Underwater Robots - Gianluca Antonelli.pdf

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10. Concluding Remarks “Cheshire-cat —Alice began— would you tell me, please, which way Iought to go from here?”. “That depends agood deal on where you want to get to”, said the cheshire-cat. Lewis Carroll, “Alice’s adventures in wonderland”. The underwater environment ishostile, the communication is difficult and external disturbances can strongly affect the dynamics of the system involved in the mission. Moreover, the sensing devices are not fully reliable in the underwater environment. This is particularly true for the positioning sensors. The vehicle actuating systems, usually composed of thrusters, is highly nonlinear and subject tolimit cycles. Some very simple operations for aground robotic system, such as to hold its position can become difficult for UVMSs. This difficulty dramatically increases for complex missions. Moreover, also ifone wants toneglect the technical difficulties and focus on the mathematical model he deals with characteristics difficult tohandle. The equations of motion are coupled and nonlinear; the hydrodynamic effects are not completely known leading to the practical impossibility toidentify the dynamic parameters. Together with the complexity ofthe mathematical model this is aserious obstacle versus the implementation of model-based control laws. In this framework, the control of the UVMS must be robust ,inawide sense, reliable and of simple implementation. With this in mind the techniques for motion and force control ofUVMSs needs to be developed. The kinematic control approach is important for accomplishing anautonomous robotic mission. Differently from industrial robotics, whereanoff-line trajectory planning is apossible approach, in case of unknown, unstructured environment, the trajectory has tobebegenerated in real-time. The possible occurrence of kinematic singularities, thus, is not avoided with a task space control law. Moreover, UVMSs are usually redundant with respect to the given task; akinematic control approach, thus, can exploit such aredundancy. The motion control of UVMSs must deal with the strong constraints discussed along the book. In this work, different approaches were discussed. Control ofanunderwater robotic system can not be reduced at the sole translation of similar approaches designed for ground-fixed manipulator; as an example,itisinterestingtostress the results achieved in Chapter 3: certain G. Antonelli: Underwater Robots, 2nd Edition, STAR 2, pp. 237–238, 2006. © Springer-Verlag Berlin Heidelberg 2006
238 10. Concluding Remarks control laws devoted atthe study of 6-DOFs dynamic control of the vehicle can deteriorate the transient due to awrong adaptive or integral action. In the Author’s opinion, this result is particularly significant. The following step needs tobethe experimental validation of the several control laws developed for UVMSs and tested only in simulation. Nec plus ultra.
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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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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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Page 245 and 246: 226 9. Coordinated Control of Plato
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Page 259 and 260: 240 A. Mathematical models ⎡ 6 .
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Page 265 and 266: References 1. Alekseev Y.K., Kosten
Page 267 and 268: References 249 28. Antonelli G.and
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Page 275 and 276: References 257 168. Kato N. and Lan
Page 277 and 278: References 259 mera. In: IEEE Inter
Page 279 and 280: References 261 235. Podder T.K. and
Page 281 and 282: References 263 273. Solvang B., Den
Page 283: References 265 310. Yoerger D.R., S
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