Motion control and path optimization of intelligent AUV using fuzzy adaptive PID and improved genetic algorithm

Yong Xiong; Lin Pan; Min Xiao; Han Xiao; Yong Xiong; Lin Pan; Min Xiao; Han Xiao

doi:10.3934/mbe.2023404

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 5: 9208-9245. doi: 10.3934/mbe.2023404

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Research article

Motion control and path optimization of intelligent AUV using fuzzy adaptive PID and improved genetic algorithm

1.
School of Navigation, Wuhan University of Technology, Wuhan, 430063, China
2.
School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
3.
Hainan Research Institute, Wuhan University of Technology, Sanya 572000, China
4.
Hebei Huifeng Network Technology Development Co., Ltd, Shijiazhuang 050092, China
5.
Shaoxing Institute of Advanced Research, Wuhan University of Technology, Shaoxing 312300, China
6.
School of Computer Science and Artificial Intelligent, Wuhan University of Technology, Wuhan, 430063, China
Academic editor: Yang Kuang

Received: 08 November 2022 Revised: 31 December 2022 Accepted: 21 February 2023 Published: 14 March 2023

This study discusses the application of fuzzy adaptive PID and improved genetic algorithm (IGA) in motion control and path optimization of autonomous underwater vehicle (AUV). The fuzzy adaptive PID method is selected because it is considered to be a strongly nonlinear and coupled system. First, this study creates the basic coordinate system of the AUV, and then analyzes the spatial force from the AUV to obtain the control model of the heading angle, climb angle, and depth. Next, the knowledge of fuzzy adaptive PID and IGA technology on AVU are investigated, then fuzzy adaptive PID controllers and path optimization are established, and experimental simulations are carried out to compare and analyze the simulation results. The research results show that controllers and IGA can be used for the motion control and path optimization of AUV. The advantages of fuzzy adaptive PID control are less overload, enhanced system stability, and more suitable for motion control and path optimization of AUV.
- autonomous underwater vehicle (AUV),
- fuzzy adaptive PID,
- motion control,
- path optimization,
- improved genetic algorithm (IGA)
Citation: Yong Xiong, Lin Pan, Min Xiao, Han Xiao. Motion control and path optimization of intelligent AUV using fuzzy adaptive PID and improved genetic algorithm[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 9208-9245. doi: 10.3934/mbe.2023404

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Abstract

This study discusses the application of fuzzy adaptive PID and improved genetic algorithm (IGA) in motion control and path optimization of autonomous underwater vehicle (AUV). The fuzzy adaptive PID method is selected because it is considered to be a strongly nonlinear and coupled system. First, this study creates the basic coordinate system of the AUV, and then analyzes the spatial force from the AUV to obtain the control model of the heading angle, climb angle, and depth. Next, the knowledge of fuzzy adaptive PID and IGA technology on AVU are investigated, then fuzzy adaptive PID controllers and path optimization are established, and experimental simulations are carried out to compare and analyze the simulation results. The research results show that controllers and IGA can be used for the motion control and path optimization of AUV. The advantages of fuzzy adaptive PID control are less overload, enhanced system stability, and more suitable for motion control and path optimization of AUV.

References

[1]	K. D. Do, J. Pan, Control of ships and underwater vehicles: Design for underactuated and nonlinear marine systems, Springer, London, 2009.
[2]	S. Wadoo, P. Kachroo, Autonomous underwater vehicles: modeling, control design and simulation, CRC Press, 2017.
[3]	M. G. Joo, Z. Qu, An autonomous underwater vehicle as an underwater glider and its depth control, Int. J. Control Autom. Syst., 13 (2015), 1212–1220. https://doi.org/10.1007/s12555-014-0252-8 doi: 10.1007/s12555-014-0252-8
[4]	E. Kim, S. Fan, N. Bose, H. Nguyen, Current estimation and path following for an autonomous underwater vehicle AUV by using a high-gain observer based on an AUV dynamic model, Int. J. Control Autom. Syst., 19 (2021), 478–490. https://doi.org/10.1007/s12555-019-0673-5 doi: 10.1007/s12555-019-0673-5
[5]	X. Cao, F. Zuo, A fuzzy-based potential field hierarchical reinforcement learning approach for target hunting by multi-AUV in 3-D underwater environments, Int. J. Control, 94 (2021), 1334–1343.
[6]	X. Guo, W. Yan, R. Cui, Neural network-based nonlinear sliding-mode control for an AUV without velocity measurements, Int. J. Control, 92 (2019), 677–692. https://doi.org/10.1080/00207179.2017.1366669 doi: 10.1080/00207179.2017.1366669
[7]	O. Doukhi, D. J. Lee, Neural network-based robust adaptive certainty equivalent controller for quadrotor uav with unknown disturbances, Int. J. Control Autom. Syst., 17 (2019), 2365–2374. https://doi.org/10.1007/s12555-018-0720-7 doi: 10.1007/s12555-018-0720-7
[8]	X. Xiao, S. Joshi, Process planning for five-axis support free additive manufacturing, Addit. Manuf., 36 (2020), 101569. https://doi.org/10.1016/j.addma.2020.101569 doi: 10.1016/j.addma.2020.101569
[9]	H. Joe, J. Kim, S. C. Yu, 3D reconstruction using two sonar devices in a monte-carlo approach for auv application, Int. J. Control Autom. Syst., 18 (2020), 587–596. https://doi.org/10.1007/s12555-019-0692-2 doi: 10.1007/s12555-019-0692-2
[10]	O. Minin, I. Minin, Computational Fluid Dynamics: Technologies and Applications, BoD-Books on Demand, 2011.
[11]	Y. Qi, W. Yu, J. Huang, Y. Yu, Model predictive control for switched systems with a novel mixed time/event-triggering mechanism, Nonlinear Anal. Hybrid Syst., 42 (2021), 101081. https://doi.org/10.1016/j.nahs.2021.101081 doi: 10.1016/j.nahs.2021.101081
[12]	G. Q. Zeng, X. Q. Xie, M. R. Chen, J. Weng, Adaptive population extremal optimization-based pid neural network for multivariable nonlinear control systems, Swarm Evol. Comput., 44 (2019), 320–334. https://doi.org/10.1016/j.swevo.2018.04.008 doi: 10.1016/j.swevo.2018.04.008
[13]	D. X. Phu, S. B. Choi, A new adaptive fuzzy pid controller based on riccati-like equation with application to vibration control of vehicle seat suspension, Appl. Sci., 9 (2019), 4540. https://doi.org/10.3390/app9214540 doi: 10.3390/app9214540
[14]	B. Shi, Y. Su, C. Lian, C. Xiong, Y. Long, C. Gong, Obstacle type recognition in visual images via dilated convolutional neural network for unmanned surface vehicles, J. Navig., 75 (2022), 437–454. https://doi.org/10.1017/S0373463321000941 doi: 10.1017/S0373463321000941
[15]	A. J. Rashidi, B. Karimi, A. Khodaparast, A constrained predictive controller for AUV and computational optimization using laguerre functions in unknown environments, Int. J. Control Autom. Syst., 18 (2020), 753–767. https://doi.org/10.1007/s12555-018-0946-4 doi: 10.1007/s12555-018-0946-4
[16]	T. Maki, H. Horimoto, T. Ishihara, K. Kofuji, Tracking a sea turtle by an AUV with a multibeam imaging sonar: Toward robotic observation of marine life, Int. J. Control Autom. Syst., 18, (2020), 597–604. https://doi.org/10.1007/s12555-019-0690-4 doi: 10.1007/s12555-019-0690-4
[17]	Y. L. Zhou, Hybrid electric vehicle powertrain and control system modeling, analysis and design optimization, Ph.D thesis, 2011.
[18]	M. W. Dunnigan, G. T. Russell, Evaluation and reduction of the dynamic coupling between a manipulator and an underwater vehicle, IEEE J. Oceanic Eng., 23 (1998), 260–273. https://doi.org/10.1109/48.701201 doi: 10.1109/48.701201
[19]	Y. Qi, X. Xu, X. Li, Z. Ke, Y. Liu, $H_\infty$ control for networked switched systems with mixed switching law and an event-triggered communication mechanism, Int. J. Syst. Sci., 51 (2020), 1066–1083. https://doi.org/10.1080/00207721.2020.1748746 doi: 10.1080/00207721.2020.1748746
[20]	Y. Qi, J. Hu, Observer-based bumpless switching control for switched linear systems with sensor faults, Trans. Inst. Meas. Control, 40 (2018), 1490–1498. https://doi.org/10.1177/0142331216685605 doi: 10.1177/0142331216685605
[21]	H. R. Li, Z. B. Jiang, N. Kang, Sliding mode disturbance observer-based fractional second-order nonsingular terminal sliding mode control for pmsm position regulation system, Math. Prob. Eng., 2015 (2015).
[22]	J. Zhang, G. Zeng, Y. Gao, J. Wang, Underactuated microsatellite electromagnetic docking control using disturbance observer-based sliding mode controller, IEEE Access, 8 (2020), 124476–124485. https://doi.org/10.1109/ACCESS.2020.3003272 doi: 10.1109/ACCESS.2020.3003272
[23]	Y. Xiong, J. Yu, Y. Tu, L. Pan, Q. Zhu, J. Mou, Research on data driven adaptive berthing method and technology, Ocean Eng., 222 (2021), 108620. https://doi.org/10.1016/j.oceaneng.2021.108620 doi: 10.1016/j.oceaneng.2021.108620
[24]	C. Ntakolia, D. V. Lyridis, A swarm intelligence graph-based pathfinding algorithm based on fuzzy logic (sigpaf): A case study on unmanned surface vehicle multi-objective path planning, J. Mar. Sci. Eng., 9 (2021), 1243.
[25]	B. Xu, M. Jiao, X. Zhang, D. Zhang, Path tracking of an underwater snake robot and locomotion efficiency optimization based on improved pigeon-inspired algorithm, J. Mar. Sci. Eng., 10 (2022), 47. https://doi.org/10.3390/jmse10010047 doi: 10.3390/jmse10010047
[26]	J. Ru, S. Yu, H. Wu, Y. Li, C. Wu, Z. Jia, H. Xu, A multi-auv path planning system based on the omni-directional sensing ability, J. Mar. Sci. Eng., 9 (2021), 806. https://doi.org/10.3390/jmse9080806 doi: 10.3390/jmse9080806
[27]	C. Yu, R. Wang, X. Zhang, Y. Li, Experimental and numerical study on underwater radiated noise of auv, Ocean Eng., 201 (2020), 107111. https://doi.org/10.1016/j.oceaneng.2020.107111 doi: 10.1016/j.oceaneng.2020.107111
[28]	C. Shen, Y. Shi, B. Buckham, Path-following control of an AUV: A multiobjective model predictive control approach, IEEE Trans. Control Syst. Technol., 27 (2018), 1334–1342. https://doi.org/10.1109/TCST.2018.2789440 doi: 10.1109/TCST.2018.2789440
[29]	X. Cao, C. Sun, M. Yan, Target search control of AUV in underwater environment with deep reinforcement learning, IEEE Access, 7 (2019), 96549–96559. https://doi.org/10.1109/ACCESS.2019.2929120 doi: 10.1109/ACCESS.2019.2929120
[30]	Y. Guo, H. Qin, B. Xu, Y. Han, Q. Y. Fan, P. Zhang, Composite learning adaptive sliding mode control for AUV target tracking, Neurocomputing, 351 (2019), 180–186. https://doi.org/10.1016/j.neucom.2019.03.033 doi: 10.1016/j.neucom.2019.03.033
[31]	H. Cozijn, H. van der Schaaf, B. de Kruif, E. Ypma, Design of an underwater vehicle for use in basin experiments, development of marin's modular AUV, IFAC-PapersOnLine, 52 (2019), 21–26. https://doi.org/10.1016/j.ifacol.2019.08.117 doi: 10.1016/j.ifacol.2019.08.117
[32]	A. Miller, B. Miller, G. Miller, On AUV control with the aid of position estimation algorithms based on acoustic seabed sensing and DOA measurements, Sensors, 19 (2019), 5520. https://doi.org/10.3390/s19245520 doi: 10.3390/s19245520
[33]	J. Wan, B. He, D. Wang, T. Yan, Y. Shen, Fractional-order PID motion control for AUV using cloud-model-based quantum genetic algorithm, IEEE Access, 7 (2019), 124828–124843. https://doi.org/10.1109/ACCESS.2019.2937978 doi: 10.1109/ACCESS.2019.2937978
[34]	J. Rodriguez, H. Castañeda, J. L. Gordillo, Lagrange modeling and navigation based on quaternion for controlling a micro AUV under perturbations, Rob. Auton. Syst., 124 (2020), 103408. https://doi.org/10.1016/j.robot.2019.103408 doi: 10.1016/j.robot.2019.103408
[35]	L. Miller, S. Brizzolara, D. J. Stilwell, Increase in stability of an x-configured auv through hydrodynamic design iterations with the definition of a new stability index to include effect of gravity, J. Mar. Sci. Eng., 9 (2021), 942.
[36]	Y. Xia, K. Xu, W. Wang, G. Xu, X. Xiang, Y. Li, Optimal robust trajectory tracking control of a X-rudder AUV with velocity sensor failures and uncertainties, Ocean Eng., 198 (2020), 106949. https://doi.org/10.1016/j.oceaneng.2020.106949 doi: 10.1016/j.oceaneng.2020.106949
[37]	G. Che, Z. Yu, Neural-network estimators based fault-tolerant tracking control for AUV via ADP with rudders faults and ocean current disturbance, Neurocomputing, 411 (2020), 442–454. https://doi.org/10.1016/j.neucom.2020.06.026 doi: 10.1016/j.neucom.2020.06.026
[38]	P. Dai, W. Lu, K. Le, D. Liu, Sliding Mode Impedance Control for contact intervention of an I-AUV: Simulation and experimental validation, Ocean Eng., 196 (2020), 106855. https://doi.org/10.1016/j.oceaneng.2019.106855 doi: 10.1016/j.oceaneng.2019.106855
[39]	J. Wan, H. Liu, J. Yuan, Y. Shen, H. Zhang, H. Wang, et al., Motion control of autonomous underwater vehicle based on fractional calculus active disturbance rejection, J. Mar. Sci. Eng., 9 (2021), 1306.
[40]	V. Bobkov, A. Kudryashov, A. Inzartsev, Method for the coordination of referencing of autonomous underwater vehicles to man-made objects using stereo images, J. Mar. Sci. Eng., 9 (2021), 1038.

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