Incremental Gain Computation and Regulation of Discrete-time Positive Lur\'e Systems using Linear Programming

Abstract

This work approaches the problem of computing incremental $\ell_1$ and $\ell_\infty$ gains for discrete-time positive systems in \lure feedback with static memoryless nonlinearities, and regulating the $\ell_\infty$ gain through the design of a state-feedback controller. Finite incremental gains provide a quantitative measure of robustness for trajectories, and will ensure that all pairs of trajectories will converge to a fixed point or will diverge together in the absence of an applied input. Upper-bounds on these incremental gains can be computed through linear programming. Computation and regulation of the $\ell_1$ and $\ell_\infty$ incremental gains are verified by numerical examples.