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Mixed time/event-triggered distributed predictive control over wired-wireless networks. (English) Zbl 1367.93048

Summary: Communicating via mixed wired-wireless connections is the development trend for large-scale distributed control systems. In this communication environment, due to the limited wireless resources, the communication patterns of subsystems have changed fundamentally, and the design of time-triggered and event-triggered distributed controllers should be taken into account simultaneously. The objective of this paper is to investigate mixed time/event-triggered dual-mode Distributed Predictive Control (DPC) for constrained large-scale linear systems subject to bounded disturbances. Considering the effects of two different communication modes and introducing a prediction error between the current actual state and predicted state, the event-triggering condition is derived for each event-triggered subsystem. Based on this, a mixed time/event-triggered dual-mode DPC algorithm is proposed in view of the asynchronous coordination among subsystems. Furthermore, sufficient conditions to ensure the recursive feasibility and closed-loop stability of mixed triggered DPC are developed. Finally, a multi-vehicle control system is provided to verify the effectiveness of the proposed approach.

MSC:

93A15 Large-scale systems
90B18 Communication networks in operations research
93C65 Discrete event control/observation systems
93B40 Computational methods in systems theory (MSC2010)
93C73 Perturbations in control/observation systems
93D15 Stabilization of systems by feedback
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References:

[1] Zhang, J.; Liu, J., Distributed moving horizon state estimation for nonlinear systems with bounded uncertainties, J. Process Control, 23, 9, 1281-1295 (2013)
[2] Zhao, J.; Dorfler, F., Distributed control and optimization in DC microgrids, Automatica, 61, 18-26 (2015) · Zbl 1327.93302
[3] Li, J.; Ho, D. W.C.; Li, J., Distributed adaptive repetitive consensus control framework for uncertain nonlinear leader-follower multi-agent systems, J. Frankl. Inst., 352, 11, 5342-5360 (2015) · Zbl 1395.93016
[4] Scattolini, R., Architectures for distributed and hierarchical model predictive control-a review, J. Process Control, 19, 5, 723-731 (2009)
[5] Song, Y.; Liu, S.; Wei, G., Constrained robust distributed model predictive control for uncertain discrete-time Markovian jump linear system, J. Frankl. Inst., 352, 1, 73-92 (2015) · Zbl 1307.93386
[6] Ding, B.; Xie, L.; Cai, W., Distributed model predictive control for constrained linear systems, Int. J. Robust Nonlinear Control, 20, 11, 1285-1298 (2010) · Zbl 1200.93037
[7] Gao, Y.; Xia, Y.; Dai, L., Cooperative distributed model predictive control of multiple coupled linear systems, IET Control Theory Appl., 9, 17, 2561-2567 (2015)
[8] Wang, C.; Ong, C., Distributed model predictive control of dynamically decoupled systems with coupled cost, Automatica, 46, 12, 2053-2058 (2010) · Zbl 1205.93132
[9] Li, H.; Shi, Y., Robust distributed model predictive control of constrained continuous-time nonlinear systemsa robustness constraint approach, IEEE Trans. Autom. Control, 59, 6, 1673-1678 (2014)
[10] Du, D.; Qi, B.; Fei, M.; Peng, C., Multiple event-triggered \(H_2 / H_\infty\) filtering for hybrid wired-wireless networked systems with random network-induced delays, Inf. Sci., 325, 393-408 (2015) · Zbl 1391.94201
[13] Hashimoto, K.; Adachi, S.; Dimarogonas, D. V., Distributed aperiodic model predictive control for multi-agent systems, IET Control Theory Appl., 9, 1, 10-20 (2015)
[14] Li, H.; Yan, W.; Shi, Y.; Wang, Y., Periodic event-triggering in distributed receding horizon control of nonlinear systems, Syst. Control Lett., 86, 16-23 (2015) · Zbl 1325.93042
[15] Willig, A.; Matheus, K.; Wolisz, A., Wireless technology in industrial networks, Proc. IEEE, 93, 6, 1130-1151 (2005)
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