Underwater Robots - Gianluca Antonelli.pdf

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176 7. Dynamic Control of UVMSs [N] [N] [N] 20 10 0 −10 −20 X 10 0 X −30 0 10 20 30 40 50 60 40 20 0 −20 −40 0 10 20 30 40 50 60 100 0 −100 time [s] time [s] −200 0 10 20 30 40 50 60 time [s] [N] [N] [N] 20 −10 −20 Y Y −30 0 10 20 30 40 50 60 40 20 0 −20 −40 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. 7.14. Output feedback control; first case study. Comparison between the control laws (7.41),(7.42) and (7.62): vehicle control forces. Left: control law (7.41),(7.42). Right: control law (7.62) trol structure; also, the control inputs remain free of chattering phenomena and are not reported for brevity. The tracking performance obtained with the simplified control law confirms thatthe controller-observer approachisintrinsicallyrobust with respect to uncertain knowledge of the system’s dynamics, thanks to the exponential stability property. Hence, perfect compensation of inertia, Coriolis and centripetal terms, as well as of hydrodynamic damping terms, is not required. Third case study. The control laws (7.43),(7.44) and (7.63) have been tested in more severe operating conditions. Namely, the update rate for the vehicle position/orientation measurements has been lowered to 5Hzand the A/D word length has been set to 12 bit for all the sensor output signals. Gaussian zero-mean noise is still added to the measures. The parameters in the control laws are the same as in the previous case studies. Figure 7.18 shows asmall degradation ofthe tracking performance for both the control schemes. In fact, the tracking errors remain of the same order of magnitude as in the previous case studies, because the computing rate of the control law isunchanged (100 Hz). Namely, the update rate of
[Nm] [Nm] [Nm] 500 0 −500 50 0 −50 −100 0 10 20 30 40 50 60 −150 0 10 20 30 40 50 60 100 50 0 −50 −100 time [s] time [s] 0 10 20 30 40 50 60 time [s] [Nm] [Nm] [Nm] 7.8 Output Feedback Control 177 500 K K 0 −500 M M 50 0 −50 −100 0 10 20 30 40 50 60 −150 0 10 20 30 40 50 60 100 N N 50 0 −50 −100 time [s] time [s] 0 10 20 30 40 50 60 time [s] Fig. 7.15. Output feedback control; first case study. Comparison between the control laws (7.41),(7.42) and (7.62): vehicle control moments. Left: control law (7.41),(7.42); Right: control law (7.62) the measurements relative tothe subsystem with faster dynamics (i.e., the manipulator) remains the same (100Hz), while the update rate of the measurements relative tothe vehicle (5 Hz) isstill adequate to its slower dynamics. Figures 7.19 to7.21 show the corresponding control forces, moments and torques. It can be recognized that unacceptable chattering onthe control inputs is experienced when numerical derivatives are used, which isalmost completely cancelled when the controller-observer scheme is adopted.
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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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Page 147 and 148: vehicle attitude [deg] 20 10 0 −1
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Page 161 and 162: 7. Dynamic Control of UVMSs 7.1 Int
Page 163 and 164: 7.2 Feedforward Decoupling Control
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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: [m] [-] [deg] x 10−3 10 8 6 4 2 0
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Page 201 and 202: [N] [N] [N] 20 0 −20 40 20 0 −2
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Page 205 and 206: 7.9 Virtual Decomposition Based Con
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Page 221 and 222: 8. Interaction Control of UVMSs 8.1
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Page 227 and 228: 8.4 External Force Control 207 be f
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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
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Page 245 and 246: 226 9. Coordinated Control of Plato
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228 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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