Radial basis function neural network based data-driven iterative learning consensus tracking for unknown multi-agent systems

doi:10.1016/j.asoc.2024.112425

Radial basis function neural network based data-driven iterative learning consensus tracking for unknown multi-agent systems

2024-12-01

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DOI：10.1016/j.asoc.2024.112425

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This paper provides a novel data-driven-distributed-consensus control protocol for unknown nonlinear nonaffine discrete-time multi-agent systems (MAS) with repetitive properties. The leader's commands are directed to the followers in the topological graph. The dynamic linearization technology (DLT) is used to build the distributed iterative learning (IL) controller along the iteration axis. In the iterative process, the control gain is automatically adjusted by updating the weight matrix of the high-order radial basis function neural network (RBFNN, HORBFNN). In global control, the higher order parameter (HOP) Newton method is used to achieve global convergence and stability of the control process. All the above processes do not require the understanding of dynamical equations or physical models for each agent, and only use local communication information of multi-agent to achieve consistent tracking of MAS leaders and followers. Based on the strong connection, the convergence performance, stability and boundedness properties of the proposed control protocol in the fixed topology as well as in the iterative topology are validated by a rigorous theoretical analysis. Simulation experiments are conducted to verify the effectiveness of the control protocol.

Keywords:

Multi-agent systems

Data-driven

Iterative learning control

RBFNN

Higher order parameter

Radial basis function neural network based data-driven iterative learning consensus tracking for unknown multi-agent systems

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