Underwater Robots - Gianluca Antonelli.pdf

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4.4 Fault Tolerant Schemes 87 basedand it handlesthe thrusterredundancy by apseudo-inverse approachof the TCM that guarantees the minimization of the actuator quadratic norm. The thruster dynamics, with the model described in [147], is also taken into account. The work [306, 307] presents afault detection-tolerant scheme for sensor faults. In detail, the depth of the vehicle is measured by using apressure sensor and abottom sonar sensor. Athird, virtual, sensor isadded: this is basically anARX (AutoRegressive eXogeneous) model of the vehicle depth dynamics. Bycomparing the measured values with the predicted ones, the residual is calculated and the failed sensor eventually disconnected for the remaining portion of the mission. Asketch ofthe scheme isshown in Figure 4.6, in nominal working conditions, the 3residual R i are close to the null value. It is worth noticing that this approach requires exact knowledge of the ocean depth. bottom knowledge u vertical sonar pressure sensor analytical model − + Fig. 4.6. Fault tolerance strategy for sensor fault proposed by K.C. Yang, Y. Yuh and S.K. Choi In [228], the case of an underactuated AUV controlled by control surfaces is considered. The vehicle tries to move in the unactuated DOFs by using elementary motions inthe actuated DOFs. The method has been tested on the vehicle ARCS and showed that, in this form, it is not applicable. This study provides information on structural changes to be adopted inthe vehicle in order to develop avehicle suitable for implementing this method. One of the first works of reconfiguration control for AUVs is given in [283], where, however, only asuperficial description of apossible fault tolerant strategy is provided. Recently, [288] proposed areconfiguration strategy to accommodate actuator faults: this is based onamixed H 2 /H∞ problem. Simulation results are provided. + − − + R 1 R 3 R 2 FD
88 4. Fault Detection/Tolerance Strategies for AUVs and ROVs 4.5 Experiments Roby 2isaROV developed at the National Research Council-ISSIA, Italy. It has been object of several wet tests aiming at validating fault detection approaches. The horizontal motion isobtained bythe use of two fore thrusters that control the surge velocity aswell as the vehicle heading; the depth is regulated by means of two vertical thrusters. In[3, 4, 5] experiments of different fault detection schemes have been carried out by causing, on purpose, an actuator failure: one ofthe thrusters has been simply turned off. The experiments in [3, 4] have been carried out in apool, in [5] the experiments also concern acomparison between EKFs and sliding-mode observers. The latter is aresult of abilateral project with the Naval Postgraduate School, Monterey, CA. The Italian National Research Council (CNR-ISSIA) also developed the ROV ROMEO and tested, in an antarctic mission, both fault detection and tolerant schemes [52, 61]. In particular the case of flooded and blocked thrusters occurred. Inboth cases the fault has been detected and the information could be reported tothe human operator in order to activate the reconfiguration procedure. Figure 4.7 shows the expected and measured motor currents in case of flooded thruster: itcan be observed apersistent mismatching between the output ofthe model and the measured values. The vehicle Theseus manufactured by ISE Research Ltd with the Canadian Department ofNational Defence successfully handled afailure during an Arctic mission of cable laying [117, 118]. In details, the vehicle did not terminate ahoming step, probably due to poor acoustic conditions and the Fault Manager activated asafe behavior: stop under the ice and wait for further instructions. This allowed tore-establish acoustic telemetry and surface tracking and safely recover the vehicle. Notice that his fault wasn’t intentionally caused [118]. The vehicle ODIN, anAUV developed at the Autonomous Systems Laboratory (ASL) of the University ofHawaii, HI, USA, has been used for several experiments. In [306, 307] the fault detection and tolerant schemes are experimentally validated. The thruster fault hasbeen tested by zeroing the output voltagebymeans of software, the fault detection schemeidentified the trouble and correctly reconfigured the force allocation byproperly modifying of the TCM. The fault tolerant scheme with respect to depth sensor fault has also been tested by zeroing the sensor reading and verifying that the algorithm, after aprogrammed time of 1s,correctly switched on the other sensor. While the theory has been developed for a6-DOFs vehicle, the experiments results only present the vehicle depth. The same vehicle has been used to validate the fault tolerant approach developed in [232, 233, 251] in 6-DOFs experiments. Different experiments have been carried out by zeroing the voltage onone or two thrusters simultaneously that, however, did not cause the vehicle becoming under-actuated. The implemented control law isbased onanidentified reduced ODIN model
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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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Page 59 and 60: y z 2.8 Kinematics of Manipulators
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Page 65 and 66: 2.11 Identification 43 f e = K ( x
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Page 71 and 72: o x z o b z b x b 3.3 Earth-Fixed-F
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Page 75 and 76: o o b y x y b x b 3.5 Model-Based C
Page 77 and 78: 3.6 Mixed Earth/Vehicle-Fixed-Frame
Page 79 and 80: 3.7 Jacobian-Transpose-Based Contro
Page 81 and 82: 3.8 Comparison Among Controllers 59
Page 83 and 84: 3.9 Numerical Comparison Among the
Page 87 and 88: force [N] moment [Nm] 20 10 0 −10
Page 101 and 102: 80 4. Fault Detection/Tolerance Str
Page 107: 86 4. Fault Detection/Tolerance Str
Page 113 and 114: 5. Experiments of Dynamic Control o
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
Page 121 and 122: 5.4 Experiments of Fault Tolerance
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
Page 145 and 146: 6.6 Fuzzy Inverse Kinematics 125 Fi
Page 147 and 148: vehicle attitude [deg] 20 10 0 −1
Page 149 and 150: 6.6 Fuzzy Inverse Kinematics 129 It
Page 151 and 152: [-] 1 0.8 0.6 0.4 0.2 0 close 0 20
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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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7.5 Non-regressor-Based Adaptive Co
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7.6 Sliding Mode Control 7.6 Slidin
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7.6 Sliding Mode Control 153 u = B
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7.6 Sliding Mode Control 155 Practi
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7.7 Adaptive Control 157 of gravity
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Let us consider the scalar function
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7.7 Adaptive Control 161 The vehicl
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[deg] 3 2.5 2 1.5 1 0.5 7.8 Output
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7.8 Output Feedback Control 165 It
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7.8 Output Feedback Control 167 whe
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7.8 Output Feedback Control 169 C T
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7.8 Output Feedback Control 171 wit
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[m] [deg] [deg] 0.1 0.05 0 −0.05
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[m] [-] [deg] x 10−3 10 8 6 4 2 0
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[Nm] [Nm] [Nm] 500 0 −500 50 0
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[m] [-] [deg] x 10−3 10 8 6 4 2 0
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[N] [N] [N] 20 0 −20 40 20 0 −2
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[Nm] [Nm] [Nm] 50 0 −50 350 300 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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