Fabio Fagnani

Feedback Control Under Data Rate Constraints: An Overview

The emerging area of control with limited data rates incorporates ideas from both control and information theory. The data rate constraint introduces quantization into the feedback loop and gives the interconnected system a two-fold nature, continuous and symbolic. In this paper, we review the results available in the literature on data-rate-limited control. For linear systems, we show how fundamental tradeoffs between the data rate and control goals, such as stability, mean entry times, and asymptotic state norms, emerge naturally. While many classical tools from both control and information theory can still be used in this context, it turns out that the deepest results necessitate a novel, integrated view of both disciplines

Randomized consensus algorithms over large scale networks

Suppose we have a directed graph G with set of nodes V = {1,...,N} and a measure xi for every node i euro V. The average consensus problem consists in computing the average xA= N-1Sigmai xi in an iterative way, exchanging information among nodes exclusively along the available edges in G. This problem appears in a number of different contexts since the 80s (decentralized computation, load balancing, clock syncronization) and, recently, has attracted much attention for possible applications to sensor networks (data fusion problems) and to coordinated control for mobile autonomous agents. Several algorithms for average consensus can be found in the literature: they differentiate on the basis of the amount of communication and computation they use, on their scalability with respect to the number of nodes, on their adaptability to time-varying graphs, and, finally, they can be deterministic or random. In this presentation we will focus on random algorithms: we will review some algorithms present in the literature and we will propose some new ones. We will present some performance results which will allow to make some comparison. Finally, we will establish some probabilistic concentration results which will give a stronger significance to previous results.

Communication constraints in the average consensus problem

The interrelationship between control and communication theory is becoming of fundamental importance in many distributed control systems, such as the coordination of a team of autonomous agents. In such a problem, communication constraints impose limits on the achievable control performance. We consider as instance of coordination the consensus problem. The aim of the paper is to characterize the relationship between the amount of information exchanged by the agents and the rate of convergence to the consensus. We show that time-invariant communication networks with circulant symmetries yield slow convergence if the amount of information exchanged by the agents does not scale well with their number. On the other hand, we show that randomly time-varying communication networks allow very fast convergence rates. We also show that by adding logarithmic quantized data links to time-invariant networks with symmetries, control performance significantly improves with little growth of the required communication effort.

Stability analysis and synthesis for scalar linear systems with a quantized feedback

It is well known that a linear system controlled by a quantized feedback may exhibit the wild dynamic behavior which is typical of a nonlinear system. In the classical literature devoted to control with quantized feedback, the flow of information in the feedback was not considered as a critical parameter. Consequently, in that case, it was natural in the control synthesis to simply choose the quantized feedback approximating the one provided by the classical methods, and to model the quantization error as an additive white noise. On the other hand, if the flow of information has to be limited, for instance, because of the use of a transmission channel with limited capacity, some specific considerations are in order. The aim of this paper is to obtain a detailed analysis of linear scalar systems with a stabilizing quantized feedback control. First, a general framework based on a sort of Lyapunov approach encompassing known stabilization techniques is proposed. In this case, a rather complete analysis can be obtained through a nice geometric characterization of asymptotically stable closed-loop maps. In particular, a general tradeoff relation between the number of quantization intervals, quantifying the information flow, and the convergence time is established. Then, an alternative stabilization method, based on the chaotic behavior of piecewise affine maps is proposed. Finally, the performances of all these methods are compared.

Average Consensus with Packet Drop Communication

The average consensus consists in determining the average of a number of quantities by means of a distributed algorithm. This is a simple instance of the problems which need to be faced when developing algorithms for the estimation of quantities from measures produced by sensor networks. Simple solutions based on linear estimators has already been proposed in the literature and their performance has been analyzed in detail. In this contribution the performance decay caused by data exchange through failing links is evaluated. In the model of the link proposed here at every time instant there is a certain probability that the data transmitted is lost by the link

Quantized stabilization of linear systems: complexity versus performance

Quantized feedback control has been receiving much attention in the control community in the past few years. Quantization is indeed a natural way to take into consideration in the control design the complexity constraints of the controller as well as the communication constraints in the information exchange between the controller and the plant. In this paper, we analyze the stabilization problem for discrete time linear systems with multidimensional state and one-dimensional input using quantized feedbacks with a memory structure, focusing on the tradeoff between complexity and performance. A quantized controller with memory is a dynamical system with a state space, a state updating map and an output map. The quantized controller complexity is modeled by means of three indexes. The first index L coincides with the number of the controller states. The second index is the number M of the possible values that the state updating map of the controller can take at each time. The third index is the number N of the possible values that the output map of the controller can take at each time. The index N corresponds also to the number of the possible control values that the controller can choose at each time. In this paper, the performance index is chosen to be the time T needed to shrink the state of the plant from a starting set to a target set. Finally, the contraction rate C, namely the ratio between the volumes of the starting and target sets, is introduced. We evaluate the relations between these parameters for various quantized stabilizers, with and without memory, and we make some comparisons. Then, we prove a number of results showing the intrinsic limitations of the quantized control. In particular, we show that, in order to obtain a control strategy which yields arbitrarily small values of T/lnC (requirement which can be interpreted as a weak form of the pole assignability property), we need to have that LN/lnC is big enough.

System-theoretic properties of convolutional codes over rings

Convolutional codes over rings are particularly suitable for representing codes over phase-modulation signals. In order to develop a complete structural analysis of this class of codes, it is necessary to study rational matrices over rings, which constitutes the generator matrices (encoders) for such convolutional codes. Noncatastrophic, minimal, systematic, and basic generator matrices are introduced and characterized by using a canonical form for polynomial matrices over rings. Finally, some classes of convolutional codes, defined according to the generator matrix they admit, are introduced and analyzed from a system-theoretic point of view.

An Eulerian Approach to the Analysis of Krause's Consensus Models

In this paper we analyze a class of multiagent consensus dynamical systems inspired by Krauses original model. As in Krauses model, the basic assumption is the so-called bounded confidence: two agents can influence each other only when their state values are below a given distance threshold

Communication constraints in coordinated consensus problems

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Average consensus by gossip algorithms with quantized communication

. We study the system under an Eulerian point of view considering (possibly continuous) probability distributions of agents, and we present original convergence results. The limit distribution is always necessarily a convex combination of delta functions at least