Distributed iterative learning control for heterogeneous parametric uncertain multi-agent systems with unknown control directions
Yingping Li, Jiaxi Chen, Junmin Li, Weisheng Chen, Shuai Zhang, Wensheng Chi · Journal of Information and Intelligence · 2025
This paper addresses the challenge of achieving accurate consensus control in heterogeneous linear parametric uncertain multi-agent systems, particularly in the presence of unknown control directions. To tackle this issue, we propose a distributed consensus protocol that integrates adaptive iterative learning control with the characteristics of Nussbaum gain functions. This novel approach not only enhances the robustness of the control system but also ensures convergence in the presence of uncertainties. Building upon this foundation, we extend our study to address the consensus problem in heterogeneous multi-agent systems under actuator failures. To overcome the issue of matrix multiplication between agents with different dimensions, we introduce a matrix dimension expansion method, which allows us to design a new equivalent matrix relation. Additionally, to ensure that the energy function decays with the number of iterations, we reconstruct the quadratic function used in the theoretical analysis based on matrix analysis theory. Through simulation examples, we demonstrate the effectiveness of our proposed theoretical framework, highlighting its practical applicability and novelty in the field of multi-agent systems control.