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1887

access icon free Robust decentralised mean field control in leader following multi-agent systems

© The Institution of Engineering and Technology
Received 03/12/2016, Accepted 03/08/2017, Revised 06/06/2017, Published 03/08/2017

This study addresses a robust counterpart of the deterministic mean field control in a multi-agent system. A decentralised mean field algorithm is proposed to solve a min–max control problem for a large population of heterogeneous agents. In the proposed leader following scheme, the leader tracks a reference signal which is unknown to the followers and each follower tracks a convex combination of the population state average and the leader's state. The leader plays a robust min–max game against disturbance and the followers play a mean field -Nash game against each other and at the same time, each follower plays a robust min–max game against the disturbance. For all the players, finite horizon quadratic cost is considered. In the proposed decentralised algorithm, followers do not need the knowledge about each of leader's and other followers' states and they only use an estimate of the population state average. In this way, propose a policy iteration method which guarantees the convergence to the saddle point mean field -Nash solution. The proposed method is applied to a large population of agents and compared with centralised algorithm to demonstrate the results.

Inspec keywords: game theory; state estimation; decentralised control; convex programming; multi-agent systems; iterative methods; minimax techniques; robust control

Other keywords: min-max control problem; decentralised mean field algorithm; finite horizon quadratic cost; population state average estimation; leader following multiagent systems; policy iteration method; robust decentralised mean field control; heterogeneous agents; robust min–max game; population state average; reference signal; saddle point mean field εN-Nash game

Subjects: Simulation, modelling and identification; Stability in control theory; Optimisation techniques; Interpolation and function approximation (numerical analysis); Game theory; Multivariable control systems

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