Abstract
This paper deals with a motion control scheme for underwater robots equipped with manipulators. In general, for each robot, its manipulator is directly driven by electric motors with position sensors such as encoders, whereas its robot body is propelled by marine thrusters with position sensors such as inertial measurement units. It has been pointed out in the literature that for this type of underwater robots, its robot body control is more challenging than its manipulator control, because the robot body has much larger inertia, and many more inaccurate position sensors and actuators than the manipulator. Therefore, it may be practically difficult to accurately control the motion of the robot body, even if an advanced control scheme with good performance is implemented in the controller of the robot body. In this paper, we develop a model-based motion controller for the manipulator under the condition that the robot body is independently controlled by a motion controller with poor performance. Its features are to design the manipulator controller in consideration of the dynamics of the robot body including the marine thrusters as well as those of the manipulator, and to be applicable to underwater robots equipped with one of the three types of servo systems (i.e., a voltage-controlled, a torque-controlled, and a velocity-controlled servo system). Some stability properties of the control system were theoretically ensured in the stability analysis. Furthermore, these results were supported by those obtained in numerical simulations.
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Antonelli G (2003) Underwater robots: motion and force control of vehicle-manipulator systems. Springer, Berlin
Canudas de Wit C, Olguin Diaz E, Perrier M (2000) Nonlinear control of an underwater vehicle/manipulator with composite dynamics. IEEE Trans Control Syst Technol 8(6):948–960
Sarkar N, Podder TK (2001) Coordinated motion planning and control of autonomous underwater vehicle-manipulator systems subject to drag optimization. IEEE J Ocean Eng 26(2):228–239
Yatoh T, Sagara S, Tamura M (2008) Digital type disturbance compensation control of a floating underwater robot with 2 link manipulator. Artif Life Robot 13(1):377–381
Han J, Park J, Chung WK (2011) Robust coordinated motion control of an underwater vehicle-manipulator system with minimizing restoring moments. Ocean Eng 38(10):1197–1206
Santhakumar M, Kim J (2012) Indirect adaptive control of an autonomous underwater vehicle-manipulator system for underwater manipulation tasks. Ocean Eng 54:233–243
Xu B, Pandian SR, Sakagami N, Petry F (2012) Neuro-fuzzy control of underwater vehicle-manipulator systems. J Franklin Inst 349(3):1125–1138
Taira Y, Oya M, Sagara S (2012) Adaptive control of underwater vehicle-manipulator systems using radial basis function networks. Artif Life Robot 17(1):123–129
Han J, Chung WK (2014) Active use of restoring moments for motion control of an underwater vehicle-manipulator system. IEEE J Ocean Eng 39(1):100–109
Esfahani HN, Azimirad V, Danesh M (2015) A time delay controller included terminal sliding mode and fuzzy gain tuning for underwater vehicle-manipulator systems. Ocean Eng 107:97–107
Santhakumar M, Kim J (2015) Coordinated motion control in task space of an autonomous underwater vehicle-manipulator system. Ocean Eng 104:155–167
Simetti E, Casalino G (2015) Whole body control of a dual arm underwater vehicle manipulator system. Ann Rev Control 40:191–200
Taira Y, Sagara S, Oya M (2015) Robust controller with a fixed compensator for underwater vehicle-manipulator systems including thruster dynamics. Int J Adv Mechatron Syst 6(6):258–268
Korkmaz O, Ider SK, Ozgoren MK (2016) Trajectory tracking control of an underactuated underwater vehicle redundant manipulator system. Asian J Control 18(5):1593–1607
Londhe PS, Mohan S, Patre BM, Waghmare LM (2017) Robust task-space control of an autonomous underwater vehicle-manipulator system by PID-like fuzzy control scheme with disturbance estimator. Ocean Eng 139:1–13
Haugalokken BOA, Jorgensen EK, Schjolberg I (2018) Experimental validation of end-effector stabilization for underwater vehicle-manipulator systems in subsea operations. Robot Auton Syst 109:1–12
Choi HS (1996) Modeling of robot manipulators working under the sea and the design of a robust controller. Robotica 14(2):213–218
Lee M, Choi HS (2000) A robust neural controller for underwater robot manipulators. IEEE Trans Neural Netw 11(6):1465–1470
Yuh J, Zhao S, Lee PM (2001) Application of adaptive disturbance observer control to an underwater manipulator. In: Proceedings of the 2001 IEEE international conference on robotics and automation, pp 3244–3249
Kim J, Chung WK, Yuh J (2003) Dynamic analysis and two-time scale control for underwater vehicle-manipulator systems. In: Proceedings of the 2003 IEEE/RSJ international conference on intelligent robots and systems, pp 577–582
Yoerger DR, Cooke JG, Slotine JJE (1990) The influence of thruster dynamics on underwater vehicle behavior and their incorporation into control system design. IEEE J Ocean Eng 15(3):167–178
Taira Y, Sagara S, Oya M (2018) Design of a motion controller for an underwater vehicle-manipulator system with a differently-controlled vehicle. In: Proceedings of the international conference on information and communication technology robotics, Paper No. FA1.2, pp 1–6
Taira Y, Sagara S, Oya M (2019) Design of two types of kinematic control for an underwater vehicle-manipulator system with a differently-controlled vehicle. In: Proceedings of the 24th international symposium on artificial life and robotics, pp 1015–1020
Craig JJ (2005) Introduction to robotics: mechanics and control, 3rd edn. Prentice Hall, Upper Saddle River
Arimoto S (1996) Control theory of nonlinear-mechanical systems: a passivity-based and circuit-theoretic approach. Oxford University Press, New York
Canudas de Wit C, Siciliano B, Bastin G (eds) (1996) Theory of robot control. Springer, London
Horn RA, Johnson CR (2013) Matrix analysis, 2nd edn. Cambridge University Press, New York
Krstic M, Kanellakopoulos I, Kokotovic P (1995) Nonlinear and adaptive control design. Wiley, New York
Ioanou PA, Sun J (1996) Robust adaptive control. Prentice Hall, Upper Saddle River
Taia Y, Sagara S, Oya M (2018) Motion and force control with a nonlinear force error filter for underwater vehicle-manipulator systems. Artif Life Robot 23(1):103–117
Kim J, Chung WK (2006) Accurate and practical thruster modeling for underwater vehicle. Ocean Eng 33(5–6):566–586
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This work was presented in part at the 24th International Symposium on Artificial Life and Robotics Beppu, Oita, January 23–25, 2019.
Appendices
Appendix 1: Derivation of (5)
For simplicity, we express \(\exp (\cdot )\) as \({e}^{(\cdot )}\) in this appendix. Substituting the second equation of (3) into the time derivative of the first equation of (3), we have
In the derivation of (90), we use the differential relation \(d\{ D(v)v(t)\} /dt =2D(v)\dot{v}(t)\). Substituting (90) into the time derivative of (2), we obtain
Replacing the symbol t by the symbol \(\bar{\tau }\) in (91), then multiplying both its sides by \({e}^{-\rho (t-\bar{\tau })}\), and then integrating both its sides from 0 to t with respect to \(\bar{\tau }\), we have
It should be noted that this operation is equivalent to applying the stable first-order filter \(1/(s+\rho )[\, \cdot \,]\) to (91). Applying the integration by parts to the first equation of (93), we have
Reapplying the integration by parts to (94), we obtain
In a way similar to the derivation of (95), we can rewrite the second equation of (93) as
Applying the integration by parts to the third equation of (93), we have
Substituting (93) (the fourth equation) and (95) to (97) into (92), and then considering the fact that the first-order filters (7) have the solution
we obtain the model (5). In this derivation, we use (6).
Appendix 2: Derivations of (21) and (23)
For simplicity, we express \(\exp (\cdot )\) as \({e}^{(\cdot )}\) in this appendix. Using the first equation of (6), we can write the norm of \(z_{C}(t)\) as
In the derivation of (99), we utilize (17) (the first and second inequalities), (19) (the first inequality), and the inequalities \(\Vert \dot{q}(t) \Vert \le \Vert \dot{\varphi }(t) \Vert\) and \(\Vert \dot{x}_{V}(t) \Vert \le \Vert \dot{\varphi }(t) \Vert\). Using the solution of the third differential equation of (7) [i.e., the third equation of (98)], we can write the norm of \(z_{VF}(t)\) as
where \(\bar{c}_{VF1} \in R^{+}\) is given by
In the derivation of (100), we utilize (17) (the first, second, fourth, and fifth inequalities), (19) (the first inequality), (20), and the inequalities \(\Vert \dot{x}_{V}(t) \Vert \le \Vert \dot{\varphi }(t) \Vert\) and \(\Vert \dot{q}(t) \Vert \le \Vert \dot{\varphi }(t) \Vert\). In view of the fact that the time integral in (100) is equivalent to the solution of the first-order differential equation (22), we can rewrite the inequality (100) as
Applying (102) to (99), we have
Furthermore, applying (103) to the norm of \(z_{Q}(t)\) [given by the second equation of (12)], we obtain
and we can directly obtain (21) from (104). In the derivation of (104), we utilize (17) (the second and seventh inequalities) and (19) (the second inequality).
In view of the condition that the disturbances \(d_{V}(t)\) and \(d_{M}(t)\) are bounded, we obtain the inequalities
where \(c_{D1}\) and \(c_{D2}\) are positive constants. Using the second equation of (6), we can write the norm of \(w_{C}(t)\) as
In the derivation of (106), we utilize the first inequality of (105). Using the solution of the first differential equation of (7) [i.e., the first equation of (98)], we can write the norm of \(w_{VF1}(t)\) as
Similarly, using the solution of the second differential equation of (7) [i.e., the second equation of (98)], we can write the norm of \(w_{VF2}(t)\) as
In the derivation of (108), we utilize the first inequality of (105). Applying (107) and (108) to (106), we obtain
Furthermore, using the third inequality of (12), we can write the norm of \(w_{Q}(t)\) as
and we can directly obtain (23) from (110). In the derivation of (110), we utilize (17) (the second and seventh inequalities), (105) (the second inequality), and (109).
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Taira, Y., Sagara, S. & Oya, M. Model-based motion control for underwater vehicle-manipulator systems with one of the three types of servo subsystems. Artif Life Robotics 25, 133–148 (2020). https://doi.org/10.1007/s10015-019-00564-8
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DOI: https://doi.org/10.1007/s10015-019-00564-8