Abstract
This paper presents a novel neural network-based control approach designed for industrial robot manipulators characterized by uncertain closed architectures and unknown dynamics. Industrial and commercial robot manipulators typically employ closed control architectures, which limit the ability to make modifications or comprehend the inner control processes. Users are generally restricted to providing joint position or velocity commands for controlling the manipulator. Furthermore, the integration of these robots with external sensors for modern applications poses challenges to system stability. Our proposed solution utilizes neural networks to approximate the robot’s dynamic model and low-level controller. The proposed controller is introduced as an outer (external feedback) loop, ensuring independence from the inner controller configuration. This outer loop leverages external sensor data and the desired trajectory to calculate commands for joint velocities. Consequently, this approach offers greater design flexibility for modern control applications. Unlike previous studies, our work introduces novelty through unconstrained control actions, avoiding the need for inner controller configuration and control gain structure. To validate our method, we conducted experiments using two industrial manipulators, namely the UR5e and UR10e, and the results clearly demonstrate the superior performance and industrial applicability of the framework we have developed.
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References
Craig JJ, Hsu P, Sastry SS (1987) Adaptive control of mechanical manipulators. Int J Robot Res 6(2):16–28
Slotine J-JE, Li W (1987) On the adaptive control of robot manipulators. Int J Robot Res 6(3):49–59
Middletone R, Goodwin GC (1986) Adaptive computed torque control for rigid link manipulators. In: 1986 25th IEEE conference on decision and control, IEEE, pp 68–73
Ortega R, Spong MW (1989) Adaptive motion control of rigid robots: a tutorial. Automatica 25(6):877–888
Arimoto S (1996) Control theory of non-linear mechanical systems: a passivity-based and circuit-theoretic approach. Oxford Univ. Press, London, U.K
Dixon WE (2007) Adaptive regulation of amplitude limited robot manipulators with uncertain kinematics and dynamics. IEEE Trans Autom Control 52(3):488–493
Astolfi A, Ortega R (2003) Immersion and invariance: a new tool for stabilization and adaptive control of nonlinear systems. IEEE Trans Autom Control 48(4):590–606
Loría A (2015) Observers are unnecessary for output-feedback control of Lagrangian systems. IEEE Trans Autom Control 61(4):905–920
Lange F, Hirzinger G (1999) Adaptive minimization of the maximal path deviations of industrial robots. In: 1999 European control conference (ECC). IEEE, pp 1914–1919
Grotjahn M, Heimann B (2002) Model-based feedforward control in industrial robotics. Int J Robot Res 21(1):45–60
Wang H (2016) Adaptive control of robot manipulators with uncertain kinematics and dynamics. IEEE Trans Autom Control 62(2):948–954
Li Y, Yin Y, Zhang D (2018) Adaptive task-space synchronization control of bilateral teleoperation systems with uncertain parameters and communication delays. IEEE Access 6:5740–5748
Wang H, Ren W, Cheah CC, Xie Y, Lyu S (2020) Dynamic modularity approach to adaptive control of robotic systems with closed architecture. IEEE Trans Autom Control 65(6):2760–2767
Cheah C-C, Wang D (1998) Learning impedance control for robotic manipulators. IEEE Trans Robot Autom 14(3):452–465
Zheng E, Li Y, Wang Q, Qiao H (2019) Toward a human-machine interface based on electrical impedance tomography for robotic manipulator control. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 2768–2774
Abbasi Moshaei A, Mohammadi Moghaddam M, Dehghan Neistanak V (2020) Analytical model of hand phalanges desired trajectory for rehabilitation and design a sliding mode controller based on this model. Modares Mech Eng 20(1):129–137
Abbasimoshaei A, Chinnakkonda Ravi AK, Kern TA (2023) Development of a new control system for a rehabilitation robot using electrical impedance tomography and artificial intelligence. Biomimetics 8(5):420
Cheng L, Hou Z-G, Tan M (2009) Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model. Automatica 45(10):2312–2318
Zhao Y, Cheah CC (2009) Neural network control of multifingered robot hands using visual feedback. IEEE Trans Neural Netw 20(5):758–767
Sanner RM, Slotine J-JE (1995) Stable adaptive control of robot manipulators using “neural" networks. Neural Comput 7(4):753–790
Lewis FL, Liu K, Yesildirek A (1995) Neural net robot controller with guaranteed tracking performance. IEEE Trans Neural Netw 6(3):703–715
Li X, Cheah CC (2013) Adaptive neural network control of robot based on a unified objective bound. IEEE Trans Control Syst Technol 22(3):1032–1043
O’Connell M, Shi G, Shi X, Azizzadenesheli K, Anandkumar A, Yue Y, Chung S-J (2022) Neural-fly enables rapid learning for agile flight in strong winds. Sci Robot 7(66):eabm6597
Chen M, Ma H, Kang Y, Wu Q (2021) Adaptive neural safe tracking control design for a class of uncertain nonlinear systems with output constraints and disturbances. IEEE Trans Cybernet
Kim S-W, Cho B, Shin S, Oh J-H, Hwangbo J, Park H-W (2021) Force control of a hydraulic actuator with a neural network inverse model. IEEE Robot Auto Lett 6(2):2814–2821
Liu Y, Wang Y, Guan X, Wang Y, Jin S, Hu T, Ren W, Hao J, Zhang J, Li G (2022) Multi-terrain velocity control of the spherical robot by online obtaining the uncertainties in the dynamics. IEEE Robot Auto Lett
Gao J, Proctor AA, Shi Y, Bradley C (2015) Hierarchical model predictive image-based visual servoing of underwater vehicles with adaptive neural network dynamic control. IEEE Trans cybernet 46(10):2323–2334
Lyu S, Cheah CC (2020) Data-driven learning for robot control with unknown jacobian. Automatica 120:109120
Khan GD, Nguyen H-T, Cheah CC (2021) A stable control strategy for industrial robots with external feedback loop. In: 2021 IEEE international conference on robotics and Automation (ICRA). IEEE, pp 12833–12838
Craig JJ (2009) Introduction to robotics: mechanics and control, 3/E. Pearson Education India
Cheah CC, Hou SP, Slotine JJE (2009) Region-based shape control for a swarm of robots. Automatica 45(10):2406–2411
Cheah CC, Ta QM, Haghighi R (2016) Grasping and manipulation of a micro-particle using multiple optical traps. Automatica 68:216–227
Slotine J-JE, Li W et al (1991) Applied nonlinear control, no. 1, vol 199. Prentice hall Englewood Cliffs, NJ
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Khan, G.D. Control of robot manipulators with uncertain closed architecture using neural networks. Intel Serv Robotics 17, 315–327 (2024). https://doi.org/10.1007/s11370-023-00507-0
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DOI: https://doi.org/10.1007/s11370-023-00507-0