Trajectory tracking for an articulated intervention AUV using a super-twisting algorithm in 6 DOF

Borlaug, Ida-Louise Garmann; Pettersen, Kristin Y.; Gravdahl, Jan Tommy

doi:10.1016/j.ifacol.2018.09.506

Cited by 28 publications

(10 citation statements)

References 22 publications

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“…The numerical tests for the 2-DOF pendulum and the 16-DOF snake [16] yielded the same result as for the 6-DOF UR5. Hence, one can assume that the numerical accuracy is independent of the number of DOF.…”

Section: A Numerical Resultsmentioning

confidence: 54%

Robot Dynamics with URDF & CasADi

Johannessen

Arbo

Gravdahl

2019

2019 7th International Conference on Control, Mechatronics and Automation (ICCMA)

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Fast, accurate evaluation of the dynamics parameters is a key ingredient for accurate control, estimation, and simulation of robots. As these are time-consuming to compute by hand, a software library for generating the rigid body dynamics symbolically can be of great use for robotics researchers. In this paper, we propose a library to efficiently compute and evaluate robot dynamics and its derivatives. Based on a URDF description of the robot's kinematics, three major rigid body dynamics algorithms are used to retrieve the dynamics symbolically in the CasADi framework. To validate the numerical accuracy, the numerical evaluation of the solutions are compared against three other well-established rigid body dynamics libraries, namely RBDL, KDL, and PyBullet. We conduct a timing comparison between the libraries, and we show that the evaluation times of the symbolic expressions are at most one order of magnitude higher than the evaluation times of the numerical libraries. Last, it is shown that the evaluation times of the dynamics derivatives remain of the same order as the evaluation times of the dynamics expressions.

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Section: A Numerical Resultsmentioning

confidence: 54%

Robot Dynamics with URDF & CasADi

Johannessen

Arbo

Gravdahl

2019

2019 7th International Conference on Control, Mechatronics and Automation (ICCMA)

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“…Those authors introduced a novel switching term to replace the conventional switching term for sliding mode control. Several other works employed the sliding mode control approach for an AUV such as in [14], [15]. A recent sliding mode control approach for improved stability and convergence speed in the presence of chattering effect using time delay estimation and control is implemented in real-time for similar applications [16], [17].…”

Section: Figurementioning

confidence: 99%

Underactuated Coupled Nonlinear Adaptive Control Synthesis Using U-Model for Multivariable Unmanned Marine Robotics

et al. 2020

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This paper presents the control modelling and synthesis using a coupled multivariable under-actuated nonlinear adaptive U-model approach for an unmanned marine robotic platform. A nonlinear marine robotics model based on the dynamic equation using the Newtonian method and derivation with respect to the kinematics equations and rigid-body mass matrixes are explained. This nonlinear marine robotics model represents the underwater thruster dynamics, marine robotics dynamics and kinematics related to the earth-fixed frame. Coupled multivariable nonlinear adaptive control synthesis using a U-model approach for the Remotely Operated Vehicle (ROV) and Unmanned Surface Vessel (USV) represent an unmanned marine robotics application. A comparison is presented for the proposed nonlinear control approach between the U-model control approach with nonlinear Fuzzy Logic Control and Sliding Mode Control for the ROV and USV platforms. The results show minimum mean square error values and tracking performance between the plant or system model with the proposed method. Lastly, robustness and stability analysis for the proposed U-Model nonlinear control approach are presented by implementing an adaptive learning rate value.

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“…We remark that the optimization problems are formulated in terms of the thruster and joint torque control inputs u, and not the commanded forces and torques τ = Bu. Consequently, the proposed framework also solves the control allocation problem, which had to be solved separately in previous works (Sverdrup-Thygeson et al, 2018;Borlaug et al, 2019). By unifying redundancy resolution, dynamic control and control allocation, strict priority among tasks can always be ensured.…”

Section: Simulationsmentioning

confidence: 99%