5 research outputs found

    Constrained Consensus in State-Dependent Directed Multiagent Networks

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    In this paper, constrained consensus of a group of continuous-time dynamical agents over state-dependent networks is investigated. The communication network, modulated by an asymmetric distance between agents, accommodates general directed information flows. Each agent proposes a comfortable range in a distributed manner, where they are inclined to agree on the final equilibrium state. Based on Lyapunov stability theory and robustness analysis, different conditions have been obtained to guarantee convergence within the common comfortable range when the network connectivity is fixed and time-varying. No global information is required in the proposed nonlinear control protocols. Furthermore, an opinion dynamics model has been introduced incorporating both social observer effect and bounded confidence phenomenon in the same state-dependent framework. Relaxed consensus conditions have been derived under certain symmetric assumptions. Finally, numerical examples have been presented to verify the effectiveness of the theoretical results

    Robust min-max model predictive vehicle platooning with causal disturbance feedback

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    Platoon-based vehicular cyber-physical systems have gained increasing attention due to their potentials in improving traffic efficiency, capacity, and saving energy. However, external uncertain disturbances arising from mismatched model errors, sensor noises, communication delays and unknown environments can impose a great challenge on the constrained control of vehicle platooning. In this paper, we propose a closed-loop min-max model predictive control (MPC) with causal disturbance feedback for vehicle platooning. Specifically, we first develop a compact form of a centralized vehicle platooning model subject to external disturbances, which also incorporates the lower-level vehicle dynamics. We then formulate the uncertain optimal control of the vehicle platoon as a worst-case constrained optimization problem and derive its robust counterpart by semidefinite relaxation. Thus, we design a causal disturbance feedback structure with the robust counterpart, which leads to a closed-loop min-max MPC platoon control solution. Even though the min-max MPC follows a centralized paradigm, its robust counterpart can keep the convexity and enable the efficient and practical implementation of current convex optimization techniques. We also derive a linear matrix inequality (LMI) condition for guaranteeing the recursive feasibility and input-to-state practical stability (ISpS) of the platoon system. Finally, simulation results are provided to verify the effectiveness and advantage of the proposed MPC in terms of constraint satisfaction, platoon stability and robustness against different external disturbances

    Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

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    Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms
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