Research on port AGV trajectory tracking control based on improved fuzzy sliding mode control

Qiu Jiandong; Tang Minan; Yu Fan; Ran Jiajia; Lin Dingqiang

doi:10.61089/aot2024.9asah786

Authors

Qiu Jiandong School of Mechanical and Engineering, Lanzhou Jiaotong University, Lanzhou, China Author https://orcid.org/0000-0001-9972-898X
Tang Minan School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, China Author https://orcid.org/0000-0003-1424-2708
Yu Fan School of Mechanical and Engineering, Lanzhou Jiaotong University, Lanzhou, China Author https://orcid.org/0000-0002-7834-7755
Ran Jiajia School of Mechanical and Engineering, Lanzhou Jiaotong University, Lanzhou, China Author https://orcid.org/0000-0001-9674-4079
Lin Dingqiang School of Mechanical and Engineering, Lanzhou Jiaotong University, Lanzhou, China Author https://orcid.org/0000-0002-3232-9519

DOI:

https://doi.org/10.61089/aot2024.9asah786

Keywords:

port AGV, dynamics, magic tire formulation, sliding mode control, fuzzy control

Abstract

The operating environment of the port AGV is outdoors in the coastal area. There are complex and changeable environmental impacts such as rainfall, wind erosion, and salt erosion, which cause more disturbances to the port AGV. Aiming at the problem of the influence of environmental disturbance on trajectory tracking accuracy during the operation of port AGV, this paper proposes a control method based on fuzzy control theory and sliding mode variable structure control theory. Firstly, a two-degree-of-freedom dynamic model of AGV in port environment is established on the premise of accurately describing the dynamic characteristics of the vehicle and reducing the requirements for hardware and software. Secondly, for the tire model, the tire model formula constructed by the magic formula proposed by Pacejka with high fitting accuracy and simple modeling is used to establish the tire model. Thirdly, aiming at the chattering problem in sliding mode control, sliding mode variable structure control is designed, and fuzzy controller is added to control the change of switching gain coefficient. Finally, for the actual operating environment of the port, the Gaussian disturbance is used to simulate the external disturbance, and the controller model designed in various ways is built and simulated by Matlab / Simulink software. The experimental results show that the proposed algorithm is superior to the control group in response speed, anti-interference ability and reducing chattering. Compared with the traditional control method, the steady-state time of the position tracking method is increased by 73.41 %, and the error generated in the disturbance stage is 1.8 % of the initial error. The speed tracking enters the Gaussian disturbance stage after the steady state, and the error is less than 1 % of the initial error, which achieves the purpose of reducing chattering, realizes the optimization of the traditional sliding mode variable structure control, and verifies the feasibility of the algorithm in the future practical application.

References

Chen, C., Shu, M., Yang, Y., et al. (2022) Robust H ∞ path tracking control of autonomous vehicle with delay and actuator saturation. Journal of Control and Decision. 9(1): 45-57. https://doi.org/10.1080/23307706.2021.1906772.

Chih, L. H.,Yang, C. C., Hung, J. H. (2017) Path Tracking of an Autonomous Ground Vehicle With Different Payloads by Hierarchical Improved Fuzzy Dynamic Sliding-Mode Control. IEEE Transactions on Fuzzy System. 899-914. https://doi.org/10.1109/TFUZZ.2017.2698370.

Guo, H. H., Ren, F., Pang, X. Y., et al. (2021) Modeling and trajectory tracking control of differential speed driven AGV. Machine Design and Manufacturing, 2021(7): 276-280. https://doi.org/10.19356/j.cnki.1001-3997.2021.07.065.

Han, Y., Cheng, Y., Xu, G. (2019) Trajectory Tracking Control of AGV Based on Sliding Mode Control With the Improved Reaching Law. IEEE Access. 7: 20748-20755. https://doi.org/10.1109/ACCESS.2019.2897985.

Hang, X., Jin, W., Zhang, L., et al. (2019). Interval trajectory tracking for AGV based on MPC. 2019 Chinese Control Conference (CCC). 2835-2839. https://doi.org/10.23919/ChiCC.2019.8866201.

Hiraoka, T., Nishihara, O., Kumamoto, H. (2009) Automatic path-tracking controller of a four-wheel steering vehicle. Vehicle System Dynamics, Taylor & Francis. 47(10): 1205-1227. https://doi.org/10.1080/00423110802545919.

Hou, S. X., Chu, Y. D., Chen, C. (2020) Global sliding mode control of active power filters based on fuzzy neural networks. Control and Decision Making. 35(10): 2329-2335. https://doi.org/10.13195/j.kzyjc.2019.1570.

Hui, L. (2023) A heuristic algorithm for equipment scheduling at an automated container terminal with multi-size containers. Archives of Transport. 65(1): 67-86. https://doi.org/10.5604/01.3001.0016.2478.

Lee, M. Y., Chen, B. S. (2022) Robust H∞ Network Observer-Based Attack-Tolerant Path Tracking Control of Autonomous Ground Vehicle. Ieee Access, Piscataway: Ieee-Inst Electrical Electronics Engineers Inc. 10: 58332-58353. https://doi.org/10.1109/ACCESS.2022.3179111.

Li, S. S., Guo, K. H., Qiu, T., et al. (2020) Stability control of active front-wheel steering vehicle under extreme operating conditions. Automotive Engineering. 42(2): 191-198. https://doi.org/10.19562/j.chinasae.qcgc.2020.02.008.

Liu, J. K. (2019) MATLAB simulation of sliding mode control: basic theory and design methods. (4rd ed.). Tsinghua University Press, (Chapter 1).

Liu, M. Y., Duan, Z. Y. (2019) Application of Neural Network Fuzzy Control Algorithm in the DynamicObstacle Avoidance of Visual AGV. Mechanical Research & Application. 32(03):151-153. https://doi.org/10.16576/j.cnki.1007-4414.2019.03.046.

Ostafew, C. J,. Schoellig, A. P., Barfoot, T. D. (2014) Learning-based nonlinear model predictive control to improve vision-based mobile robot path-tracking in challenging outdoor environments. 2014 IEEE International Conference on Robotics and Automation (ICRA). 4029-4036. https://doi.org/10.1109/ICRA.2014.6907444.

Shao, X., Sun, G., Yao, W., et al. (2022) Adaptive Sliding Mode Control for Quadrotor UAVs With Input Saturation. IEEE/ASME Transactions on Mechatronics. 27(3): 1498-1509. https://doi.org/10.1109/TMECH.2021.3094575.

Song, L. B., Zhang, J. L., Li, B. Z., et al. (2004) AGV and its sliding mode controller design. Mechanical Design and Research. (6): 13-15+6. https://doi.org/10.13952/j.cnki.jofmdr.2004.06.002.

Soysal, B. (2014) Real-time control of an automated guided vehicle using a continuous mode of sliding mode control. Turkish Journal of Electrical Engineering and Computer Sciences. 22(5): 1298-1306. https://doi.org/10.3906/elk-1211-130.

Wang, R., Hu, C., Yan, F., et al. (2016) Composite Nonlinear Feedback Control for Path Following of Four-Wheel Independently Actuated Autonomous Ground Vehicle. IEEE Transactions on Intelligent Transportation Systems. 17(7): 2063-2074. https://doi.org/10.1109/TITS.2015.2498172.

Wang, S. (2020) Adaptive Fuzzy Sliding Mode and Robust Tracking Control for Manipulators with Uncertain Dynamics. Complexity. 2020: 1-9. https://doi.org/10.1155/2020/1492615.

Weng, X., Zhang, J., Ma, Y. (2021) Path Following Control of Automated Guided Vehicle Based on Model Predictive Control with State Classification Model and Smooth Transition Strategy. International Journal of Automotive Technology. 22(3):677-686. https://doi.org/10.1007/s12239-021-0063-x.

Wu, Y., Wang, L., Zhang, J., et al. (2019) Path Following Control of Autonomous Ground Vehicle Based on Nonsingular Terminal Sliding Mode and Active Disturbance Rejection Control. IEEE Transactions on Vehicular Technology. 68(7): 6379-6390. https://doi.org/10.1109/TVT.2019.2916982.

Xu, Q., Wang, Z., Zhen, Z. (2019) Adaptive neural network finite time control for quadrotor UAV with unknown input saturation. Nonlinear Dynamics. 98(3):1973-1998. https://doi.org/10.1007/s11071-019-05301-1.

Yang, D., Su, C., Wu, H., et al. (2022) Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic Environments. Ieee Access, Piscataway: Ieee-Inst Electrical Electronics Engineers Inc. 10: 104375-104383. https://doi.org/10.1109/ACCESS.2022.3210251.

Yang, J. Q., Gao, Y. X., Chen, R. T., et al. (2020) Design of terminal sliding mode controller for uncertain nonlinear systems based on disturbance observer. Control and Decision. 35(1): 155-160. https://doi.org/10.13195/j.kzyjc.2018.0599.

Yang, X. L., Zhu, Q. X. (2008) Design and application of electric fusion welding machine based on microcontroller and fuzzy control. Journal of Instrumentation. (7): 1507-1511. https://doi.org/10.19650/j.cnki.cjsi.2008.07.034.

Yin, C., Wang, S., Li, X., et al. (2020) Trajectory Tracking Based on Adaptive Sliding Mode Control for Agricultural Tractor. IEEE Access. 8: 113021-113029. https://doi.org/10.1109/ACCESS.2020.3002814.

You, J. P., Yu, J., Wang, X. J., et al. (2022) Design of Intelligent Dispatching System for Port Automatic Guided Vehicles. Machine Design and Research. 2022,38(02):7-11+17. https://doi.org/10.13952/j.cnki.jofmdr.2022.0040.

Zhang, J., Liu, H. X. (2020) Model-based design of the vehicle dynamics control for an omnidirectional automated guided vehicle (AGV). International Conference Mechatronic Systems and Materials. 1-6. https://doi.org/10.1109/MSM49833.2020.9202248.

Zhang, K., Sun, Q., Shi, Y. (2021) Trajectory Tracking Control of Autonomous Ground Vehicle Using Adaptive Learning MPC. IEEE Transactions on Neural Networks and Learning Systems. 32(12): 5554-5564. https://doi.org/10.1109/TNNLS.2020.3048305.

Zhang, K., Sun, Q., Shi, Y. (2021) Trajectory Tracking Control of Autonomous Ground Vehicles Using Adaptive Learning MPC. IEEE Transactions on Neural Networks and Learning Systems. 32(12): 5554–5564. https://doi.org/10.1109/TNNLS.2020.3048305.

Zhao, B., Wang, H., Li, Q., et al. (2019) PID Trajectory Tracking Control of Autonomous Ground Vehicle Based on Genetic Algorithm. 2019 Chinese Control And Decision Conference (CCDC). 3677–3682. https://doi.org/10.1109/CCDC.2019.8832531 .

Zhou, W. Q., Qi, X., Chen, L., et al. (2019) Vehicle state estimation based on the combination of traceless Kalman filter and genetic algorithm. Automotive Engineering. 41(2): 198-205. https://doi.org/10.19562/j.chinasae.qcgc.2019.02.012.