Model Reference Adaptive Stabilizing Control with Application to Leaderless Consensus

This article describes an extension of the well-known model reference adaptive control (MRAC) approach. The extension relies on explicitly involving the tracking error in the feedback control law: it is shown that including this term along with its appropriate extra adaptive gain allows one to handle possibly unstable reference dynamics....

Geometry-Aware Distributed Kalman Filtering for Affine Formation Control under Observation Losses

Affine formation control of multiagent systems has recently received increasing attention in various applications. The distributed control of these agents, under single integrator dynamics, relies on the observations of relative positions of the neighboring agents, which when unavailable is detrimental to the mission. In this paper, we...

Distributed Bayesian: A Continuous Distributed Constraint Optimization Problem Solver

In this paper, the novel Distributed Bayesian (D-Bay) algorithm is presented for solving multi-agent problems within the Continuous Distributed Constraint Optimization Problem (C-DCOP) framework. This framework extends the classical DCOP framework towards utility functions with continuous domains. D-Bay solves a C-DCOP by utilizing Bayesian...

Distributed Aperiodic Time-Triggered and Event-Triggered Consensus: A Scalability Viewpoint

We revisit distributed sampled-data consensus problems from a scalability point of view. Existing solutions in the literature for estimating the maximum sampling interval that preserves stability rely on the Lyapunov functional method. With this method, the overall closed-loop system (i.e. the overall network of agents) is treated as a time...

Leaderless Consensus of Heterogeneous Multiple Euler-Lagrange Systems With Unknown Disturbance

This article studies the leaderless consensus problem of heterogeneous multiple networked Euler-Lagrange systems subject to persistent disturbances with unknown constant biases, amplitudes, initial phases, and frequencies. The main characteristic of this study is that none of the agents has information of a common reference model or of a...

Distributed Time-Varying Optimization of Second-Order Multiagent Systems Under Limited Interaction Ranges

This article investigates the distributed time-varying optimization problem for second-order multiagent systems (MASs) under limited interaction ranges. The goal is to seek the minimum of the sum of local time-varying cost functions (CFs), where each CF is only available to the corresponding agent. Limited communication range refers to the...

A Separation-Based Methodology to Consensus Tracking of Switched High-Order Nonlinear Multiagent Systems

This work investigates a reduced-complexity adaptive methodology to consensus tracking for a team of uncertain high-order nonlinear systems with switched (possibly asynchronous) dynamics. It is well known that high-order nonlinear systems are intrinsically challenging as feedback linearization and backstepping methods successfully developed...

Can social agents efficiently perform in automated negotiation?

In the last few years, we witnessed a growing body of literature about automated negotiation. Mainly, negotiating agents are either purely self-driven by maximizing their utility function or by assuming a cooperative stance by all parties involved in the negotiation. We argue that, while optimizing one’s utility function is essential, agents...

Consensus in high-power multiagent systems with mixed unknown control directions via hybrid Nussbaum-based control

This work investigates the consensus tracking problem for high-power nonlinear multiagent systems with partially unknown control directions. The main challenge of considering such dynamics lies in the fact that their linearized dynamics contain uncontrollable modes, making the standard backstepping technique fail; also, the presence of mixed...

The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems

This article describes the MultiAgent Decision Process (MADP) toolbox, a software library to support planning and learning for intelligent agents and multiagent systems in uncertain environments. Key features are that it supports partially observable environments and stochastic transition models; has unified support for single- and multiagent...

Operational Scheduling in a Multi-Actor Environment using Multiagent Systems

Liquid bulk is one of the largest industries of the modern world, and efficient scheduling is needed for liquid bulk terminals to remain competitive. A common solution for the planning efficiency problem is to apply planning algorithms, rather than human planners for creating schedules. However, replacing human planners by algorithms is often a...

Modelling situation awareness relations in a multiagent system

There is broad consensus that situation awareness (SA) plays a key role in agent-based modelling of complex sociotechnical systems. However in the social sciences and human factors literature there are different views on what SA is and how it could be modelled. More specifically, one school of research considers SA as the process of gaining...

Ships in an Artificial Force Field: A Multi-agent System for Nautical Traffic and Safety

The main objective of this research is developing a simulation tool that provides information of detailed ship behavior in a specific navigational environment, on both the ship traffic level and the individual ship level, for safety analysis, decision making, planning of ports and waterways, and design of mitigation measures. In this research,...

AORTA: Adding organizational reasoning to agents (Extended Abstract)

Open systems are characterized by a diversity of heterogeneous and autonomous agents that act according to private goals, and with a behavior that is hard to predict. They can be regulated through organizations similar to human organizations, which regulate the agents’ behavior space and describe the expected behavior of the agents. Agents need...

Creating incentives to prevent intentional execution failures

When information or control in a multiagent system is private to the agents, they may misreport this information or refuse to execute an agreed outcome, in order to change the resulting end state of such a system to their benefit. This may result in execution failures. When only information is private, mechanisms such as VCG use payments to...

Can agents measure up? A comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty

Experiments studying the behavior of agent based methods over varying levels of uncertainty in comparison to traditional optimization methods are generally absent from the literature. In this paper we apply two structurally distinct solution approaches, an on-line optimization and an agent based approach, to a drayage problem with time windows...

A Comprehensive Survey of Multiagent Reinforcement Learning

Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity ofmany tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must, instead, discover a solution on their own, using learning....