© The Institution of Engineering and Technology
This study considers an iterative learning control approach to achieve accurate coordination performances of the output data sequences for multiple plants that are involved in a networked environment. To realise such a desirable control objective, an update process of the input data sequence is needed to refine its output performance iteratively for each plant, which uses the local or nearest neighbour knowledge. The nominal multi-agent systems are employed as the plants’ description, for which input–output data-driven consensus problems are addressed in a hybrid networked environment given by signed directed graphs with both cooperative and antagonistic interactions. It is proved that the output data can be guaranteed to achieve bipartite consensus or remain stable for the multi-agent networks under structurally balanced or structurally unbalanced signed graphs. Moreover, the convergence conditions are derived, which need less knowledge of the agents’ plant, and the proposed consensus results can be developed to take into account the plant uncertainties and noises. Simulation tests are performed to verify the effectiveness of the learning approach in refining high input–output data-driven consensus performances of networked agents.
References
-
-
1)
-
35. Meng, D., Jia, Y., Du, J., Yuan, S: ‘Robust discrete-time iterative learning control for nonlinear systems with varying initial state shifts’, IEEE Trans. Autom. Control, 2009, 54, (11), pp. 2626–2631 (doi: 10.1109/TAC.2009.2031564).
-
2)
-
16. Yang, S., Xu, J.X.: ‘Adaptive iterative learning control for multi-agent systems consensus tracking’. Proc. of the IEEE Int. Conf. on Systems, Man, and Cybernetics, Seoul, Korea, 14–17 October 2012, pp. 2803–2808.
-
3)
-
3. Ahn, H.S., Moore, K.L., Chen, Y.: ‘Trajectory-keeping in satellite formation flying via robust periodic learning control’, Int. J. Robust Nonlinear Control, 2010, 20, (14), pp. 1655–1666 (doi: 10.1002/rnc.1538).
-
4)
-
4. Wu, H.X., Panda, S.K., Xu, J.X.: ‘Design of a plug-in repetitive control scheme for eliminating supply-side current harmonics of three-phase PWM boost rectifiers under generalized supply voltage conditions’, IEEE Trans. Power Electron., 2010, 25, (7), pp. 1800–1810 (doi: 10.1109/TPEL.2010.2042304).
-
5)
-
10. Chi, R., Wang, D., Hou, Z., Jin, S.: ‘Data-driven optimal terminal iterative learning control’, J. Process Control, 2012, 22, (10), pp. 2026–2037 (doi: 10.1016/j.jprocont.2012.08.001).
-
6)
-
13. Ahn, H.S., Chen, Y., Moore, K.L.: ‘Iterative learning control: Brief survey and categorization’, IEEE Trans. Syst. Man Cybern. C, Appl. Rev., 2007, 37, (6), pp. 1099–1121 (doi: 10.1109/TSMCC.2007.905759).
-
7)
-
1. Olfati-Saber, R., Murray, R.M.: ‘Consensus problems in networks of agents with switching topology and time-delays’, IEEE Trans. Autom. Control, 2004, 49, (9), pp. 1520–1533 (doi: 10.1109/TAC.2004.834113).
-
8)
-
34. Horn, R.A., Johnson, C.R.: ‘Topics in matrix analysis’ (Cambridge University Press, Cambridge, 1991).
-
9)
-
8. Chen, X., Jia, Y.: ‘Stereo vision-based formation control of mobile robots using iterative learning’. Proc. of the Int. Conf. on Humanized Systems, Kyoto, Japan, 17–19 September 2010, pp. 62–67.
-
10)
-
11. Chi, R., Hou, Z., Jin, S., Wang, D.: ‘Improved data-driven optimal TILC using time-varying input signals’, J. Process Control, 2014, 24, (12), pp. 78–85 (doi: 10.1016/j.jprocont.2014.07.007).
-
11)
-
20. Meng, D., Jia, Y., Du, J., Yu, F.: ‘Tracking algorithms for multiagent systems’, IEEE Trans. Neural Netw. Learn. Syst., 2013, 24, (10), pp. 1660–1676 (doi: 10.1109/TNNLS.2013.2262234).
-
12)
-
4. Jadbabaie, A., Lin, J., Morse, A.S.: ‘Coordination of groups of mobile autonomous agents using nearest neighbor rules’, IEEE Trans. Autom. Control, 2003, 48, (6), pp. 988–1001 (doi: 10.1109/TAC.2003.812781).
-
13)
-
19. Meng, D., Jia, Y., Du, J.: ‘Multi-agent iterative learning control with communication topologies dynamically changing in two directions’, IET Control Theory Appl., 2013, 7, (2), pp. 261–270 (doi: 10.1049/iet-cta.2012.0812).
-
14)
-
22. Yang, S., Xu, J.X., Huang, D., Tan, Y.: ‘Optimal iterative learning control design for multi-agent systems consensus tracking’, Syst. Control Lett., 2014, 69, pp. 80–89 (doi: 10.1016/j.sysconle.2014.04.009).
-
15)
-
15. Ahn, H.S., Chen, Y.: ‘Iterative learning control for multi-agent formation’. Proc. of the ICROS-SICE Int. Joint Conf., Fukuoka, Japan, 18–21 August 2009, pp. 3111–3116.
-
16)
-
2. Ren, W., Beard, R.W.: ‘Consensus seeking in multi-agent systems under dynamically changing interaction topologies’, IEEE Trans. Autom. Control, 2005, 50, (5), pp. 655–661 (doi: 10.1109/TAC.2005.846556).
-
17)
-
23. Shen, B., Wang, Z., Hung, Y.S.: ‘Distributed ℋ∞-consensus filtering in sensor networks with multiple missing measurements: the finite-horizon case’, Automatica, 2010, 46, (10), pp. 1682–1688 (doi: 10.1016/j.automatica.2010.06.025).
-
18)
-
9. Shen, B., Wang, Z., Liu, X.: ‘A stochastic sampled-data approach to distributed ℋ∞ filtering in sensor networks’, IEEE Trans. Circuits Syst. I, 2011, 58, (9), pp. 2237–2246 (doi: 10.1109/TCSI.2011.2112594).
-
19)
-
31. Valcher, M.E., Misra, P.: ‘On the consensus and bipartite consensus in high-order multi-agent dynamical systems with antagonistic interactions’, Syst. Control Lett., 2014, 66, pp. 94–103 (doi: 10.1016/j.sysconle.2014.01.006).
-
20)
-
1. Bayezit, I., Fidan, B.: ‘Distributed cohesive motion control of flight vehicle formations’, IEEE Trans. Ind. Electron., 2013, 60, (12), pp. 5763–5772 (doi: 10.1109/TIE.2012.2235391).
-
21)
-
30. Altafini, C.: ‘Consensus problems on networks with antagonistic interactions’, IEEE Tran. Autom. Control, 2013, 58, (4), pp. 935–946 (doi: 10.1109/TAC.2012.2224251).
-
22)
-
36. Horn, R.A., Johnson, C.R.: ‘Matrix analysis’ (Cambridge University Press, Cambridge, 1985).
-
23)
-
18. Li, J., Li, J.: ‘Adaptive iterative learning control for consensus of multi-agent systems’, IET Control Theory Appl., 2013, 7, (1), pp. 136–142 (doi: 10.1049/iet-cta.2012.0048).
-
24)
-
7. Sun, H., Hou, Z., Li, D.: ‘Coordinated iterative learning control schemes for train trajectory tracking with overspeed protection’, IEEE Trans. Autom. Sci. Eng., 2013, 10, (2), pp. 323–333 (doi: 10.1109/TASE.2012.2216261).
-
25)
-
12. Bristow, D.A., Tharayil, M., Alleyne, A.G.: ‘A survey of iterative learning control: A learning-based method for high-performance tracking control’, IEEE Control Syst. Mag., 2006, 26, (3), pp. 96–114 (doi: 10.1109/MCS.2006.1636313).
-
26)
-
14. Xu, J.X.: ‘A survey on iterative learning control for nonlinear systems’, Int. J. Control, 2011, 84, (7), pp. 1275–1294 (doi: 10.1080/00207179.2011.574236).
-
27)
-
33. Fan, M.C., Zhang, H.T., Wang, M.: ‘Bipartite flocking for multi-agent systems’, Commun. Nonlinear Sci. Numer. Simul., 2014, 19, (9), pp. 3313–3322 (doi: 10.1016/j.cnsns.2013.10.009).
-
28)
-
15. Cao, Y.C., Wu, W.W., Ren, W., Chen, G.R.: ‘An overview of recent process in the study of distributed multi-agent coordination’, IEEE Trans. Ind. Inf., 2013, 9, (1), pp. 427–438 (doi: 10.1109/TII.2012.2219061).
-
29)
-
17. Meng, D., Jia, Y., Du, J., Yu, F.: ‘Tracking control over a finite interval for multi-agent systems with a time-varying reference trajectory’, Syst. Control Lett., 2012, 61, (7), pp. 807–818 (doi: 10.1016/j.sysconle.2012.04.007).
-
30)
-
6. Liu, J., Zanchetta, P., Degano, M., Lavopa, E.: ‘Control design and implementation for high performance shunt active filters in aircraft power grids’, IEEE Trans. Ind. Electron., 2012, 59, (9), pp. 3604–3613 (doi: 10.1109/TIE.2011.2165454).
-
31)
-
9. Meng, D., Jia, Y., Du, J., Yu, F.: ‘Data-driven control for relative degree systems via iterative learning’, IEEE Trans. Neural Netw., 2011, 22, (12), pp. 2213–2225 (doi: 10.1109/TNN.2011.2174378).
-
32)
-
2. Wu, B., Poh, E.K., Wang, D., Xu, G.: ‘Satellite formation keeping via real-time optimal control and iterative learning control’. Proc. of the IEEE Aerospace Conf., Big Sky, MT, USA, 7–14 March 2009, pp. 1–8.
-
33)
-
32. Hu, J., Xiao, Z., Zhou, Y., Yu, J.: ‘Formation control over antagonistic networks’. Proc. of the Chinese Control Conf., Xi’an, China, 26–28 July 2013, pp. 6879–6884.
-
34)
-
2. Shi, J.T., He, X., Wang, Z.D., Zhou, D.H.: ‘Iterative consensus for a class of second-order multi-agent systems’, J. Intell. Robot. Syst., 2014, 73, (1–4), pp. 655–664 (doi: 10.1007/s10846-013-9996-2).
-
35)
-
5. Luo, A., Xu, X., Fang, L., Fang, H., Wu, J., Wu, C.: ‘Feedbackfeedforward PI-type iterative learning control strategy for hybrid active power filter with injection circuit’, IEEE Trans. Ind. Electron., 2010, 57, (11), pp. 3767–3779 (doi: 10.1109/TIE.2010.2040567).
-
36)
-
23. Meng, D., Jia, Y., Du, J., Zhang, J.: ‘On iterative learning algorithms for the formation control of nonlinear multi-agent systems’, Automatica, 2014, 50, (1), pp. 291–295 (doi: 10.1016/j.automatica.2013.11.009).
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