Underwater Robots - Gianluca Antonelli.pdf

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180 7. Dynamic Control of UVMSs [m] [-] [deg] x 10−3 10 8 6 4 2 0 8 6 4 2 0 0 10 20 30 40 50 60 x 10−3 10 3 2 1 time [s] 0 10 20 30 40 50 60 time [s] 0 0 10 20 30 40 50 60 time [s] [m] [-] [deg] x 10−3 10 8 6 4 2 0 8 6 4 2 0 0 10 20 30 40 50 60 x 10−3 10 3 2 1 time [s] 0 10 20 30 40 50 60 time [s] 0 0 10 20 30 40 50 60 time [s] Fig. 7.18. Output feedback control; third case study. Comparison between the control laws (7.43),(7.44) and (7.63): norm of the tracking (solid) and estimation (dashed) errors. Left: control law (7.43),(7.44). Right: control law (7.63). Top: vehicleposition error; Middle: vehicleorientation error (vector part of the quaternion); Bottom: joint position errors
[N] [N] [N] 20 0 −20 40 20 0 −20 −40 0 10 20 30 40 50 60 −60 0 10 20 30 40 50 60 100 0 −100 7.9 Virtual Decomposition Based Control 181 X X time [s] time [s] −200 0 10 20 30 40 50 60 time [s] [N] [N] [N] 20 0 −20 Y Y 40 20 0 −20 −40 0 10 20 30 40 50 60 −60 0 10 20 30 40 50 60 100 0 −100 Z Z time [s] time [s] −200 0 10 20 30 40 50 60 time [s] Fig. 7.19. Output feedback control; third case study. Comparison between the control laws (7.43),(7.44) and (7.63): vehicle control forces. Left: control law (7.43),(7.44). Right: control law (7.63) 7.9 Virtual Decomposition Based Control Divide et Impera. Anonymous from the middle age. Usually, adaptive control approaches for UVMSs look atthe system as a whole, giving rise to high-dimensional problems: differently from the case of earth-fixed manipulators, in the case of UVMSs itisnot possible to achieve areduction of the number of dynamic parameters to be adapted, since the base of the manipulator —i.e., the vehicle— has full mobility. As amatter of fact, the computational load of such control algorithms grows as much as the fourth-order power ofthe number of the system’s degrees of freedom. Forthis reason, practical application of adaptive control to UVMS has been limited, even in simulation, to vehicles carrying arms with very few joints (i.e., two or three) and usually performing planar tasks. In this Section an adaptive control scheme for the tracking problem of UVMS is discussed, which is based on the approach in [325]. Differently from previously proposed schemes, the serial-chain structure ofthe UVMS
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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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Page 159 and 160: e.e. position error [m] 0.02 0.01 0
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Page 163 and 164: 7.2 Feedforward Decoupling Control
Page 165 and 166: 7.2 Feedforward Decoupling Control
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Page 169 and 170: 7.5 Non-regressor-Based Adaptive Co
Page 171 and 172: 7.6 Sliding Mode Control 7.6 Slidin
Page 173 and 174: 7.6 Sliding Mode Control 153 u = B
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Page 177 and 178: 7.7 Adaptive Control 157 of gravity
Page 179 and 180: Let us consider the scalar function
Page 181 and 182: 7.7 Adaptive Control 161 The vehicl
Page 183 and 184: [deg] 3 2.5 2 1.5 1 0.5 7.8 Output
Page 185 and 186: 7.8 Output Feedback Control 165 It
Page 187 and 188: 7.8 Output Feedback Control 167 whe
Page 189 and 190: 7.8 Output Feedback Control 169 C T
Page 191 and 192: 7.8 Output Feedback Control 171 wit
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Page 195 and 196: [m] [-] [deg] x 10−3 10 8 6 4 2 0
Page 197 and 198: [Nm] [Nm] [Nm] 500 0 −500 50 0
Page 199: [m] [-] [deg] x 10−3 10 8 6 4 2 0
Page 203 and 204: [Nm] [Nm] [Nm] 50 0 −50 350 300 2
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Page 217 and 218: e.e. orientation error [deg] 2 1 0
Page 219 and 220: vehicle position [m] 0.1 0.05 0 −
Page 221 and 222: 8. Interaction Control of UVMSs 8.1
Page 223 and 224: 8.3 Impedance Control 203 8.2 Dexte
Page 225 and 226: 8.4 External Force Control 205 The
Page 227 and 228: 8.4 External Force Control 207 be f
Page 229 and 230: 8.4 External Force Control 209 that
Page 231 and 232: 8.4 External Force Control 211 UVMS
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Page 237 and 238: 8.5 Explicit Force Control 217 wher
Page 239 and 240: 8.5 Explicit Force Control 219 wher
Page 241 and 242: [m] [deg] 0.2 0.1 0 −0.1 8.5 Expl
Page 243 and 244: [m] [deg] 0.2 0.1 0 −0.1 −0.2 0
Page 245 and 246: 226 9. Coordinated Control of Plato
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232 9. Coordinated Control of Plato
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238 10. Concluding Remarks control
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240 A. Mathematical models ⎡ 6 .
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242 A. Mathematical models using th
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244 A. Mathematical models L = 2 m
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References 1. Alekseev Y.K., Kosten
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References 249 28. Antonelli G.and
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References 251 62. Caccia M., Indiv
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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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