Michael Schönlein

Uniform ensemble controllability for one-parameter families of time-invariant linear systems ☆

Abstract In this paper we derive necessary as well as sufficient conditions for approximate controllability of parameter-dependent linear systems in the supremum norm. Using tools from complex approximation theory, we prove the existence of parameter-independent open-loop controls that steer the zero initial state of an ensemble of linear systems uniformly to a prescribed family of terminal states. New necessary conditions for uniform ensemble controllability of single-input systems are derived. Our results extend earlier ones of Li for ensemble controllability of linear systems.

Estimation of linear positive systems with unknown time-varying delays

Abstract This paper considers the estimation problem for linear positive systems with time-varying unknown delays. Similar to set-valued estimation approaches, we provide a confident region within which the trajectory of the observed positive system always evolves. Guaranteed upper and lower estimates for the instantaneous states are characterized by means of a special kind of extended Luenberger-type interval observer. We provide constructive conditions for its existence and establish the asymptotic convergence of its associated interval error. In addition, we give an LP-based method which allows one to construct the proposed interval observer solely from the data of the system.

Measurement and optimization of robust stability of multiclass queueing networks: Applications in dynamic supply chains

Multiclass queueing networks are an essential tool for modeling and analyzing complex supply chains. Roughly speaking, stability of these networks implies that the total number of customers/jobs in the network remains bounded over time. In this context robustness characterizes the ability of a multiclass queueing network to remain stable, if the expected values of the interarrival and service times distributions are subject to uncertain shifts. A powerful starting point for the stability analysis of multiclass queueing networks is the associated fluid network. Based on the fluid network analysis we present a measure to quantify the robustness, which is indicated by a single number. This number will be called the stability radius. It represents the magnitude of the smallest shift of the expected value of the interarrival and/or service times distributions so that the associated fluid network looses the property of stability. The stability radius is a worst case measure and is a conceptual adaptation from the dynamical systems literature. Moreover, we provide a characterization of the shifts that destabilize the network. Based on these results, we formulate a mathematical program that minimizes the required network capacity, while ensuring a desired level of robustness towards shifts of the expected values of the interarrival times distributions. This approach provides a new view on long-term robust production capacity allocation in supply chains. The capabilities of our method are demonstrated using a real world supply chain.

On converse Lyapunov theorems for fluid network models

We consider the class of closed generic fluid network (GFN) models, which provides an abstract framework containing a wide variety of fluid networks. Within this framework a Lyapunov method for stability of GFN models was proposed by Ye and Chen. They proved that stability of a GFN model is equivalent to the existence of a functional on the set of paths that is decaying along paths. This result falls short of a converse Lyapunov theorem in that no state-dependent Lyapunov function is constructed. In this paper we construct state-dependent Lyapunov functions in contrast to path-wise functionals. We first show by counterexamples that closed GFN models do not provide sufficient information that allow for a converse Lyapunov theorem. To resolve this problem we introduce the class of strict GFN models by forcing closed GFN models to satisfy a concatenation and a semicontinuity condition. For the class of strict GFN models we define a state-dependent Lyapunov function and show that a converse Lyapunov theorem holds. Finally, it is shown that common fluid network models, like general work-conserving and priority fluid network models as well as certain linear Skorokhod problems define strict GFN models.

Some Remarks on Stability and Robustness of Production Networks Based on Fluid Models

The dynamics of complex, large-scale production networks present an important issue not only for the management of such networks but also for scientific research. This problem is usually investigated either by numerical simulations or by mathematical analysis of the associated queueing model. One major property of stable production networks is the robustness of these networks with respect to perturbations of the arrival process of jobs from outside. Given a generic structure of the considered network with several production locations, different products and re-entrant material flows the determination of robustness is non trivial. In this paper we use a fluid model approach to analyse the robustness of queueing networks. First conditions for stability of a fluid network are introduced. These conditions allow to assess the dynamic behavior of the production network. Second the obtained results are investigated with the help of a simulation of the fluid and queueing network. Simulations of a test case scenario accompany the results of the theoretical analysis.

A comparison of mathematical modelling approaches for stability analysis of supply chains

Production and transportation processes along a supply chain are dynamic. In particular they are subject to perturbations (e.g., breakdown of a resource) that can destabilise the network. Stability is a major property of a supply chain that is essential for a sustainable relationship to its customers. In order to verify the stability of a given supply chain different criteria have been developed. This paper addresses the problem of choosing a proper mathematical modelling approach for a real world network in order to investigate stability. For this reason we discuss different modelling approaches. Each of these approaches can model different characteristics of a supply chain and features a specific stability criterion. By comparing these approaches the paper supports choosing a proper modelling approach for a real world supply chain.

Control of Ensembles of Single-Input Continuous-Time Linear Systems

Abstract In this paper we consider continuous-time linear time-invariant single-input systems that depend on a single parameter. Given a family of desired states we aim to find open-loop control inputs which are independent of the parameter that simultaneously steer the zero initial state arbitrarily close to a family of terminal states. As an application we show that a family of networks of harmonic oscillators can be robustly controlled by a single input function. Thus our goal is to effectively compute open-loop input functions that are cabable of controlling an ensemble of linear systems.

Controllability of ensembles of linear dynamical systems

We investigate the task of controlling ensembles of initial and terminal state vectors of parameter-dependent linear systems by applying parameter-independent open loop controls. Necessary, as well as sufficient, conditions for ensemble controllability are established, using tools from complex approximation theory. For real analytic families of linear systems it is shown that ensemble controllability holds only for systems with at most two independent parameters. We apply the results to networks of linear systems and address the question of open-loop robust synchronization.

A Lyapunov view on positive Harris recurrence of multiclass queueing networks

In this paper we prove the positive Harris recurrence of the underlying Markov process of a multiclass queueing network. Using the fact that under certain conditions the stability of a fluid network is equivalent to the existence of a continuous Lyapunov function, we propose a new method to conclude that the underlying Markov process is positive Harris recurrent if the associated fluid network model is stable by using explicitly the Lyapunov function of the fluid network.

An Approach to Model Reduction of Logistic Networks Based on Ranking

Simulations or mathematical analysis of a real-world logistic network require a model. In this context two challenges occur for modelling: First, the model should represent the real-world logistic network in an accurate way. Second, it should foster simulations or analytical analysis to be conducted in a reasonable time. A large size is often a drawback of many models. In the case of logistic networks this drawback can be overcome by reducing the number of locations and transportation links of the graph model. In this paper we present an approach to model reduction of a logistic network based on ranking. The rank of a given location states the importance of the location for the whole network. In order to calculate the importance of a location we introduce an adaptation of the PageRank algorithm for logistic networks. The information about the rank and the structural relations between the locations are used for our approach to model reduction. Depending on the structural relation between locations we suggest three different approaches to obtain a model of lower size.