Abstract
Receding horizon control has been shown as a good method in multiple robot formation control problem. However, there are still two disadvantages in almost all receding horizon formation control (RHFC) algorithms. One of them is the huge computational burden due to the complicated nonlinear dynamical optimization, and the other is that most RHFC algorithms use the absolute states directly while relative states between two robots are more accurate and easier to be measured in many applications. Thus, in this paper, a new relative state modeling based distributed RHFC algorithm is designed to solve the two problems referred to above. Firstly, a simple strategy to modeling the dynamical process of the relative states is given; Subsequently, the distributed RHFC algorithm is introduced and the convergence is ensured by some extra constraints; Finally, formation control simulation with respect to three ground robots is conducted and the results show the improvement of the new given algorithm in the real time capability and the insensitiveness to the measurement noise.
This work is supported by the Chinese National Natural Science Foundation: 61005078 and 61035005.
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References
Das, A.K., Fierro, R., Kumar, V.: A vision-based formation control framework. J. IEEE Transactions on Robotics and Automation 18(5), 813–825 (2002)
Balch, T., Arkin, R.C.: Behavior-based formation control for multi-robot teams. J. IEEE Transactions on Robotics and Automation 14(6), 926–939 (1998)
Lewis, M.A., Tan, K.H.: High precision formation control of mobile robots using virtual structures. J. Autonomous Robots 4(4), 387–403 (1997)
Camponogara, E., Jia, D., Krogh, B.H., Talukdar, S.: Distributed model predictive control. J. IEEE Control Systems Magazine 22(1), 44–52 (2002)
Motee, N., Sayyar-Rodsari, B.: Optimal partitioning in distributed model predictive control. In: Proceedings of the American Control Conference, pp. 5300–5305 (2003)
Jia, D., Krogh, B.H.: Min-max feedback model predictive control for distributed control with communication. In: Proceedings of the American Control Conference, pp. 4507–4512 (2002)
Richards, A., How, J.: A decentralized algorithm for robust constrained model predictive control. In: Proceedings of the American Control Conference, pp. 4261–4266 (2004)
Keviczy, T., Borrelli, F., Balas, G.J.: Decentralized receding horizon control for large scale dynamically decoupled systems. J. Automatica 42(12), 2105–2115 (2006)
Dunbar, W.B., Murray, R.M.: Distributed receding horizon control for multi-vehicle formation stabilization. J. Automatica 42(4), 549–558 (2006)
Inalhan, G., Tillerson, M., How, J.P.: Relative dynamics and control of spacecraft formations in eccentric orbits. J. Guidance, Control, and Dynamics 25(1), 48–59 (2002)
Chen, X.P., Serrani, A., Ozbay, H.: Control of leader-follower formations of terrestrial UAVs. In: Proceedings of Decision and Control, pp. 498–503 (2003)
Wang, Z., He, Y.Q., Han, J.D.: Multi-unmanned helicopter formation control on relative dynamics. In: IEEE International Conference on Mechatronics and Automation, pp. 4381–4386 (2009)
Chen, H., Allgower, F.: Quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability. J. Automatica 34(10), 1205–1217 (1998)
Fukao, T., Nakagawa, H., Adachi, N.: Adaptive tracking control of a nonholonomic mobile robot. J. IEEE Transactions on Robotics and Automation 16(5), 609–615 (2002)
Béjar, M., Ollero, A., Cuesta, F.: Modeling and control of autonomous helicopters. J. Advances in Control Theory and Applications 353, 1–29 (2007)
Leitner, J.: Formation flying system design for a planer-finding telescope-occulter system. In: Proceedings of SPIE the International Society for Optical Engineering, pp. 66871D-10 (2007)
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Zheng, W., Yuqing, H., Jianda, H. (2011). Relative State Modeling Based Distributed Receding Horizon Formation Control of Multiple Robot Systems. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_14
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DOI: https://doi.org/10.1007/978-3-642-21524-7_14
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