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Nussbaum Function Based Approach for Tracking Control of Robot Manipulators (2403.00970v1)
Published 1 Mar 2024 in cs.RO, cs.SY, and eess.SY
Abstract: This paper introduces a novel Nussbaum function-based PID control for robotic manipulators. The integration of the Nussbaum function into the PID framework provides a solution with a simple structure that effectively tackles the challenge of unknown control directions. Stability is achieved through a combination of neural network-based estimation and Lyapunov analysis, facilitating automatic gain adjustment without the need for system dynamics. Our approach offers a gain determination with minimum parameter requirements, significantly reducing the complexity and enhancing the efficiency of robotic manipulator control. The paper guarantees that all signals within the closed-loop system remain bounded. Lastly, numerical simulations validate the theoretical framework, confirming the effectiveness of the proposed control strategy in enhancing robotic manipulator control.
- B. M. Yilmaz, E. Tatlicioglu, A. Savran, and M. Alci, “Self-adjusting fuzzy logic based control of robot manipulators in task space,” IEEE Transactions on Industrial Electronics, vol. 69, no. 2, pp. 1620–1629, 2021.
- C. A. Lightcap and S. A. Banks, “An extended kalman filter for real-time estimation and control of a rigid-link flexible-joint manipulator,” IEEE Transactions on Control Systems Technology, vol. 18, no. 1, pp. 91–103, 2009.
- M. Zhu, L. Ye, and X. Ma, “Estimation-based quadratic iterative learning control for trajectory tracking of robotic manipulator with uncertain parameters,” IEEE Access, vol. 8, pp. 43 122–43 133, 2020.
- H. Rahimi Nohooji, A. Zaraki, and H. Voos, “Actor–critic learning based pid control for robotic manipulators,” Applied Soft Computing, vol. 151, p. 111153, 2024.
- I. Cervantes and J. Alvarez-Ramirez, “On the pid tracking control of robot manipulators,” Systems & control letters, vol. 42, no. 1, pp. 37–46, 2001.
- R. P. Borase, D. Maghade, S. Sondkar, and S. Pawar, “A review of pid control, tuning methods and applications,” International Journal of Dynamics and Control, vol. 9, pp. 818–827, 2021.
- S. A. Ajwad, J. Iqbal, M. I. Ullah, and A. Mehmood, “A systematic review of current and emergent manipulator control approaches,” Frontiers of mechanical engineering, vol. 10, pp. 198–210, 2015.
- J. Armendariz, V. Parra-Vega, R. García-Rodríguez, and S. Rosales, “Neuro-fuzzy self-tuning of pid control for semiglobal exponential tracking of robot arms,” Applied Soft Computing, vol. 25, pp. 139–148, 2014.
- A. Belkadi, H. Oulhadj, Y. Touati, S. A. Khan, and B. Daachi, “On the robust pid adaptive controller for exoskeletons: A particle swarm optimization based approach,” Applied Soft Computing, vol. 60, pp. 87–100, 2017.
- M. I. Azeez, A. Abdelhaleem, S. Elnaggar, K. A. Moustafa, and K. R. Atia, “Optimization of pid trajectory tracking controller for a 3-dof robotic manipulator using enhanced artificial bee colony algorithm,” Scientific reports, vol. 13, no. 1, p. 11164, 2023.
- C. Huang and C. B. Yu, “Tuning function design for nonlinear adaptive control systems with multiple unknown control directions,” Automatica, vol. 89, pp. 259–265, 2018.
- A. Scheinker and M. Krstić, “Minimum-seeking for clfs: Universal semiglobally stabilizing feedback under unknown control directions,” IEEE Transactions on Automatic Control, vol. 58, no. 5, pp. 1107–1122, 2012.
- H. E. Psillakis, “Consensus in networks of agents with unknown high-frequency gain signs and switching topology,” IEEE Transactions on Automatic Control, vol. 62, no. 8, pp. 3993–3998, 2016.
- R. D. Nussbaum, “Some remarks on a conjecture in parameter adaptive control,” Systems & control letters, vol. 3, no. 5, pp. 243–246, 1983.
- K. Zhao, C. Wen, Y. Song, and F. L. Lewis, “Adaptive uniform performance control of strict-feedback nonlinear systems with time-varying control gain,” IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 2, pp. 451–461, 2022.
- Y. Song, X. Huang, and C. Wen, “Robust adaptive fault-tolerant pid control of mimo nonlinear systems with unknown control direction,” IEEE Transactions on Industrial Electronics, vol. 64, no. 6, pp. 4876–4884, 2017.
- H. Habibi, H. Rahimi Nohooji, and I. Howard, “Adaptive pid control of wind turbines for power regulation with unknown control direction and actuator faults,” IEEE Access, vol. 6, pp. 37 464–37 479, 2018.
- Habibi, Hamed and Rahimi Nohooji, Hamed and Howard, Ian, “Backstepping nussbaum gain dynamic surface control for a class of input and state constrained systems with actuator faults,” Information Sciences, vol. 482, pp. 27–46, 2019.
- H. Rahimi Nohooji, I. Howard, and L. Cui, “Neural network adaptive control design for robot manipulators under velocity constraints,” Journal of the Franklin Institute, vol. 355, no. 2, pp. 693–713, 2018.
- C. Chen, Z. Liu, Y. Zhang, C. P. Chen, and S. Xie, “Adaptive control of mimo mechanical systems with unknown actuator nonlinearities based on the nussbaum gain approach,” IEEE/CAA Journal of Automatica Sinica, vol. 3, no. 1, pp. 26–34, 2016.
- Chen, Ci, Liu, Zhi, Zhang, Yun, Chen, CL Philip, and Xie, Shengli, “Saturated nussbaum function based approach for robotic systems with unknown actuator dynamics,” IEEE transactions on cybernetics, vol. 46, no. 10, pp. 2311–2322, 2015.
- Y. P. Pane, S. P. Nageshrao, J. Kober, and R. Babuška, “Reinforcement learning based compensation methods for robot manipulators,” Engineering Applications of Artificial Intelligence, vol. 78, pp. 236–247, 2019.
- Q. Chen, Y. Wang, and Y. Song, “Tracking control of self-restructuring systems: a low-complexity neuroadaptive pid approach with guaranteed performance,” IEEE Transactions on Cybernetics, 2021.
- H. Rahimi Nohooji, “Constrained neural adaptive pid control for robot manipulators,” Journal of the Franklin Institute, vol. 357, no. 7, pp. 3907–3923, 2020.