Stability analysis of nonlinear sampled-data systems via causal linear periodically time-varying scaling

This paper is concerned with causal linear periodically time-varying (LPTV) scaling for stability analysis. The effectiveness of such a scaling technique was first shown for the robust stability analysis of linear sampled-data systems, and it was then extended to a more general technique called noncausal LPTV scaling. Even though noncausal LPTV scaling has been shown to be more effective than causal LPTV scaling in such a context, this paper aims at showing that causal LPTV scaling can be applied even to sampled-data systems with sector nonlinearities. A numerical example is also studied to compare the effectiveness with another method based on the conventional multiplier technique.