Learning Consensus of Second-Order Unknown Nonlinear Parameterized Multiagent Systems With Periodic Disturbances
Jiaxi Chen, Junmin Li, Weisheng Chen, Shuai Zhang · IEEE Systems Journal · 2023
This article addresses the problem of learning consensus control for second-order unknown nonlinear parameterized multiagent systems using an iterative learning method. It focuses on developing a control law based on neural network approximation, symbolic function, and Fourier series expansion. The proposed iterative learning control law aims to achieve consensus among the agents. To ensure the stability of the closed-loop systems, a composite energy function is constructed and analyzed. Additionally, an improved smooth control protocol is introduced to mitigate control protocol chattering. The algorithm's generalization capabilities are explored through scenarios involving heterogeneous network topologies and external disturbances. Moreover, the consensus protocol is extended to address formation control with fixed tracking distances. Simulation results are presented to demonstrate the effectiveness of the adaptive iterative learning control algorithm.