Multi-agent decision-making dynamics inspired by honeybees

When choosing between candidate nest sites, a honeybee swarm reliably chooses the most valuable site and even when faced with the choice between near-equal value sites, it makes highly efficient decisions. Value-sensitive decision-making is enabled by a distributed social effort among the honeybees, and it leads to decision-making dynamics of the swarm that are remarkably robust to perturbation and adaptive to change. To explore and generalize these features to other networks, we design distributed multi-agent network dynamics that exhibit a pitchfork bifurcation, ubiquitous in biological models of decision-making. Using tools of nonlinear dynamics we show how the designed agent-based dynamics recover the high performing value-sensitive decision-making of the honeybees and rigorously connect investigation of mechanisms of animal group decision-making to systematic, bio-inspired control of multi-agent network systems. We further present a distributed adaptive bifurcation control law and prove how it enhances the network decision-making performance beyond that observed in swarms

Resilience and Controllability of Dynamic Collective Behaviors

The network paradigm is used to gain insight into the structural root causes of the resilience of consensus in dynamic collective behaviors, and to analyze the controllability of the swarm dynamics. Here we devise the dynamic signaling network which is the information transfer channel underpinning the swarm dynamics of the directed interagent connectivity based on a topological neighborhood of interactions. The study of the connectedness of the swarm signaling network reveals the profound relationship between group size and number of interacting neighbors, which is found to be in good agreement with field observations on flock of starlings [Ballerini et al. (2008) Proc. Natl. Acad. Sci. USA, 105: 1232]. Using a dynamical model, we generate dynamic collective behaviors enabling us to uncover that the swarm signaling network is a homogeneous clustered small-world network, thus facilitating emergent outcomes if connectedness is maintained. Resilience of the emergent consensus is tested by introducing exogenous environmental noise, which ultimately stresses how deeply intertwined are the swarm dynamics in the physical and network spaces. The availability of the signaling network allows us to analytically establish for the first time the number of driver agents necessary to fully control the swarm dynamics

Fully distributed consensus for high-order strict-feedback nonlinear multiagent systems with switched topologies

summary:This paper studies the distributed consensus problem of high-order strict-feedback nonlinear multiagent systems. By employing the adaptive backstepping technique and switched system theory, a novel protocol is proposed for MASs with switched topologies. Global information such as the number of agents and communication topology is not used. In addition, the communication topology between agents can be switched between possible topologies at any time. Based on the Lyapunov function method, the proposed adaptive protocol guarantees the complete consensus of multiagent systems without restricting the dwell time of the switched signal. Finally, two numerical examples are provided to illustrate the effectiveness and advantages of the given protocol

Input Delay Estimation for Input-Affine Dynamical Systems Based on Taylor Expansion

In this brief, we propose a novel method based on the Taylor expansion for the estimation of input delay for a class of input-affine dynamical systems. The proposed method guarantees the asymptotic convergence of the estimation error to zero. An application to the input delay estimation of a continuous stirred tank reactor system shows the effectiveness of the proposed method

Active-passive dynamic consensus filters: Theory and applications

”This dissertation presents a new method for distributively sensing dynamic environments utilizing integral action based system theoretic distributed information fusion methods. Specifically, the main contribution is a new class of dynamic consensus filters, termed active-passive dynamic consensus filters, in which agents are considered to be active, if they are able to sense an exogenous quantity of interest and are considered to be passive, otherwise, where the objective is to drive the states of all agents to the convex hull spanned by the exogenous inputs sensed by active agents. Additionally, we generalize these results to allow agents to locally set their value-of-information, characterizing an agents ability to sense a local quantity of interest, which may change with respect to time. The presented active-passive dynamic consensus filters utilize equations of motion in order to diffuse information across the network, requiring continuous information exchange and requiring agents to exchange their measurement and integral action states. Additionally, agents are assumed to be modeled as having single integrator dynamics. Motivated from this standpoint, we utilize the ideas and results from event-triggering control theory to develop a network of agents which only share their measurement state information as required based on errors exceeding a user-defined threshold. We also develop a static output-feedback controller which drives the outputs of a network of agents with general linear time-invariant dynamics to the average of a set of applied exogenous inputs. Finally, we also present a system state emulator based adaptive controller to guarantee that agents will reach a consensus even in the presence of input disturbances. For each proposed active-passive dynamic consensus filter, a rigorous analysis of the closed-loop system dynamics is performed to demonstrate stability. Finally, numerical examples and experimental studies are included to demonstrate the efficacy of the proposed information fusion filters”--Abstract, page iv