Underwater Robots - Gianluca Antonelli.pdf

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142 7. Dynamic Control of UVMSs In [105, 106] some dynamic considerations are given to underline the existence ofadynamic coupling between vehicle and manipulator. From this analysis, based onaspecific structure ofanUVMS, aSliding Mode approach with afeedforward compensation term is presented. Numerical simulation results show that the knowledge ofthe dynamics allows improvement ofthe tracking performance. In [86, 87], during the manipulation motion, the vehicle is assumed tobe inactive and modeled as apassive joint. Arobust controller, with adisturbance observer-based action is used and its effectiveness verified ina1-DOF vehicle carrying a3-DOF manipulator. Reference [186] presents asliding mode controller that benefits from the compensation of a multilayer neural network. Simulation with a 2-DOF, ground-fixed, underwater manipulator isprovided. In [131, 245] the possibility to mount a force/torque sensor at the base of the manipulator isconsidered inorder to compensate for the vehicle/manipulator dynamic interaction. In case of absence of the sensor, [245] also proposes adisturbance observer. The possibility touse information coming from another sensor is interesting; however, the practical implementation of such algorithms is not trivial. Reference [169] presents an iterative learning control experimentally validated ona3-DOF manipulator with fixed base. This is first moved inair and then in water in order to learn the hydrodynamic dynamic contribution and then use it in afeedforward compensation. Reference [174], after having reported some interesting dynamic considerations about the interaction between the vehicle and the manipulator, propose atwo-time scale control. The vehicle, characterized bylow bandwidth actuators that can not compensate for the high manipulator bandwidth, is controlled by asimple P-type action, while the manipulator iscontrolled by afeedback linearizing controller. In [162] the model of aplanar motion ofthe AUV Twin-Burger equipped with a2-link manipulator isdeveloped. AResolved Acceleration Control is then applied and simulated on the 5-DOFs. Reference [281] proposes an adaptiveaction mainly based on the transpose of the Jacobian. The approach is validated on simulations involving a2-DOF model of ODIN carrying a2-DOF planar manipulator. Aproblem slightly different isapproached in [200], where the 4-DOF manipulator Sherpa ,mounted under the ROV Victor 6000 developed at the Ifremer, is considered. This manipulator has been originally deigned to be controlled in open-loop by aremote operator via amaster/slave configuration using ajoystick: it is, thus, not-provided with proprioceptive sensors. The Authors propose closed-loop system based on an eye-to-hand visual servoing approach to control its displacement.
7.2 Feedforward Decoupling Control 7.2 Feedforward Decoupling Control 143 In 1996 T. McLain, S.Rock and S. Lee, in [205, 206], present acontrol law for UVMSs with some interesting experimental results conducted at the Monterey Bay Aquarium Research Institute (MBARI). A1-link manipulator is mounted on the vehicle OTTER (see Figure 7.1) controlled in all the 6- DOFs by mean of 8thrusters. Acoordinated control is then implemented to improve the tracking error of the end effector. Fig. 7.1. OTTER vehicle developed in the Aerospace Robotics Laboratory at Stanford University (courtesy of T. McLain, Brigham Young University) In order to explain the control implemented let rewrite the equations of motion for the sole vehicle in matrix form as M v ˙ν + C v ( ν ) ν + D RB( ν ) ν + g RB( R I B )=τ v − τ m ( R I B , q , ζ , ˙ ζ )(7.1) where τ m ( R I B , q , ζ , ˙ ζ ) ∈ IR 6 represents the coupling effect caused bythe presence of the manipulator. The control ofthe vehicle and ofthe arm isachieved, independently, by classical control technique. The coordination action
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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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Page 111 and 112: 90 4. Fault Detection/Tolerance Str
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Page 115 and 116: 5.3 Experiments of Dynamic Control
Page 117 and 118: [m] [m] 5 4 3 2 1 0 0 100 200 300 4
Page 119 and 120: 5.3 Experiments of Dynamic Control
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Page 123 and 124: [deg] 120 100 80 60 40 20 0 −20 5
Page 125 and 126: 6. Kinematic Control of UVMSs “.
Page 127 and 128: 6.2 Kinematic Control 107 UVMS. Thi
Page 129 and 130: 6.2 Kinematic Control 109 singulari
Page 131 and 132: 6.2 Kinematic Control 111 case of t
Page 133 and 134: 6.5 Singularity-Robust Task Priorit
Page 141 and 142: 6.6 Fuzzy Inverse Kinematics 121 It
Page 143 and 144: Simulations 6.6 Fuzzy Inverse Kinem
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Page 147 and 148: vehicle attitude [deg] 20 10 0 −1
Page 149 and 150: 6.6 Fuzzy Inverse Kinematics 129 It
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Page 155 and 156: joint positions 1-3 [deg] joint pos
Page 159 and 160: e.e. position error [m] 0.02 0.01 0
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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
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Page 181 and 182: 7.7 Adaptive Control 161 The vehicl
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Page 185 and 186: 7.8 Output Feedback Control 165 It
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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
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Page 201 and 202: [N] [N] [N] 20 0 −20 40 20 0 −2
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7.9 Virtual Decomposition Based Con
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joint torques [Nm] 200 0 −200 −
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e.e. orientation error [deg] 2 1 0
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vehicle position [m] 0.1 0.05 0 −
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8. Interaction Control of UVMSs 8.1
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8.3 Impedance Control 203 8.2 Dexte
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8.4 External Force Control 205 The
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8.4 External Force Control 207 be f
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8.4 External Force Control 209 that
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8.4 External Force Control 211 UVMS
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[N] [m] 350 300 250 200 150 100 50
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[deg] [deg] 10 5 0 −5 −10 50 40
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8.5 Explicit Force Control 217 wher
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8.5 Explicit Force Control 219 wher
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[m] [deg] 0.2 0.1 0 −0.1 8.5 Expl
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[m] [deg] 0.2 0.1 0 −0.1 −0.2 0
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226 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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