© The Institution of Engineering and Technology
This work explores the consensus problems under the directed graph, variable learning gains, fast convergence and datadriven control framework comprehensively and proposes an adaptive estimationbased terminal iterative learning control for a nonlinear discretetime multiagent system (MAS) with a constant control input. A linear iterationincremental model is built by using an iterative dynamic linearisation where the unknown partial derivatives are estimated iteratively using I/O data. The learning control law is designed with both a constant learning gain and an iterationtimevarying learning gain. The constant one can be selected properly according to the estimation of partial derivatives and the varying one can be estimated from iteratively utilising I/O data. The result has also been extended to the nonlinear MAS with timevarying control input and an extended adaptive estimationbased TILC is developed by using timevarying control input to enhance the control performance. A fast convergence of both the proposed methods is achieved by removing the unnecessary error constraints at other time instants than the endpoint. Both the proposed methods is apparently datadriven since no model information is involved. The proposed finite time consensus control methods are confirmed to be effective under the directed graph through mathematic proof and extensive simulations.
References


1)

1. Zhang, H.T., Chen, Z., Yan, L.: ‘Applications of collective circular motion control to multirobot systems’, IEEE Trans. Control Syst. Technol., 2013, 21, (4), pp. 1416–1422.

2)

2. Fax, J.A., Murray, R.M.: ‘Information flow and cooperative control of vehicle formations’, IEEE Trans. Autom. Control, 2004, 49, (9), pp. 1465–1476.

3)

3. Kang, W., Yeh, H.H.: ‘Coordinated attitude control of multisatellite systems’, Int. J. Robust Nonlinear Control, 2002, 12, (2), pp. 185–205.

4)

4. Cao, Y.C., Ren, W.: ‘Finitetime consensus for multiagent networks with unknown inherent nonlinear dynamics’, Automatica, 2014, 50, pp. 2648–2656.

5)

5. Dimarogonas, D., Frazzoli, E., Johansson, K.: ‘Distributed eventtriggered control for multiagent systems’, IEEE Trans. Autom. Control, 2012, 57, pp. 1291–1297.

6)

6. Hu, W., Liu, L., Feng, G.: ‘Consensus of linear multiagent systems by distributed eventtriggered strategy’, IEEE Trans. Cybern., 2016, 46, pp. 148–157.

7)

7. He, X., Wang, Q.: ‘Distributed finitetime leaderless consensus control for doubleintegrator multiagent systems with external disturbances’, Appl. Math. Comput., 2017, 295, pp. 65–67.

8)

8. Cao, Z., Li, C., Wang, X., et al: ‘Finitetime consensus of linear multiagent system via distributed eventtriggered strategy’, J. Franklin Inst., 2018, 355, pp. 1338–1350.

9)

9. Zhang, H., Yue, D., Yin, X., et al: ‘Finitetime distributed eventtriggered consensus control for multiagent systems’, Inf. Sci., 2016, 339, pp. 132–142.

10)

10. Ahn, H.S., Moore, K.L., Chen, Y.: ‘Trajectorykeeping in satellite formation flying via robust periodic learning control’, Int. J. Robust Nonlinear Control, 2010, 20, (14), pp. 1655–1666.

11)

11. Yufka, A., Parlaktuna, O., Ozkan, M.: ‘Formationbased cooperative transportation by a group of nonholonomic mobile robots’, IEEE Int. Conf. Syst. Man Cybern., 2010, 12, (4), pp. 3300–3307.

12)

12. Arimoto, S., Kawamura, S., Miyazaki, F.: ‘Bettering operation of robots by learning’, J. Robot Syst., 1984, 1, (2), pp. 123–140.

13)

13. Chi, R.H., Hou, Z.S.: ‘Modelfree periodic adaptive control for a class of SISO nonlinear discretetime systems’. 8th IEEE Int. Conf. on Control and Automation, Xiamen, China, June 2010, pp. 9–11.

14)

14. Ahn, H.S., Chen, Y.Q.: ‘Iterative learning control for multiagent formation’. ICROSSICE Int. Joint Conf., Japan, August 2009, pp. 18–21.

15)

15. Liu, Y., Jia, Y.: ‘An iterative learning approach to formation control of multiagent systems’, Syst. Control Lett., 2012, 61, pp. 148–154.

16)

16. Yang, S., Xu, J.X., Huang, D.Q., et al: ‘Optimal iterative learning control design for multiagent systems consensus tracking’, Syst. Control Lett., 2014, 69, pp. 80–89.

17)

17. Yang, S., Xu, J.X., Li, X.: ‘Iterative learning control with input sharing for multiagent consensus tracking’, Syst. Control Lett., 2016, 94, pp. 97–106.

18)

18. Meng, D., Jia, Y., Du, J., et al: ‘On iterative learning algorithms for the formation control of nonlinear multiagent systems’, Automatica, 2014, 50, pp. 291–295.

19)

19. Li, J.M., Li, J.S.: ‘Adaptive fuzzy iterative learning control with initialstate learning for coordination control of leaderfollowing multiagent systems’, Fuzzy Sets Syst., 2014, 248, pp. 122–137.

20)

20. Li, J.M., Li, J.S.: ‘Coordination control of multiagent systems with secondorder nonlinear dynamics using fully distributed adaptive iterative learning’, J. Franklin Inst., 2015, 352, pp. 2441–2463.

21)

21. Jin, X.: ‘Adaptive iterative learning control for highorder nonlinear multiagent systems consensus tracking’, Syst. Control Lett., 2016, 89, pp. 16–23.

22)

22. Maupong, T.M., Rapisarda, P.: ‘Datadriven control: a behavioral approach’, Syst. Control Lett., 2017, 101, pp. 37–43.

23)

23. Chi, R.H., Hou, Z.S., Huang, B.: ‘Optimal iterative learning control of batch processes: from modelbased to datadriven’, Acta Autom. Control, 2017, 43, (6), pp. 917–932.

24)

24. Tanaskovic, M., Fagiano, L., Novara, C., et al: ‘Datadriven control of nonlinear systems: an online direct approach’, Automatica, 2017, 75, (1), pp. 1–10.

25)

25. Hou, Z.S., Chi, R.H., Gao, H.: ‘An overview of dynamic linearization based datadriven control and applications’, IEEE Trans. Ind. Electron., 2017, 64, (5), pp. 4076–4090.

26)

26. Xu, J.X., Chen, Y., Lee, T.H., et al: ‘Terminal iterative learning control with an application to RTPCVD thickness control’, Automatica, 1999, 35, pp. 1535–1542.

27)

27. Han, J., Shen, D., Chien, C.J.: ‘Terminal iterative learning control for discretetime nonlinear systems based on neural networks’, J. Franklin Inst., 2018, 355, pp. 3641–3658.

28)

28. Chi, R.H., Wang, D.W., Hou, Z.S., et al: ‘Datadriven optimal terminal iterative learning control’, J. Process Control, 2012, 22, pp. 2026–2037.

29)

29. Chi, R.H., Hou, Z.S., Huang, B., et al: ‘A unified datadriven design framework of optimalitybased generalized iterative learning control’, Comput. Chem. Eng., 2015, 77, pp. 10–23.

30)

30. Chi, R.H., Lin, N., Zhang, R.K., et al: ‘Stochastic highorder internal modelbased adaptive TILC with random uncertainties in initial states and desired reference points’, Int. J. Adapt. Control Signal Process., 2017, 31, (5), pp. 726–741.

31)

31. Chi, R.H., Hou, Z.S., Jin, S.T., et al: ‘Enhanced datadriven optimal terminal ILC using current iteration control knowledge’, IEEE Trans. Neural Netw. Learn. Syst., 2015, 26, (11), pp. 2939–2948.

32)

32. Chi, R.H., Liu, Y., Hou, Z.S., et al: ‘Datadriven terminal iterative learning control with highorder learning law for a class of nonlinear discretetime multipleinput–multiple output systems’, IET Control Theory Appl., 2015, 9, (7), pp. 1075–1082.

33)

33. Chi, R.H., Huang, B., Wang, D.W., et al: ‘Datadriven optimal terminal iterative learning control with initial value dynamic compensation’, IET Control Theory Appl., 2016, 10, (12), pp. 1357–1364.

34)

34. Chi, R.H., Hou, Z.S., Jin, S.T., et al: ‘Improved datadriven optimal TILC using timevarying input signals’, J. Process Control, 2014, 24, pp. 78–85.

35)

35. Meng, D., Jia, Y.: ‘Iterative learning approaches to design finitetime consensus protocols for multiagent systems’, Syst. Control Lett., 2012, 61, pp. 187–194.

36)

36. Sun, M., Wang, D.: ‘Closedloop iterative learning control for nonlinear systems with initial shifts’, Int. J. Adapt. Control Signal Process., 2002, 16, (7), pp. 515–538.
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