An adaptive approximation-free control for leader-follower tracking of multi-agent systems with unknown nonlinearity and disturbance

Abstract

Due to variable and complex work environments, nonlinearities, uncertainty and disturbances are inevitable in multi-agent systems. Approximation-free control can address these issues without involving approximators, such as fuzzy logic systems and neural networks. However, some issues like the singularity problem caused by the signals exceeding the preset boundary in changing work conditions still remain. This paper proposes an adaptive and reliable approximation-free control, which comprises a novel singularity compensator and a modified transforming function. The proposed control scheme performs better in terms of convergence rate and overshoot, avoids issues relating to singularity, and has added flexibility in terms of parameter choice. The proposed control law adapts to changes in operating conditions and nonlinearities—the efficacy of which is demonstrated using simulations.