Adaptive control for the nonlinear event-triggered networked control system: a decoupling method

In this paper, the adaptive control for a kind of nonlinear system over the event-driven sensors-controller communication network is addressed. Over this network, the existing strategy has been developed. However, the event-triggered mechanism and adaptive estimators are coupled together, which results in extra information delivery of the adaptive estimators over the network or extra computation equipment on the sensor side. To overcome this limitations, two kinds of adaptive control strategies are proposed in this paper. At first, the control strategy with the new event-triggered adaptive estimator is developed for the system without external disturbances where the event-triggered mechanism does not need the information of the adaptive estimators. It is proved that the closed-loop system is asymptotically stable and the Zeno behaviour is excluded. Furthermore, for the system with external disturbances, a robust control scheme with modified adaptive estimator and event-triggered mechanism is proposed and the practical stability of the closed-loop system is guaranteed. Also, the Zeno behaviour is excluded carefully. Comparing with the existing results, due to the decoupling structure of event-triggered mechanism and adaptive estimators, it is unnecessary to transfer the estimators' information to the event-triggered mechanism on the sensor side, which saves the communication cost. What's more, although the system state is sampled and event driven, the asymptotical stability is still guaranteed. Also, the developed strategy can handle with the external disturbances. Finally, two examples are performed to illustrate the effectiveness of the schemes and some comparisons are displayed to explain the advantages of the methods.

Automated summary

This paper addresses the adaptive control for a kind of nonlinear system over the event-driven sensors-controller communication network. Two kinds of adaptive control strategies are proposed to overcome the limitations of the existing strategy. The decoupling structure of event-triggered mechanism and adaptive estimators saves communication cost and ensures the asymptotical stability of the system.