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Modeling and Nonlinear Robust Control of Delta-Like Parallel Kinematic Manipulators [Pehme köide]

(La Salle University, Mexico), (LIRMM, University of Montpellier, CNRS, Montpellier, France.), (Polytechnic University of Tulancingo, Mexico)
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Modeling and Nonlinear Robust Control of Delta-Like Parallel Kinematic ManipulatorsSuurem pilt
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Püsilink: https://www.kriso.ee/db/9780323961011.html
Märksõnad:
Modelling and Nonlinear Robust Control of Delta-Like Parallel Kinematic Manipulators deals with the modeling and control of parallel robots. The book's content will benefit students, researchers and engineers in robotics by providing a simplified methodology to obtain the dynamic model of parallel robots with a delta-type architecture. Moreover, this methodology is compatible with the real-time implementation of model-based and robust control schemes. And, it can easily extend the proposed robust control solutions to other robotic architectures.
  • Introduces a novel parallel robot designed for machining operations called SPIDER4
  • Presents a mathematical formulation of the kinematic and dynamic models of SPIDER4
  • Offers validation of the computed mathematical models and designed controllers through real-time experiments under different operating conditions
About the authors ix
Preface xi
1 Introduction
1.1 Classification of robotic manipulators
1(5)
1.1.1 Serial manipulators
1(1)
1.1.2 Parallel kinematic manipulators
2(1)
1.1.3 Serial versus parallel manipulators
3(1)
1.1.4 Hybrid manipulators
4(2)
1.2 Overview of Parallel Kinematic Manipulators (PKMs)
6(11)
1.2.1 Historical review of PKMs
6(4)
1.2.2 Main applications of parallel kinematic manipulators
10(7)
1.3 Control problem formulation
17(4)
1.3.1 PKMs control challenges
17(4)
2 Literature review about modeling and control of PKMs
2.1 Introduction
21(1)
2.2 Dynamic modeling of parallel kinematic machines
21(8)
2.2.1 Dynamic modeling approaches for PKMs
22(5)
2.2.2 Dynamic modeling representation
27(2)
2.3 Overview of motion controllers for PKMs
29(1)
2.4 Non-model-based control schemes
29(4)
2.4.1 Non-model-based non-adaptive controllers
29(3)
2.4.2 Non-model-based-adaptive controllers
32(1)
2.5 Model-based control schemes
33(10)
2.5.1 Model-based-non-adaptive controllers
33(3)
2.5.2 Model-based-adaptive controllers
36(7)
3 Description and modeling of experimental platforms
3.1 Introduction
43(1)
3.2 Kinematic modeling for delta-like PKMs
44(3)
3.2.1 Inverse kinematic formulation
44(1)
3.2.2 Forward kinematic formulation
45(1)
3.2.3 Jacobian computation
45(2)
3.3 Dynamic modeling for delta-like PKMs
47(2)
3.3.1 Principle of modeling
47(1)
3.3.2 Torques of forces due to the actuation system
48(1)
3.3.3 Torques due to the traveling plate
49(1)
3.3.4 The general expression
49(1)
3.4 Application to modeling algorithms to 3-DOF Delta PKM
49(14)
3.4.1 Inverse kinematic model
50(5)
3.4.2 Forward kinematic model
55(4)
3.4.3 Velocity relationship and Jacobian analysis
59(2)
3.4.4 Inverse dynamic model
61(2)
3.5 Application to modeling algorithms to 5-DOF SPIDER4 RA-PKM
63(22)
3.5.1 Inverse kinematic model
64(7)
3.5.2 Forward kinematic model
71(5)
3.5.3 Velocity relationship and Jacobian analysis
76(3)
3.5.4 Inverse dynamic model of the delta-like positioning mechanism
79(3)
3.5.5 Inverse dynamic model of the wrist
82(2)
3.5.6 Inverse dynamic model of SPIDER4 RA-PKM
84(1)
3.6 The actuation redundancy issue on SPIDER4 RA-PKM
85(1)
3.7 Conclusion
86(1)
4 Proposed robust control solutions
4.1 Introduction
87(1)
4.2 Background on RISE feedback control
88(3)
4.2.1 Control law
89(1)
4.2.2 Application of standard RISE feedback control to PKMs
89(2)
4.3 Control solution 1: A RISE controller with nominal feedforward
91(6)
4.3.1 Motivation
91(1)
4.3.2 Proposed control law
92(5)
4.4 Control solution 2: A RISE feedforward controller with adaptive feedback gains
97(5)
4.4.1 Motivation
97(1)
4.4.2 Proposed control law
98(4)
4.5 Conclusion
102(3)
5 Simulation and real-time experimental results
5.1 Introduction
105(1)
5.2 Performance evaluation criteria
106(1)
5.3 Tuning gains procedure
106(2)
5.3.1 Tuning gains procedure for control solution 1
107(1)
5.3.2 Tuning gains procedure for control solution 2
107(1)
5.4 Simulation results for Delta PKM
108(7)
5.4.1 Software settings for simulations
108(1)
5.4.2 Description of the evaluation scenarios
108(4)
5.4.3 Simulation results of scenario 1
112(2)
5.4.4 Simulation results of scenario 2
114(1)
5.5 Real-time experimental results for SPIDER4 RA-PKM
115(7)
5.5.1 Testbed hardware and software description
115(3)
5.5.2 Reference trajectory generation
118(2)
5.5.3 Evaluation scenarios
120(2)
5.6 Experimental results of control solution 1
122(2)
5.7 Experimental results of control solution 2
124(12)
5.7.1 Machining path evaluation at low speed
126(2)
5.7.2 Machining path evaluation at medium speed
128(2)
5.7.3 Machining path evaluation at high speed
130(6)
5.8 Conclusion
136(3)
General conclusion
139(4)
A Trajectory points for SPIDER4 real-time experiments
A.1 Trajectory points for scenario 1
143(1)
A.2 Trajectory points for scenario 2
144(3)
Bibliography 147(8)
Index 155
Jonatan Martin Escorcia Hernández received his B.Sc in Robotic Engineering, M.Sc. in Automation and Control, and Ph.D. in Optomechatronics from the Polytechnic University of Tulancingo (UPT), Tulancingo de Bravo, Mexico in 2013, 2017, and 2020, respectively. He is currently working as a part time professor at the UPT, teaching classes in robotics engineering. His research interests include modeling, mechanical design, and nonlinear control of robotics systems. Ahmed Chemori earned his M.Sc. and Ph.D. in Automatic Control from the Grenoble Institute of Technology in 2001 and 2005, respectively. He has worked as a research and teaching assistant and is currently a senior research scientist at LIRMM, University of Montpellier, focusing on nonlinear control and its applications in robotics. Hipólito Aguilar Sierra received the B.Sc. degree in Mechatronics Engineering from UPIITA-IPN in 2009; and M.Sc. and Ph. D degrees both in Automatic Control from the CINVESTAV Zacatenco, Mexico City, Mexico, in 2011 and 2016, respectively. He is currently a Full-time professor at Faculty of Engineering from the La Salle Mexico University. His research interests include Medical robots, Rehabilitation robots, Exoskeleton robotics and Nonlinear control.