Document Type : Research Article

Authors

1 Ph. D. Student, Damavand Branch, Islamic Azad University, Damavand, Iran

2 Associate Professor, Department of Electrical Engineering, Technical and Engineering Faculty, Qom University, Iran

3 Associate Professor, Faculty of New Technologies and Aerospace Engineering, Shahid Beheshti University, Tehran, IRAN

Abstract

due to the variability and uncertainty of some process parameters under investigation and limited uncertainties and confusions, the controller design faces problems. the controller is performed locally using the information of neighboring agents and the corresponding graph has a spanning tree. fuzzy systems are used as a general approximator and the parameters of the fuzzy system are adjusted in such a way that the tracking error of each agents and the stability of the uniform ultimately bounded of the closed loop system are guaranteed. 1- considering of the nonlinear non-affine of multi-agent system, 2- The unknown dynamics of the agents, 3- The convergence of the tracking error and the formation error to zero, 4- The use of fuzzy systems as a general estimator, are the main advantages of the presented method. Finally, in the simulations performed on the quadrotor, the leader-follower formation for the desired mission is realized and according to the set criteria, the proposed methodology is satisfactory.

Keywords

Main Subjects

[1]   G. Feng, S.-G. Cao, and N. W. Rees, "Stable adaptive control of fuzzy dynamic systems," Fuzzy Sets Systems, vol. 131, no. 2, pp. 217-224, 2002.
[2]   G. Feng, "An approach to adaptive control of fuzzy dynamic systems," IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 268-275, 2002.
[3]   D. W. Djamari, "Scalable formation of heterogeneous agents considering unknown disturbances," Asian Journal of Control, vol. 23, no. 4, pp. 1631-1642, 2021.
[4]   R. Ghasemi, "Designing observer based variable structure controller for large scale nonlinear systems," IAES International Journal of Artificial Intelligence, vol. 2, no. 3, p. 125, 2013.
[5]   C. P. Chen, G.-X. Wen, Y.-J. Liu, and F.-Y. Wang, "Adaptive consensus control for a class of nonlinear multiagent time-delay systems using neural networks," IEEE Transactions on Neural Networks Learning Systems, vol. 25, no. 6, pp. 1217-1226, 2014.
[6]   R. Olfati-Saber and R. M. Murray, "Consensus problems in networks of agents with switching topology and time-delays," IEEE Transactions on automatic control, vol. 49, no. 9, pp. 1520-1533, 2004.
[7]   R. Ghasemi, "Adaptive state tracking controller for multi-input multi-output non-affine nonlinear systems," International Journal of Computer Electrical Engineering, vol. 3, no. 3, p. 426, 2011.
[8]   H. Zhang and F. L. Lewis, "Adaptive cooperative tracking control of higher-order nonlinear systems with unknown dynamics," Automatica, vol. 48, no. 7, pp. 1432-1439, 2012.
[9]   C. P. Chen, Y.-J. Liu, and G.-X. Wen, "Fuzzy neural network-based adaptive control for a class of uncertain nonlinear stochastic systems," IEEE Transactions on Cybernetics, vol. 44, no. 5, pp. 583-593, 2013.
[10] X. Wang, T. Li, C. P. Chen, and B. Lin, "Adaptive robust control based on single neural network approximation for a class of uncertain strict-feedback discrete-time nonlinear systems," Neurocomputing, vol. 138, pp. 325-331, 2014.
[11] D. Li, J. Ma, H. Zhu, and M. Sun, "The consensus of multi-agent systems with uncertainties and randomly occurring nonlinearities via impulsive control," International Journal of Control, Automation Systems, vol. 14, no. 4, pp. 1005-1011, 2016.
[12] S. Djaidja and Q. Wu, "Leader-following consensus of single-integrator multi-agent systems under noisy and delayed communication," International Journal of Control, Automation Systems, vol. 14, no. 2, pp. 357-366, 2016.
[13] C. P. Chen, C.-E. Ren, and T. Du, "Fuzzy observed-based adaptive consensus tracking control for second-order multiagent systems with heterogeneous nonlinear dynamics," IEEE Transactions on Fuzzy Systems, vol. 24, no. 4, pp. 906-915, 2015.
[14] C. P. Chen, G.-X. Wen, Y.-J. Liu, and Z. Liu, "Observer-based adaptive backstepping consensus tracking control for high-order nonlinear semi-strict-feedback multiagent systems," IEEE transactions on cybernetics, vol. 46, no. 7, pp. 1591-1601, 2015.
[15] Q. Shen, P. Shi, and Y. Shi, "Distributed adaptive fuzzy control for nonlinear multiagent systems via sliding mode observers," IEEE transactions on cybernetics, vol. 46, no. 12, pp. 3086-3097, 2015.
[16] G. Wang, C. Wang, L. Li, and Q. Du, "Distributed adaptive consensus tracking control of higher-order nonlinear strict-feedback multi-agent systems using neural networks," Neurocomputing, vol. 214, pp. 269-279, 2016.
 [17] T. Wang, Y. Zhang, J. Qiu, and H. Gao, "Adaptive fuzzy backstepping control for a class of nonlinear systems with sampled and delayed measurements," IEEE Transactions on Fuzzy Systems, vol. 23, no. 2, pp. 302-312, 2014.
[18] T. Wang, J. Qiu, and H. Gao, "Adaptive neural control of stochastic nonlinear time-delay systems with multiple constraints," IEEE Transactions on Systems, Man, Cybernetics: Systems, vol. 47, no. 8, pp. 1875-1883, 2016.
[19] T. Wang, J. Qiu, H. Gao, C. J. I. T. o. S. Wang, Man,, and C. Systems, "Network-based fuzzy control for nonlinear industrial processes with predictive compensation strategy," IEEE Transactions on Systems, Man, Cybernetics: Systems, vol. 47, no. 8, pp. 2137-2147, 2016.
[20] C. Ahn, H. Kim, and Y. Kim, "Adaptive sliding mode control for non-affine nonlinear vehicle systems," in AIAA Guidance, Navigation and Control Conference and Exhibit, 2007, p. 6506.
[21] J. Ghommam, H. Mehrjerdi, and M. Saad, "Robust formation control without velocity measurement of the leader robot," Control Engineering Practice, vol. 21, no. 8, pp. 1143-1156, 2013.
[22] G. W. Gamage, G. K. Mann, and R. G. Gosine, "Formation control of multiple nonholonomic mobile robots via dynamic feedback linearization," in 2009 International Conference on Advanced Robotics, 2009, pp. 1-6: IEEE.
[23] Y. Tian-Tian, L. Zhi-Yuan, C. Hong, and P. Run, "Formation control and obstacle avoidance for multiple mobile robots," Acta Automatica Sinica, vol. 34, no. 5, pp. 588-593, 2008.
[24] M. Parsa and M. Danesh, "Containment control of high‐order multi‐agent systems with heterogeneous uncertainties, dynamic leaders, and time delay," Asian Journal of Control, vol. 23, no. 2, pp. 799-810, 2021.
[25] M. Defoort, T. Floquet, A. Kokosy, and W. Perruquetti, "Sliding-mode formation control for cooperative autonomous mobile robots," IEEE Transactions on Industrial Electronics, vol. 55, no. 11, pp. 3944-3953, 2008.
[26] J. Ghommam, M. S. Mahmoud, and M. Saad, "Robust cooperative control for a group of mobile robots with quantized information exchange," Journal of the Franklin Institute, vol. 350, no. 8, pp. 2291-2321, 2013.
[27] Y.-H. Chang, C.-Y. Yang, W.-S. Chan, C.-W. Chang, and C.-W. Tao, "Leader-following formation control of multi-robot systems with adaptive fuzzy terminal sliding-mode controller," in 2013 International Conference on System Science and Engineering (ICSSE), 2013, pp. 45-50: IEEE.
[28] A. Guillet, R. Lenain, B. Thuilot, and P. Martinet, "Adaptable robot formation control: Adaptive and predictive formation control of autonomous vehicles," IEEE Robotics Automation Magazine, vol. 21, no. 1, pp. 28-39, 2014.
[29] B. Shasti, A. Alasty, and N. Assadian, "Robust distributed control of spacecraft formation flying with adaptive network topology," Acta Astronautica, vol. 136, pp. 281-296, 2017.
[30] M. A. V. Shahbahrami, A. Alikhani, "Attitude and Vibration Control of a Flexible Spacecraft using Hybrid Adaptive Super-Twisting Non-singular Terminal Sliding Mode Control," Journal of Space Science and Technology, vol. Online publishing.
[31] N. D. V. Nekoukar, "Fuzzy Adaptive Control of Unmanned Aerial Vehicle for Carrying Time-Varying Cargo on Predefined Path," Journal of Contro, vol. 14, 2020.
[32] P. Manouchehri, R. Ghasemi, A. Toloei, and F. Mohammadi, "Distributed neural observer-based formation strategy of non-affine nonlinear multi-agent systems with unknown dynamics," Journal of Circuits, Systems Computers, vol. 30, no. 05, p. 2130005, 2021.
[33] J. Bae and Y. Kim, "Adaptive controller design for spacecraft formation flying using sliding mode controller and neural networks," Journal of the Franklin Institute, vol. 349, no. 2, pp. 578-603, 2012.
[34] W. Xin, Z. Shasha, and Z. Xingwang, "Adaptive fuzzy sliding mode controller for attitude coordinated control in spacecraft formation," GSTF Journal on Aviation Technology, vol. 1, no. 2, pp. 1-7, 2015.
[35] V. Behnamgol, A. Vali, and A. Mohammadi, "Lyapunov-basedAdaptive SmoothSecond-order Sliding Mode Guidance Law with Proving Finite Time Stability," Journal of Space ScienceTechnology, vol. 11, no. 2, pp. 33-39, 2018.