Formation control of nonholonomic wheeled mobile robots using adaptive distributed fractional order fast terminal sliding mode control

• Autorzy: Damani Allaeddine Yahia , Benselama Zoubir Abdeslem , Hedjar Ramdan

Identyfikatory

DOI

10.24425/ame.2023.148700

• Języki publikacji: EN

Abstrakty

EN

In this paper, an adaptive distributed formation controller for wheeled nonholonomic mobile robots is developed. The dynamical model of the robots is first derived by employing the Euler-Lagrange equation while taking into consideration the presence of disturbances and uncertainties in practical applications. Then, by incorporating fractional calculus in conjunction with fast terminal sliding mode control and consensus protocol, a robust distributed formation controller is designed to assure a fast and finite-time convergence of the robots towards the required formation pattern. Additionally, an adaptive mechanism is integrated to effectively counteract the effects of disturbances and uncertain dynamics. Moreover, the suggested control scheme’s stability is theoretically proven through the Lyapunov theorem. Finally, simulation outcomes are given in order to show the enhanced performance and efficiency of the suggested control technique.

Słowa kluczowe

EN

mobile robots; fractional calculus; formation control; sliding mode; consensus protocol

PL

roboty mobilne; rachunek ułamkowy; kontrola formacji; tryb przesuwania; protokół konsensusu

• Wydawca: Komitet Budowy Maszyn PAN
• Czasopismo: Archive of Mechanical Engineering
• Rocznik: 2023
• Tom: LXX, nr 4
• Strony: 567–--587
• Opis fizyczny: Bibliogr. 32 poz., wykr., tab.

Twórcy

autor

Damani Allaeddine Yahia

• Laboratory of signal and image processing, Saad Dahlab University Blida 1, Blida, Algeria.

• https://orcid.org/0000-0002-3068-8518

autor

Benselama Zoubir Abdeslem

• Laboratory of signal and image processing, Saad Dahlab University Blida 1, Blida, Algeria.

• https://orcid.org/0000-0003-3839-7544

autor

Hedjar Ramdan

• Center of Smart Robotics Research CEN, King Saud University, Riyadh, Saudi Arabia

• https://orcid.org/0000-0002-4648-1554

Bibliografia

• [1] D. Xu, X. Zhang, Z. Zhu, C. Chen, and P. Yang. Behavior-based formation control of swarm robots. Mathematical Problems in Engineering, 2014:205759, 2014. doi: 10.1155/2014/ 205759.
• [2] G. Lee and D. Chwa. Decentralized behavior-based formation control of multiple robots considering obstacle avoidance. Intelligent Service Robotics, 11:127–138, 2018. doi: 10.1007/s11370-017-0240-y.
• [3] N. Hacene and B. Mendil. Behavior-based autonomous navigation and formation control of mobile robots in unknown cluttered dynamic environments with dynamic target tracking. International Journal of Automation and Computing, 18:766–786, 2021. doi: 10.1007/s11633-020-1264-x.
• [4] Z. Pan, D. Li, K. Yang, and H. Deng. Multi-robot obstacle avoidance based on the improved artificial potential field and pid adaptive tracking control algorithm. Robotica, 37(11):1883– 1903, 2019. doi: 10.1017/S026357471900033X.
• [5] A.D. Dang, H.M. La, T. Nguyen, and J. Horn. Formation control for autonomous robots with collision and obstacle avoidance using a rotational and repulsive force–based approach. International Journal of Advanced Robotic Systems, 16(3):1729881419847897, 2019. doi: 10.1177/1729881419847897.
• [6] M. Maghenem, A. Loría, E. Nuno, and E. Panteley. Consensus-based formation control of networked nonholonomic vehicles with delayed communications. IEEE Transactions on Automatic Control, 66(5):2242–2249, 2020. doi: 10.1109/TAC.2020.3005668.
• [7] J.G. Romero, E. Nuño, E. Restrepo, and I. Sarras. Global consensus-based formation control of nonholonomic mobile robots with time-varying delays and without velocity measurements. IEEE Transactions on Automatic Control, 2023. doi: 10.1109/TAC.2023.3264744.
• [8] S.-L. Dai, S. He, X. Chen, and X. Jin. Adaptive leader–follower formation control of nonholonomic mobile robots with prescribed transient and steady-state performance. IEEE Transactions on Industrial Informatics, 16(6):3662–3671, 2019. doi: 10.1109/TII.2019.2939263.
• [9] J. Hirata-Acosta, J. Pliego-Jiménez, C. Cruz-Hernádez, and R. Martínez-Clark. Leader-follower formation control of wheeled mobile robots without attitude measurements. Applied Sciences, 11(12):5639, 2021. doi: 10.3390/app11125639.
• [10] X. Liang, H. Wang, Y.-H. Liu, Z. Liu, and W. Chen. Leader-following formation control of non- holonomic mobile robots with velocity observers. IEEE/ASME Transactions on Mechatronics, 25(4):1747–1755, 2020. doi: 10.1109/TMECH.2020.2990991.
• [11] X. Chen, F. Huang, Y. Zhang, Z. Chen, S. Liu, Y. Nie, J. Tang, and S. Zhu. A novel virtual- structure formation control design for mobile robots with obstacle avoidance. Applied Sciences, 10(17):5807, 2020. doi: 10.3390/app10175807.
• [12] L. Dong, Y. Chen, and X. Qu. Formation control strategy for nonholonomic intelligent vehicles based on virtual structure and consensus approach. Procedia Engineering, 137:415–424, 2016. doi: 10.1016/j.proeng.2016.01.276.
• [13] N. Nfaileh, K. Alipour, B. Tarvirdizadeh, and A. Hadi. Formation control of multiple wheeled mobile robots based on model predictive control. Robotica, 40(9):3178–3213, 2022. doi: 10.1017/S0263574722000121.
• [14] H. Xiao, C.L.P. Chen, G. Lai, D. Yu, and Y. Zhang. Integrated nonholonomic multi-robot consensus tracking formation using neural-network-optimized distributed model predictive control strategy. Neurocomputing, 518:282–293, 2023. doi: 10.1016/j.neucom.2022.11.007.
• [15] W. Wang, J. Huang, C. Wen, and H. Fan. Distributed adaptive control for consensus tracking with application to formation control of nonholonomic mobile robots. Automatica, 50(4):1254–1263, 2014. doi: 10.1016/j.automatica.2014.02.028.
• [16] Y.H. Moorthy and S. Joo. Distributed leader-following formation control for multiple nonholo nomic mobile robots via bioinspired neurodynamic approach. Neurocomputing, 492:308–321, 2022. doi: 10.1016/j.neucom.2022.04.001.
• [17] S. Ik Han. Prescribed consensus and formation error constrained finite-time sliding mode control for multi-agent mobile robot systems. IET Control Theory & Applications, 12(2):282–290, 2018. doi: 10.1049/iet-cta.2017.0351.
• [18] C.-C. Tsai, Y.-X. Li, and F.-C. Tai. Backstepping sliding-mode leader-follower consensus formation control of uncertain networked heterogeneous nonholonomic wheeled mobile multirobots. In 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pages 1407–1412. IEEE, 2017. doi: 10.23919/SICE.2017.8105661.
• [19] R. Rahmani, H. Toshani, and S. Mobayen. Consensus tracking of multi-agent systems using constrained neural-optimiser-based sliding mode control. International Journal of Systems Science, 51(14):2653–2674, 2020. doi: 10.1080/00207721.2020.1799257.
• [20] R. Afdila, F. Fahmi, and A. Sani. Distributed formation control for groups of mobile robots using consensus algorithm. Bulletin of Electrical Engineering and Informatics, 12(4):20952104, 2023. doi: 10.11591/eei.v12i4.3869.
• [21] L.-D. Nguyen, H.-L. Phan, H.-G. Nguyen, and T.-L. Nguyen. Event-triggered distributed robust optimal control of nonholonomic mobile agents with obstacle avoidance formation, input constraints and external disturbances. Journal of the Franklin Institute, 360(8):55645587, 2023. doi: 10.1016/j.jfranklin.2023.02.033.
• [22] Y.-H. Chang, C.-Y. Yang, W.-S. Chan, H.-W. Lin, and C.-W. Chang. Adaptive fuzzy sliding mode formation controller design for multi-robot dynamic systems. International Journal of Fuzzy Systems, 16(1):121–131, 2014.
• [23] X. Chu, Z. Peng, G. Wen, and A. Rahmani. Robust fixed-time consensus tracking with application to formation control of unicycles. IET Control Theory & Applications, 12(1):53–59, 2018. doi: 10.1049/iet-cta.2017.0319.
• [24] Y. Cheng, R. Jia, H. Du, G. Wen, and W. Zhu. Robust finite-time consensus formation control for multiple nonholonomic wheeled mobile robots via output feedback. International Journal of Robust and Nonlinear Control, 28(6):2082–2096, 2018. doi: 10.1002/rnc.4002.
• [25] Y. Xie, X. Zhang, W. Meng, S. Zheng, L. Jiang, J. Meng, and S. Wang. Coupled fractional order sliding mode control and obstacle avoidance of a four-wheeled steerable mobile robot. ISA Transactions, 108:282–294, 2021. doi: 10.1016/j.isatra.2020.08.025.
• [26] J. Bai, G. Wen, A. Rahmani, and Y. Yu. Distributed formation control of fractional-order multi-agent systems with absolute damping and communication delay. International Journal of Systems Science, 46(13):2380–2392, 2015. doi: 10.1080/00207721.2014.998411.
• [27] R. Cajo, M. Guinaldo, E. Fabregas, S. Dormido, D. Plaza, R. De Keyser, and C. Ionescu. Distributed formation control for multiagent systems using a fractional-order proportional integral structure. IEEE Transactions on Control Systems Technology, 29(6):2738–2745, 2021. doi: 10.1109/TCST.2021.3053541.
• [28] K.K. Ayten, M.H. Çiplak, and A. Dumlu. Implementation a fractional-order adaptive model based pid-type sliding mode speed control for wheeled mobile robot. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 233(8):1067–1084, 2019. doi: 10.1177/0959651819847395.
• [29] D. Baleanu, K. Diethelm, E. Scalas, and J.J. Trujillo. Fractional Calculus: Models and Numerical Methods, volume 3. World Scientific, 2012.
• [30] Y.-H. Chang, C.-W. Chang, C.-L. Chen, and C.-W. Tao. Fuzzy sliding-mode formation control for multirobot systems: design and implementation. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2):444–457, 2011. doi: 10.1109/TSMCB.2011.2167679.
• [31] W. Ren and Beard R.W. Distributed consensus in multi-vehicle cooperative control: Theory and applications. Springer, London, 2007.
• [32] T.-L. Liao, J.-J. Yan, and W.-S. Chan. Distributed sliding-mode formation controller design for multirobot dynamic systems. Journal of Dynamic Systems, Measurement, and Control, 139(6):061008, 2017. doi: 10.1115/1.4035614.