Dynamic adjustment neural network-based cooperative control for vehicle platoons with state constraints

• Autorzy: Wang Ping , Gao Min , Li Junyu , Zhang Anguo

Identyfikatory

DOI

10.61822/amcs-2024-0015

• Języki publikacji: EN

Abstrakty

EN

This paper addresses the challenge of managing state constraints in vehicle platoons, including maintaining safe distances and aligning velocities, which are key factors that contribute to performance degradation in platoon control. Traditional platoon control strategies, which rely on a constant time-headway policy, often lead to deteriorated performance and even instability, primarily during dynamic traffic conditions involving vehicle acceleration and deceleration. The underlying issue is the inadequacy of these methods to adapt to variable time-delays and to accurately modulate the spacing and speed among vehicles. To address these challenges, we propose a dynamic adjustment neural network (DANN) based cooperative control scheme. The proposed strategy employs neural networks to continuously learn and adjust to time varying conditions, thus enabling precise control of each vehicle’s state within the platoon. By integrating a DANN into the platoon control system, we ensure that both velocity and inter-vehicular spacing adapt in response to real-time traffic dynamics. The efficacy of our proposed control approach is validated using both Lyapunov stability theory and numeric simulation, which confirms substantial gains in stability and velocity tracking of the vehicle platoon.

Słowa kluczowe

EN

vehicle platoon; dynamic adjustment neural network; DANN; cooperative control; state constraint

PL

konwój pojazdów; sieć neuronowa; DANN; ograniczenia stanu

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2024
• Tom: Vol. 34, no. 2
• Strony: 211--224
• Opis fizyczny: Bibliogr. 33 poz., rys., tab., wykr.

Twórcy

autor

Wang Ping

• School of Mechanical and Electrical Engineering, Hefei Technology College, No. 2, Daihe Road, Xinzhan District, Hefei 230009, China

autor

Gao Min

• School of Mechanical and Electrical Engineering, Hefei Technology College, No. 2, Daihe Road, Xinzhan District, Hefei 230009, China

autor

Li Junyu

• School of Mechanical and Electrical Engineering, Hefei Technology College, No. 2, Daihe Road, Xinzhan District, Hefei 230009, China

autor

Zhang Anguo

• Institute of Microelectronics, University of Macau, Avenida da Universidade, Taipa 999007, Macau, China

Bibliografia

• [1] Chang, B.-J., Hwang, R.-H., Tsai, Y.-L., Yu, B.-H. and Liang, Y.-H. (2019). Cooperative adaptive driving for platooning autonomous self driving based on edge computing, International Journal of Applied Mathematics and Computer Science 29(2): 213-225, DOI: 10.2478/amcs-2019-0016.
• [2] Corets, J., Martinez, S., Karatas, T. and Bullo, F. (2002). Coverage control for mobile sensing networks, International Conference on Robotics and Automation, Washington, USA, pp. 1327-1332.
• [3] Dutta, R.G., Hu, Y., Yu, F., Zhang, T. and Jin, Y. (2022). Design and analysis of secure distributed estimator for vehicular platooning in adversarial environment, IEEE Transactions on Intelligent Transportation Systems 23(4): 3418-3429.
• [4] Earnhardta, C., Groelke, B., Borek, J., Evan, Pelletier, Brennan, S. and Vermillion, C. (2022). Cooperative exchange-based platooning using predicted fuel-optimal operation of heavy-duty vehicles, IEEE Transactions on Intelligent Transportation Systems 23(10): 17312-17324.
• [5] Feng, G., Dang, D. and He, Y. (2022). Robust coordinated control of nonlinear heterogeneous platoon interacted by uncertain topology, IEEE Transactions on Intelligent Transportation Systems 23(6): 4982-4992.
• [6] Gao, F., Hu, X., Li, S.E., Li, K. and Sun, Q. (2018). Distributed adaptive sliding mode control of vehicular platoon with uncertain interaction topology, IEEE Transactions on Industrial Electronics 65(8): 6352-6361.
• [7] Gao, F., Li, S. E., Zheng, Y. and Kum, D. (2016). Robust control of heterogeneous vehicular platoon with uncertain dynamics and communication delay, IET Intelligent Transport Systems 10(7): 503-513.
• [8] Gong, S., Zheng, M., Hu, J. and Zhang, A. (2023). Event-triggered cooperative control for high-order nonlinear multi-agent systems with finite-time consensus, International Journal of Applied Mathematics and Computer Science 33(3): 439-448, DOI: 10.34768/amcs-2023-0032.
• [9] Hu, B.-B., Zhang, H.-T., Yao, W., Ding, J. and Cao, M. (2023). Spontaneous-ordering platoon control for multirobot path navigation using guiding vector fields, IEEE Transactions on Robotics 39(4): 2654-2668.
• [10] Hu, M., Li, C., Bian, Y., Zhang, H., Qin, Z. and Xu, B. (2022). Fuel economy-oriented vehicle platoon control using economic model predictive control, IEEE Transactions on Intelligent Transportation Systems 23(11).
• [11] Huang, J., Chen, J., Yang, H. and Li, D. (2023). Vehicle platoon tracking control based on adaptive neural network algorithm, International Journal of Control, Automation and Systems 21(10): 3405-3418.
• [12] Huang, Z., Chu, D.,Wu, C. and He, Y. (2019). Path planning and cooperative control for automated vehicle platoon using hybrid automata, IEEE Transactions on Intelligent Transportation Systems 20(3): 959-974.
• [13] Li, J., Zhang, A. and Peng, C. (2022). Neuro-adaptive cooperative control for a class of high-order nonlinear multi-agent systems, Measurement and Control 56(5-6): 928-937.
• [14] Li, K., Li, S.E., Gao, F., Lin, Z., Li, J. and Sun, Q. (2020a). Robust distributed consensus control of uncertain multiagents interacted by eigenvalue-bounded topologies, IEEE Internet of Things Journal 7(5): 3790-3798.
• [15] Li, M., Cao, Z. and Li, Z. (2021). A reinforcement learning-based vehicle platoon control strategy for reducing energy consumption in traffic oscillations, IEEE Transactions on Neural Networks and Learning Systems 32(12): 5309-5322.
• [16] Li, S. E., Zheng, Y., Li, K. and Wang, J. (2015). An overview of vehicular platoon control under the four-component framework, IEEE Intelligent Vehicles Symposium (IV), Seoul, Korea, pp. 286-291.
• [17] Li, Y., Chen, W., Peeta, S. and Wang, Y. (2020b). Platoon control of connected multi-vehicle systems under v2x communications: Design and experiments, IEEE Transactions on Intelligent Transportation Systems 21(5): 1891-1902.
• [18] Li, Y., Tang, C., Peeta, S. and Wang, Y. (2019). Nonlinear consensus-based connected vehicle platoon control incorporating car-following interactions and heterogeneous time delays, IEEE Transactions on Intelligent Transportation Systems 20(6): 2209-2219.
• [19] Liang, X., Xu, C. and Wang, D. (2020). Adaptive neural network control for marine surface vehicles platoon with input saturation and output constraints, AIMS Math 5(1): 587-602.
• [20] Liu, H., Zhuang, W., Yin, G., Tang, Z. and Xu, L. (2018). Strategy for heterogeneous vehicular platoons merging in automated highway system, Chinese Control And Decision Conference (CCDC), Shenyang, China, pp. 2736-2746.
• [21] Liu, Y., Yao, D., Li, H. and Lu, R. (2022). Distributed cooperative compound tracking control for a platoon of vehicles with adaptive NN, IEEE Transactions on Cybernetics 52(7): 7039-7048.
• [22] Liu, Y., Zong, C. and Zhang, D. (2019). Lateral control system for vehicle platoon considering vehicle dynamic characteristics, IET Intelligent Transport Systems 13(9): 1356-1364.
• [23] Mitrinovic, D.S., Pecaric, J.E. and Fink, A.M. (1993a). Cauchy’s and Related Inequalities, Springer, Dordrecht.
• [24] Mitrinovic, D.S., Pecaric, J.E. and Fink, A.M. (1993b). Young’s Inequality, Springer, Dordrecht.
• [25] Okamoto, A., Feeley, J., Edwards, D. and Wall, R. (2004). Robust control of a platoon of underwater autonomous vehicles, Oceans’04, MTS/IEEE Techno-Ocean, Kobe, Japan, pp. 505-510.
• [26] Peng, C., Zhang, A. and Li, J. (2021). Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties, International Journal of Applied Mathematics and Computer Science 31(4): 635-645, DOI: 10.34768/amcs-2021-0044.
• [27] Prayitno, A., Indrawati, V. and Nilkhamhang, I. (2023). Distributed model reference control for synchronization of a vehicle platoon with limited output information and subject to periodical intermittent information, International Journal of Applied Mathematics and Computer Science 33(4): 537-551, DOI: 10.34768/amcs-2023-0039.
• [28] Wang, P., Deng, H., Zhang, J., Wang, L., Zhang, M. and Li, Y. (2022). Model predictive control for connected vehicle platoon under switching communication topology, IEEE Transactions on Intelligent Transportation Systems 23(7): 7817-7830.
• [29] Wang, W., Gao, X., Li, T., Wang, Y. and Chen, C.L.P. (2023). Observer-based platoon formation control with prescribed performance guarantees for unmanned surface vehicles in presence of nonsmooth input characteristics, IEEE Transactions on Circuits and Systems II: Express Briefs 71(3): 1226-1230.
• [30] Wu, Z., Sun, J. and Hong, S. (2022). RBFNN-based adaptive event-triggered control for heterogeneous vehicle platoon consensus, IEEE Transactions on Intelligent Transportation Systems 23(10): 18761-18773.
• [31] Zhang, D., Shen, Y.-P., Zhou, S.-Q., Dong, X.-W. and Yu, L. (2021). Distributed secure platoon control of connected vehicles subject to DoS attack: Theory and application, IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(11): 7269-7278.
• [32] Zhao, X., Chen, Y.H. and Zhao, H. (2017). Robust approximate constraint-following control for autonomous vehicle platoon systems, Asian Journal of Control 20(4): 1611-1623.
• [33] Zhou, H., Saigal, R., Dion, F. and Yang, L. (2012). Vehicle platoon control in high-latency wireless communications environment: Model predictive control method, Journal of the Transportation Research Board 2324(1): 81-90.