Underwater Robots - Gianluca Antonelli.pdf

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200 7. Dynamic Control of UVMSs joint errors [deg] 5 0 −5 0 10 20 30 time [s] Fig. 7.38. Virtual decomposition control +proper adaptive action. Joint position errors compatible with kinematic control strategies; it avoids representation singularities; it is modular, thus simplifying the software debugging and maintenance. Motion control of UVMSs was addressed inliterature inseveral papers. However, few experimental set-up exist which can perform autonomous vehicle/manipulator control. The next step in understanding and evaluating the efficiency and reliability ofthe different approaches passes necessarily through the practical implementation of such algorithms. In this sense, because of the symbolic analogy, numerical simulation analysis only can confirm the results already obtained in industrial robotics. It is obvious, on the other hand, that arigorous numerical simulation of all the subsystems of the UVMSs (sensors, thrusters, interaction, ecc.), is crucial to tune the control law parameters before starting wet tests.
8. Interaction Control of UVMSs 8.1 Introduction to Interaction Control of Robots In view of the development ofanunderwater vehicle able to perform acompletely autonomous mission the capability ofthe vehicle to interact with the environment by the use of amanipulator isofgreatest interest. To this aim, control of the force exchanged with the environment must be properly investigated. Underwater Vehicle-Manipulator Systems are complex systems characterized by several strong constraints that must be taken into account when designing aforce control scheme: • Uncertainty in the model knowledge, mainly due tothe poor description of the hydrodynamic effects; • Complexity ofthe mathematical model; • Structural redundancy of the system; • Difficulty tocontrol the vehicle in hovering, mainly due to the poor thrusters performance; • Dynamic coupling between vehicle and manipulator; • Low sensors’ bandwidth. Limiting our attention to elastically compliant, frictionless environments several control schemes have been proposed in the literature. An overview of interaction control schemes can befound, e.g., in [70, 303]. Stiffness control is obtained by adopting a suitable position control scheme when in contact with the environment [248]. In this case, it is not possible to give areference force value; instead, adesired stiffness attitude of the tip of the manipulator isassigned. Force and position at steady state depend on the relative compliance between the environment and the manipulator. Impedance control allows toachieve the behavior of agiven mechanical impedance at the end effector rather than asimple compliance attitude [149]. In this case, while it is not possible to give areference force value, the force measure at the end effector is required to achieve adecoupled behavior. To allow the implementation ofacontrol scheme that fulfills contact force regulation one should rather consider direct force control [302]. This can be G. Antonelli: Underwater Robots, 2nd Edition, STAR 2, pp. 201–223, 2006. © Springer-Verlag Berlin Heidelberg 2006
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Springer Tracts in Advanced Robotic
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Gianluca Antonelli Underwater Robot
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Editorial Advisory Board EUROPE Her
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Elalocomotiva sembrava fosse un mos
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Acknowledgements The contributions
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Preface to the Second Edition The p
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XVIII Preface to the First Edition
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XX Preface tothe First Edition mani
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XXII Notation η q =[η T 1 ε T η
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XXIV Notation ˜x error variable de
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XXVI Contents 3. Dynamic Control of
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XXVIIIContents A. Mathematical mode
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2 1. Introduction Maybe the first i
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4 1. Introduction (Massachusetts, U
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6 1. Introduction Fig. 1.4. Coordin
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8 1. Introduction Fig. 1.5. Pig, ma
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10 1. Introduction An example of cr
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12 1. Introduction Fig. 1.8. Romeo
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2. Modelling ofUnderwater Robots
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2.2 Rigid Body’s Kinematics 17 Th
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J T k,oqJ k,oq = 1 4 I 3 , that all
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5. compute the quaternion Q by the
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2.3 Rigid Body’s Dynamics 23 Let
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2.4 Hydrodynamic Effects 25 τ 1 =
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C A ( ν )=− C T A ( ν ) ∀ ν
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2.4 Hydrodynamic Effects 29 effects
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2.5 Gravity and Buoyancy 2.5 Gravit
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2.6 Thrusters’ Dynamics 33 Fig. 2
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2.7 Underwater Vehicles’ Dynamics
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y z 2.8 Kinematics of Manipulators
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2.9 Dynamics ofUnderwater Vehicle-M
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2.9 Dynamics ofUnderwater Vehicle-M
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2.11 Identification 43 f e = K ( x
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3. Dynamic Control of 6-DOF AUVs 3.
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3.2 Earth-Fixed-Frame-Based, Model-
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o x z o b z b x b 3.3 Earth-Fixed-F
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3.4 Vehicle-Fixed-Frame-Based, Mode
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o o b y x y b x b 3.5 Model-Based C
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3.6 Mixed Earth/Vehicle-Fixed-Frame
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3.7 Jacobian-Transpose-Based Contro
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3.8 Comparison Among Controllers 59
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3.9 Numerical Comparison Among the
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force [N] moment [Nm] 20 10 0 −10
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80 4. Fault Detection/Tolerance Str
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5. Experiments of Dynamic Control o
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5.3 Experiments of Dynamic Control
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[m] [m] 5 4 3 2 1 0 0 100 200 300 4
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5.3 Experiments of Dynamic Control
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5.4 Experiments of Fault Tolerance
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[deg] 120 100 80 60 40 20 0 −20 5
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6. Kinematic Control of UVMSs “.
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6.2 Kinematic Control 107 UVMS. Thi
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6.2 Kinematic Control 109 singulari
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6.2 Kinematic Control 111 case of t
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6.5 Singularity-Robust Task Priorit
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6.6 Fuzzy Inverse Kinematics 121 It
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Simulations 6.6 Fuzzy Inverse Kinem
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6.6 Fuzzy Inverse Kinematics 125 Fi
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vehicle attitude [deg] 20 10 0 −1
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6.6 Fuzzy Inverse Kinematics 129 It
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[-] 1 0.8 0.6 0.4 0.2 0 close 0 20
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joint positions 1-3 [deg] joint pos
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e.e. position error [m] 0.02 0.01 0
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7. Dynamic Control of UVMSs 7.1 Int
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7.2 Feedforward Decoupling Control
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7.2 Feedforward Decoupling Control
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7.4 Nonlinear Control for UVMSs wit
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Page 183 and 184: [deg] 3 2.5 2 1.5 1 0.5 7.8 Output
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Page 189 and 190: 7.8 Output Feedback Control 169 C T
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Page 197 and 198: [Nm] [Nm] [Nm] 500 0 −500 50 0
Page 199 and 200: [m] [-] [deg] x 10−3 10 8 6 4 2 0
Page 201 and 202: [N] [N] [N] 20 0 −20 40 20 0 −2
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Page 219: vehicle position [m] 0.1 0.05 0 −
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Page 237 and 238: 8.5 Explicit Force Control 217 wher
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Page 245 and 246: 226 9. Coordinated Control of Plato
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Page 259 and 260: 240 A. Mathematical models ⎡ 6 .
Page 261 and 262: 242 A. Mathematical models using th
Page 263 and 264: 244 A. Mathematical models L = 2 m
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Page 267 and 268: References 249 28. Antonelli G.and
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References 253 96. Cui Y. and Sarka
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References 255 134. Garcia R., Puig
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References 257 168. Kato N. and Lan
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References 259 mera. In: IEEE Inter
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References 261 235. Podder T.K. and
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References 263 273. Solvang B., Den
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References 265 310. Yoerger D.R., S
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