Abstract
This paper proposes a distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme. The proposed scheme is independent of a precise mathematical model, but can achieve both bipartite time-invariant and time-varying trajectory tracking for unknown dynamic discrete-time heterogeneous multi-agent systems (MASs) with switching topology and coopetition networks. The main innovation of this algorithm is to estimate an equivalent dynamic linearization data model by the pseudo partial derivative (PPD) approach, where only the input–output (I/O) data of each agent is required, and the cooperative interactions among agents are investigated. The rigorous proof of the convergent property is given for DMFABCT, which reveals that the trajectories error can be reduced. Finally, three simulations results show that the novel DMFABCT scheme is effective and robust for unknown heterogeneous discrete-time MASs with switching topologies to complete bipartite consensus tracking tasks.
Highlights
Multi-agent systems (MASs) and machine learning, two exciting trends in the robotics field, have recently attracted more and more researchers’ attention due to the new epoch of artificial intelligence (AI) [1,2]
In [15], Odekunle et al presented a novel approach to solve the non-zero-sum game output regulation problem for multi-agent systems (MASs) by using reinforcement learning (RL). We found that another category of model-free control (MFC) methods is based on neural networks (NNs), which have unparalleled approximation abilities for nonlinear dynamics
The proposed distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme is designed by neighbor-based online measurement I/O data that can bypass the confusion of existing consensus algorithms as seen in [5,6,7,8,9,10,11,24,25,26,27,28,29,30,31,32,33,34,35] to obtain an accurate mathematical model so that the designed scheme is more robust and reduces energy costs from the massive computation
Summary
Multi-agent systems (MASs) and machine learning, two exciting trends in the robotics field, have recently attracted more and more researchers’ attention due to the new epoch of artificial intelligence (AI) [1,2]. The proposed DMFABCT scheme is designed by neighbor-based online measurement I/O data that can bypass the confusion of existing consensus algorithms as seen in [5,6,7,8,9,10,11,24,25,26,27,28,29,30,31,32,33,34,35] to obtain an accurate mathematical model so that the designed scheme is more robust and reduces energy costs from the massive computation Both collaborative and antagonistic interactions among agents are considered in the proposed protocol.
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