Abstract
The underwater robot based on visual guidance is disturbed by ocean currents, wind waves and other disturbances in the process of moving to the target position, which makes its motion nonlinear and uncertain. In this paper, an active disturbance rejection controller (ADRC) based on nonlinear function was proposed, which utilize the engineering practice concept of “large error, small gain” and “small error, large gain”, to improve the motion control of visual-guided underwater robot. Firstly, the control model of the underwater robot is established. Next, an extended state observer based on the nfal function is designed and its convergence is verified by Lyapunov stability theory. Finally, the anti-disturbance comparison experiment of the proposed ADRC was carried out on the simulation platform, and the controller was applied to the specific practice. The experimental results show that the controller can eliminate the influence of external factors faster and has better anti-interference ability.
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References
Lapierre, L.: Robust diving control of an AUV. Ocean Eng. 36(1), 92–104 (2009)
Gonzalez-Vazquez, S., Moreno-Valenzuela, J.: A new nonlinear PI/PID controller for quadrotor posture regulation. Electronics, Robotics and Automotive Mechanics Conf. Morelos: IEEE, 642–647 (2010)
Divine, M., Ahmed, C., et al.: Adaptive depth and pitch control of an underwater vehicle with real-time experiments. Ocean Engineering (2015)
Zheng, E.H., Xiong, J.J., Luo, J.L.: Second order sliding mode control for a quadrotor UAV. Isa Transactions 53(4), 1350–1356 (2014)
Chen, Y., Zhang, R., Zhao, X., et al.: Adaptive fuzzy inverse trajectory tracking control of underactuated underwater vehicle with uncertainties. Ocean Engineering 121(JUL.15), 123–133 (2016)
Wang, Y., Zhang, M., Wilson, P.A., et al.: Adaptive neural network-based backstepping fault tolerant control for underwater vehicles with thruster fault. Ocean Engineering 110(DEC.1PT.A), 15–24 (2015)
Han, J.Q.: Active disturbance rejection controller and its application. Control and Decision 13(1), 19–23 (1998)
Orozco-Soto, S.M., Ibarra-Zannatha, J.M., Malo-Tamayo, A.J., et al.: Active disturbance rejection control for UAV hover using ROS. In: 2018 XX Congreso Mexicano de Robótica (COMRob), pp. 1–5. IEEE (2018)
Shen, Y., Shao, K., Ren, W., et al.: Diving control of autonomous underwater vehicle based on improved active disturbance rejection control approach. Neurocomputing 173(JAN.15PT.3), 1377–1385 (2016)
Yan, Z., Liu, Y., Zhou, J., et al.: Path following control of an AUV under the current using the SVR-ADRC. J. Appl. Math. (2014-3-13), 1–12 (2014)
Chen, T., Gao, H., Xu, D., et al.: Trajectory-keeping control of AUV based on RNM-ADRC method under current disturbances for terrain survey mission. Lecture Notes in Computer Science (2017)
Zca, B., Bq, A., Ms, A., et al.: Q-Learning-based parameters adaptive algorithm for active disturbance rejection control and its application to ship course control. Neurocomputing 408, 51–63 (2020)
Li, H., He, B., Yin, Q., et al.: Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control. Electronics 8(6), 608(2019)
Xiong, S., Wang, W., Liu, X., et al.: A novel extended state observer. ISA Trans. 58, 309–317 (2015)
Lozgachev, G.I.: On a method of construction of Lyapunov functions. Autom. Remote. Control. 10(10), 101–111 (1998)
Acknowledgements
This research was supported by the National Natural Science Foundation of China (Nos. 62176149, 61673252, and 51975344).
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Hua, Z., Ruan, H., Tu, D., Zhang, X., Zhang, K. (2023). Research on Motion Control of Underwater Robot Based on Improved Active Disturbance Rejection Control. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14274. Springer, Singapore. https://doi.org/10.1007/978-981-99-6501-4_6
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DOI: https://doi.org/10.1007/978-981-99-6501-4_6
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