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We address problems where the structure of each agent's resource allocation function is unknown but depends on the entire resource allocation vector. We incorporate dynamics and tackle this problem for a dynamic system. A cooperative approach that permits communication between neighbouring agents to minimize the unknown overall resource allocation function subject to the resource requirement is utilized.

A simulation example is included to show the effectiveness of the proposed technique. Keywords: Agents-based systems , Cooperative control , Distributed control Abstract: Output containment is achieved among linear heterogeneous multi-agent systems MAS thanks to the collaborative efforts of all agents, expressed by a communication digraph with nonnegative weights.

This paper investigates the output containment of heterogeneous non-introspective MAS on signed digraphs, in the presence of uncertain parameter variations.

Agents on signed digraphs can have antagonistic interactions, modeled as negative weights on the communication network. To this end, we first formulate a new control problem called the robust bipartite output containment problem RBOC , which aims at making each follower's output converge to the dynamic convex hull spanned by the outputs and the negative outputs of the leaders.


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It is proved that the RBOC problem can be solved by making certain signed output containment errors go to zero asymptotically. Then, a dynamic output-feedback control protocol is designed based on internal model principles. Finally, local sufficient conditions are obtained and explicit local design procedures are provided. Numerical simulations are performed to illustrate the proposed RBOC performances on signed communication digraphs. Keywords: Agents-based systems , Cooperative control , Lyapunov methods Abstract: This paper studies H inf almost state and output synchronizations of homogeneous multi-agent systems MAS with partial-state coupling with general linear agents affected by external disturbances.

We will characterize when static linear protocols can be designed for state or output synchronization for a MAS such that the impact of disturbances on the network disagreement dynamics, expressed in terms of the H inf norms of the corresponding closed-loop transfer function is reduced to any arbitrarily small value. Meanwhile, the static protocol only needs rough information on the network graph, that is a lower bound for the real part and an upper bound for the modulus of the non-zero eigenvalues of the Laplacian matrix associated with the network graph.


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Our study focuses on three classes of agents which are squared-down passive, squared-down passifiable via output feedback and squared-down minimum phase with relative degree 1. Keywords: Agents-based systems Abstract: Game with second-order dynamic agents is explored for the case where the payoff function of each agent is concave and continuously differentiable. Several control laws that stabilize the Nash equilibrium by utilizing only the knowledge of each agent's payoff function are proposed. We use the results from convex optimization to derive our control laws.

We illustrate our approach with a numerical example. Existing planning approaches with LTL specifications rely on graph search techniques applied to a product automaton constructed among the robots. In our previous work, we have proposed a more tractable sampling-based algorithm that builds incrementally trees that approximate the state-space and transitions of the synchronous product automaton and does not require sophisticated graph search techniques.

Here, we extend our previous work by introducing bias in the sampling process which is guided by transitions in the Buchi automaton that belong to the shortest path to the accepting states. This allows us to synthesize optimal motion plans from product automata with hundreds of orders more states than those that state-of-the-art methods can manipulate.

We also show that the proposed algorithm is probabilistically complete and asymptotically optimal. Keywords: Agents-based systems , Stability of nonlinear systems , Network analysis and control Abstract: In this paper we propose a novel method to establish stability and convergence to a consensus state for a class of nonlinear discrete-time Multi-Agent System MAS which is not based on Lyapunov function arguments.

In particular, we focus on a class of discrete-time multi-agent systems whose global dynamics can be represented by sub-homogeneous and order-preserving nonlinear maps. The preliminary results of this paper directly generalize results for sub-homogeneous and order-preserving linear maps which are shown to be the counterpart to stochastic matrices thanks to nonlinear Perron-Frobenius theory.

We provide sufficient conditions on local interaction rules among agents to establish convergence to a fixed point and study the consensus problem in this generalized framework as a particular case. Examples to show the effectiveness of the method are provided to corroborate the theoretical analysis. In these examples, some nonlinear interaction protocols are proved to converge to the consensus state without the use of Lyapunov functions. Keywords: Information technology systems , Quantized systems , Optimization Abstract: We study the problem of maximizing privacy of quantized sensor measurements by adding random variables.

In particular, we consider the setting where information about the state of a process is obtained using noisy sensor measurements. This information is quantized and sent to a remote station through an unsecured communication network. It is desired to keep the state of the process private; however, because the network is not secure, adversaries might have access to sensor information, which could be used to estimate the process state.

To avoid an accurate state estimation, we add random numbers to the quantized sensor measurements and send the sum to the remote station instead. The distribution of these random variables is designed to minimize the mutual information between the sum and the quantized sensor measurements for a desired level of distortion -- how different the sum and the quantized sensor measurements are allowed to be.

Simulations are presented to illustrate our results. Keywords: Information theory and control , Fault detection , Machine learning Abstract: Data-driven approaches are becoming increasingly crucial for modeling and performance monitoring of complex dynamical systems.

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Such necessity stems from complex interactions among sub-systems and high dimensionality that render majority of first-principle based methods insufficient. This paper explores the capability of a recently proposed probabilistic graphical modeling technique called spatiotemporal pattern network STPN in capturing Granger causality among observations in a dynamical system.

We numerically compare the two frameworks for a real-life anomaly detection problem involving an industrial robot platform. Keywords: Information theory and control , Network analysis and control , Biological systems Abstract: Reliable information processing is a hallmark of many physical and biological networked systems. In this paper, we propose a novel framework for modelling information transmission within a linear dynamical network.


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Information propagation is modelled by means of a digital communication protocol that takes into account the realistic phenomenon of inter-symbol interference. Building on this framework, we adopt Shannon information rate to quantify the amount of information that can be reliably sent over the network within a fixed time window. We investigate how the latter information metric is affected by the connectivity structure of the network.

Here, we focus in particular on networks characterized by a normal adjacency matrix. We show that for such networks the maximum achievable information rate depends only on the spectrum of the adjacency matrix. We then provide numerical results that suggest that matrix non-normality could benefit information transmission in dynamical networks. Keywords: Information theory and control , Control over communications , Stochastic systems Abstract: Bode integrals of sensitivity and sensitivity-like functions along with complementary sensitivity and complementary sensitivity-like functions are conventionally used for describing performance limitations of a feedback control system.

In this paper, we show that in the case when the disturbance is a wide sense stationary process the complementary sensitivity Bode integral and the complementary sensitivity-like Bode integral are identical. A lower bound of the continuous-time complementary sensitivity-like Bode integral is also derived and examined with the linearized flight-path angle tracking control problem of an F aircraft. Keywords: Information theory and control , Statistical learning , Control system architecture Abstract: Target localization is a critical task for mobile sensors and has many applications.

1. Introduction

However, generating informative trajectories for these sensors is a challenging research problem. A common method uses information maps that estimate the value of taking measurements from any point in the sensor state space. These information maps are used to generate trajectories; for example, a trajectory might be designed so its distribution of measurements matches the distribution of the information map. Regardless of the trajectory generation method, generating information maps as new observations is made is critical.

However, it can be challenging to compute these maps in real-time. We propose using convolutional neural networks to generate information maps from a target estimate and motion model in real-time. Simulations show that maps are accurately rendered while offering orders of magnitude reduction in computation time. Keywords: Emerging control applications , Information theory and control , Control over communications Abstract: It is known that the conventional second law of thermodynamics is not applicable to thermodynamic systems when feedback control is applied to such systems. A generalized form of the second law should be introduced in this case, which contains an additive term that describes the correlation between the microstates and the measurement outcomes.

In this study, we consider a situation where a linear stochastic thermodynamic system, which is in contact with a heat bath, is controlled over a noiseless digital channel to evaluate how channel capacity and control performance are interrelated considering the second law of thermodynamics. We show that in this case, the second law of thermodynamics is inclusive of a term that represents channel capacity.

We then show that given a fixed value of free energy difference, we can extract a larger amount of work from the system and obtain higher control performance if more channel capacity is used, in the case where an optimal controller and a proper encoder are used in the control system. Keywords: Distributed control , Autonomous systems , Control of networks Abstract: In this paper we consider the problem of a multi-agent system achieving a formation in the presence of misbehaving or adversarial agents. We introduce a novel continuous time resilient controller to guarantee that normally behaving agents can converge to a formation with respect to a set of leaders.

The controller employs a norm-based filtering mechanism, and unlike most prior algorithms, also incorporates input bounds. In addition, the controller is shown to guarantee convergence in finite time. A sufficient condition for the controller to guarantee convergence is shown to be a graph theoretical structure which we denote as Resilient Directed Acyclic Graph RDAG. Further, we employ our filtering mechanism on a discrete time system which is shown to have exponential convergence.

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Our results are demonstrated through simulations. Keywords: Cooperative control , Stability of nonlinear systems , Networked control systems Abstract: Control laws for a multi-agent system to follow level curves of a spatially distributed scalar field generally require sharing field measurements among the agents for gradient estimation.

This paper presents a distributed control law that does not rely on the communication of the field measurements nor the estimation of the gradient. Each agent only modulates its speed according to local measurements of the field and relative positions of its neighbors. The distributed control law scales to large swarms with connected undirected visibility graph. Stability is justified in a singular perturbation framework. The efficiency of the control law is validated through simulations demonstrating level curve tracking behaviors of 2-dimensional scalar fields.