Adaptive distributed estimation and performance constrained optimal consensus control of multi-agent systems

Purpose This paper proposes an adaptive distributed observer-based optimal control protocol designed to tackle the challenges posed by unavailable state information, without relying on global information, while ensuring optimal system performance in heterogeneous multi-agent systems. The purpose of this paper is to achieve distributed output tracking control under these constraints. Design/methodology/approach An adaptive distributed observer using single adaptive and local static gains estimates leader’s output. A feedback-feedforward control policy is designed for optimal tracking. Unlike conventional methods, this reduces communication load and broadens applications when leader’s state is unmeasurable. Findings A numerical simulation example related to autonomous ground vehicle is given to demonstrate that the proposed control algorithm can ensure output tracking in heterogeneous multi-agent systems. Research limitations/implications Although the proposed algorithm can effectively guarantee output tracking, further research is needed to design an optimal controller for model-free system dynamics in unknown environments. Practical implications The proposed method can be implemented for formation control of multi-robot systems since each robot can measure the relative output information, thereby effectively overcoming the limitation of unavailable system states. Originality/value This paper introduces a novel adaptive distributed observer-based optimal control protocol for heterogeneous multi-agent systems, uniquely relying on a single adaptive gain and local output information, eliminating the need for global network data. This reduces communication overhead and enhances applicability in scenarios where the leader’s state is unmeasurable. Unlike existing methods, it integrates optimal tracking control via the Bellman equation, considering transient performance. Its value lies in practical implementation for multi-robot formation control, though limitations include the need for further research into model-free systems in unknown environments.