SUMMARYIn this paper, a solution to the approximate tracking problem of sampled-data systems with uncertain, time-varying sampling intervals and delays is presented. Such time-varying sampling intervals and delays can typically occur in the field of networked control systems. The uncertain, time-varying sampling and network delays cause inexact feedforward, which induces a perturbation on the tracking error dynamics, for which a model is presented in this paper. Sufficient conditions for the input-to-state stability (ISS) of the tracking error dynamics with respect to this perturbation are given. Hereto, two analysis approaches are developed: a discrete-time approach and an approach in terms of delay impulsive differential equations. These ISS results provide bounds on the steady-state tracking error as a function of the plant properties, the control design and the network properties. Moreover, it is shown that feedforward preview can significantly improve the tracking performance and an online extremum seeking (nonlinear programming) algorithm is proposed to online estimate the optimal preview time. The results are illustrated on a mechanical motion control example showing the effectiveness of the proposed strategy and providing insight into the differences and commonalities between the two analysis approaches.
We consider the stabilization problem for Networked Control Systems (NCSs) with uncertain, time-varying network-induced delays and a bounded number of subsequent packet dropouts. A discrete-time model, describing a NCS with packet dropouts and time-varying delays, that can be both smaller and larger than the sampling interval, is presented. Based on this NCS model sufficient LMI conditions are proposed for the stability analysis and controller synthesis problem for two different controllers, i.e. a feedback controller that depends on both the state and the past control inputs and a state-feedback controller. The applicability of both controllers is compared. Moreover, the stability and controller synthesis LMIs allow for a performance analysis in terms of a lower bound for the transient decay rate of the response. The results are illustrated by application to a typical motion control example.
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