Disturbance Observer-Based Saturation-Tolerant Prescribed Performance Control for Nonlinear Multi-Agent Systems

This study focuses on the adaptive tracking control issue for nonlinear multi-agent systems (MASs) under the influence of asymmetric input constraints and external disturbances. Firstly, an auxiliary system is proposed, which can ensure flexible prescribed performance under input saturation conditions. Meanwhile, by introducing a transformation function, the distributed errors are freed from initial constraints. Employing the backstepping method, the adaptive technique, and a neural network approximation technology, a finite-time prescribed performance adaptive tracking control algorithm is designed, enabling the tracking errors to stably converge within the prescribed performance bounds. Secondly, a composite disturbance observer is developed to estimate and mitigate the combined disturbances, which include external perturbations and approximation errors from radial basis function neural networks (RBF NNs). It not only achieves effective disturbance compensation but also further suppresses the approximation errors of RBF NNs. Finally, stability analysis using the Lyapunov function demonstrates that all closed-loop signals remain uniformly ultimately bounded (UUB), with adaptive tracking errors converging to a compact region within a finite time. Simulation results and comparative studies confirm the proposed method’s effectiveness and advantages, providing a basis for its practical use in distributed control applications.