Stability conditions for infinite networks of nonlinear systems and their application for stabilization

We introduce a new concept of ℓ∞-input-to-state stability for infinite networks composed of a countable set of interconnected nonlinear subsystems of ordinary differential equations. We suppose that the entire state vector is an element of ℓ∞ and each subsystem is input-to-state stable whereas the dimension of its entire disturbance input including possible interconnections with other subsystems is finite. Our first main result provides conditions for ℓ∞-input-to-state stability of such infinite-dimensional networks. In our second main result, we solve the problem of decentralized ℓ∞-ISS stabilization for such networks composed of interconnected lower-triangular form subsystems with uncontrollable linearization. To apply our first main result and obtain the second one, we construct a feedback for each individual agent, which satisfies our new stability conditions. This yields the stabilization of the entire network. Our design is also new for finite networks and this can be considered as an important special case

Decentralized uniform input-to-state stabilization of hierarchically interconnected triangular switched systems with arbitrary switchings

We consider a class of large-scale systems composed of hierarchically interconnected switched nonlinear triangular form subsystems affected by external disturbances with arbitrarily varying switching signals. For any system of this class, we design a decentralized feedback controller which renders the entire large-scale closed-loop system globally ISS with respect to the external disturbances uniformly and regardless of the unknown switching signals. To solve the problem, we use a certain modification of the classical small gain theorems formulated in terms of the ISS Lyapunov functions and combine it with our version of the backstepping approach with a suitable gain assignment. (C) 2018 Elsevier Ltd. All rights reserved

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp