Hybrid [formula omitted]-performance analysis and control of linear time-varying impulsive and switched positive systems

Abstract

Recent works have shown that the L1 and L∞-gains are natural performance criteria for linear positive systems as they can be exactly characterized by linear programs. Those performance measures have also been extended to linear positive impulsive and switched systems through the concept of hybrid L1×ℓ1-gain. For LTI positive systems, the L∞-gain is known to coincide with the L1-gain of the transposed system and, as a consequence, one can use linear copositive Lyapunov functions for characterizing the L∞-gain of LTI positive systems. Unfortunately, this does not hold in the time-varying setting and one cannot characterize the hybrid L∞×ℓ∞-gain of a linear positive impulsive system in terms of the hybrid L1×ℓ1-gain of the transposed system. To solve this, an approach based on the use of linear copositive max-separable Lyapunov functions is proposed. We first prove very general necessary and sufficient conditions characterizing the exponential stability and the L∞×ℓ∞- and L1×ℓ1-gains using linear max-separable copositive and linear sum-separable copositive Lyapunov functions. These two results are then connected together using operator theoretic results and the notion of adjoint system. Results characterizing the stability and the hybrid L∞×ℓ∞-gain of linear positive impulsive systems under arbitrary, constant, minimum, and range dwell-time constraints are then derived from the previously obtained general results. These conditions are then exploited to yield constructive convex stabilization conditions via state-feedback. By reformulating linear positive switched systems as impulsive systems with multiple jump maps, stability and stabilization conditions are also obtained for linear positive switched systems. It is notably proven that the obtained conditions generalize existing ones of the literature. As all the results are stated as infinite-dimensional linear programs, sum of squares programming is used to turn those optimization problems into sufficient tractable finite-dimensional semidefinite programs. Interestingly, the relaxation becomes necessary if we allow the degrees of the polynomials to be arbitrarily large. Several particular cases of the approach such as LTV positive systems and periodic positive systems are also discussed for completeness. Examples are given for illustration.