6 Principles of variable structure control
281(1)
9.7 Design of sliding mode control laws
281(1)
9.8 Application example
282(3)
9.9 Higher-order sliding mode control
285(1)
9.10 Application examples
286(19)
Second-order twisting algorithm: Inverted pendulum on a cart model
286(5)
First-order sliding mode control
291(2)
Second-order sliding mode control
293(1)
Super-twisting algorithm
294(7)
Exercises
301(2)
References
303(2)
10 Parameter identification
305(16)
Introduction
305(1)
10.1 The parameter identification concept: Transfer function identification
306(1)
10.2 Model parameter identification
307(1)
10.3 Regression and least squares solution
308(1)
10.4 Recursive parameter updating
309(1)
10.5 Matrix inversion lemma
310(1)
10.6 The recursive algorithm
310(1)
10.7 Application examples: Example 10.1
311(3)
10.8 Least squares estimation
314(1)
10.9 The generalized least squares problem
314(2)
10.10 The solution to the generalized least squares problem in recursive form
316(1)
10.11 The nonlinear least squares problem
316(2)
10.12 Application examples: Example 10.2
318(3)
Exercises
319(1)
References
319(2)
11 Adaptive and model predictive control
321(64)
11.1 The adaptive control concept
321(1)
11.2 Basics of adaptive control
322(2)
11.3 Self-tuning control
324(4)
11.4 Methods of parameter identification
328(1)
11.5 Model reference adaptive control
328(4)
11.6 Indirect and direct adaptive control
332(1)
11.7 Inverted pendulum on a cart model
333(8)
Adaptive sliding mode control: The nominal and actual models of the plant
336(1)
The regressor matrix
337(1)
Defining the Lyapunov function and its derivative
338(1)
Derivation of the control and adaptation laws
339(2)
11.8 Adaptive control of a two-link serial manipulator
341(6)
Modeling the parameter updates
343(1)
Governing dynamics
344(1)
Defining the Lyapunov function and its derivative
345(1)
Derivation of the control and adaptation laws
345(2)
11.9 PID tracking control and the sliding surface: The general approach
347(2)
11.10 Robust adaptive control of a linear plant
349(5)
11.11 Robust adaptive control of a robot manipulator
354(3)
11.12 Neural network--based adaptive control
357(2)
11.13 Model predictive control (MPC)
359(26)
MPC with a linear prediction model
362(3)
MPC with a nonlinear prediction model
365(1)
Dynamic model
365(1)
Perturbation dynamics
366(5)
MPC with a nonlinear filter/controller
371(4)
MPC with a nonlinear H∞ controller
375(6)
Exercises
381(2)
References
383(2)
12 Lyapunov design: The backstepping approach
385(40)
Introduction
385(1)
12.1 Lyapunov stability
385(1)
Definition of Lyapunov stability revisited
385(1)
Positive definite function revisited
385(1)
Second method of Lyapunov revisited
385(1)
12.2 Motivating examples
386(2)
12.3 The backstepping principle
388(2)
12.4 The backstepping lemma
390(2)
12.5 Relationship to H∞ control
392(2)
12.6 Model matching, decoupling and inversion
394(2)
12.7 Application of the backstepping lemma
396(10)
12.8 Design of a backstepping control law for the ACROBOT
406(7)
Construction of a Lyapunov function
406(1)
Construction of a Lyapunov function
407(6)
12.9 Designing the auxiliary controller for the alternate feedback linearization
413(12)
Construction of a Lyapunov function
414(4)
Reducing the control law
418(3)
Exercises
421(1)
References
422(3)
13 Hybrid position and force control
425(32)
Introduction
425(5)
13.1 Hybrid position and force control (direct force control)
430(8)
Example: Hybrid force--position control by decoupling the position and force control loops
430(1)
Motion constraint equations for the end-effector tip to the maintain contact
431(1)
Modeling the system for decoupling control
432(2)
Defining the auxiliary controls
434(2)
The decoupling control law
436(2)
13.2 Hybrid position and force control: The general theory
438(4)
13.3 Indirect adaptive control of position and force
442(1)
13.4 Direct adaptive control of impedance
443(2)
13.5 Sliding mode control of impedance and position
445(2)
13.6 Operational space concept
447(2)
13.7 Active interaction control
449(1)
13.8 Coordinated spatial control of multiple serial manipulators in contact with an object
450(3)
13.9 Coordinated spatial control of multiple serial manipulators in contact with a constrained object
453(4)
Exercise
454(1)
References
454(3)
14 UAV control
457(80)
Introduction
457(1)
14.1 Aircraft/UAV parameter estimation
458(1)
14.2 Application of parameter estimation to stability and control
459(12)
14.3 Motion control of rigid bodies
471(5)
14.4 Nonlinear dynamic inversion
476(11)
Scalar and vector backstepping
477(1)
Examples
478(9)
14.5 Dynamics of a quadrotor UAV
487(3)
14.6 Backstepping control of the quadrotor
490(7)
14.7 Backstepping control of a fixed-wing aircraft
497(7)
14.8 Adaptive control of UAVs
504(2)
14.9 Flight control of UAVs with dynamic inversion control
506(7)
Stability of the closed loop without adaptation
508(2)
Adaptive dynamic inversion
510(1)
Stability of the closed loop with adaptation
511(2)
14.10 Adaptive flight path tracking of fixed-wing UAVs
513(4)
14.11 Adaptive attitude control of fixed-wing UAVs
517(4)
14.12 Attitude control of fixed-wing UAVs with adaptive dynamic inversion
521(1)
14.13 Guidance of UAVs
522(15)
Basic flight planning
523(6)
Line-of-sight (LOS)-based pursuit guidance
529(1)
Straight-line guidance
530(1)
Exercises
530(3)
References
533(4)
Index 537
Dr. Ranjan Vepa earned his PhD in applied mechanics from Stanford University, Stanford, California, specialising in the area of aeroelasticity under the guidance of the late Professor Holt Ashley. He currently serves as a Reader in Aerospace Engineering in the School of Engineering and Material Science, Queen Mary University of London, where he has also been the programme director of the Avionics Programme since 2001. He is the author of seven books titled: Electric Aircraft Dynamics: A Systems Engineering Approach (CRC Press, 2020), Dynamics and Control of Autonomous Space Vehicles and Robotics (2019), Nonlinear Control of Robots and Unmanned Aerial Vehicles: An Integrated Approach (CRC Press, 2016), Flight Dynamics Simulation and Control of Aircraft: Rigid and Flexible (CRC Press, 2014), Dynamic Modelling, Simulation and Control of Energy Generation (2013), Dynamics of Smart Structures (2010) and Biomimetic Robotics: Mechanisms and Control (2009).

His research interests include the design of control systems, and associated signal processing with applications in aerospace systems, smart structures, robotics, biomedical engineering, and energy systems. In particular, the research interests include dynamics and robust adaptive estimation and control of linear and nonlinear aerospace, energy systems, including renewable and sustainable energy and desalination systems with parametric and dynamic uncertainties. Dr. Vepa is a member of the Royal Aeronautical Society, London; the Institution of Electrical and Electronic Engineers (IEEE), New York; a fellow of the Higher Education Academy; a member of the Royal Institute of Navigation, London; and a chartered engineer.