Fixed-Time Cooperative Tracking Control With Novel Finite-Time Parameter Estimation Algorithm for Nonlinear Multi-Agent Systems

Cooperative control of multi-agent systems (MASs) has gained significant attention due to its applications in autonomous vehicles, robotics, and distributed sensor networks. A key challenge is achieving accurate tracking and consensus under nonlinear dynamics, system uncertainties, and external disturbances. Most existing approaches offer asymptotic or finite-time convergence guarantees, often relying on prior knowledge of system parameters or specific initial conditions. This research proposes a fixed-time cooperative tracking control with finite-time parameter estimation in nonlinear second-order multi-agent systems (MASs) under directed communication topology. An auxiliary filter is designed to reconstruct the unknown parameters based on the parameter estimation error within a finite time. The adaptive law proposed in this framework is formulated by incorporating a filtered regressor driven both by the consensus and parameter estimation error. The fixed-time convergence of the consensus error to zero, which is aided by the salient feature of the parameter estimation algorithm, is analyzed rigorously via Lyapunov analysis. The fixed-time tracking control, a fractional-order-based nonlinear algorithm, is developed to leverage a modified consensus error and employs sliding mode with adaptive coefficients. Additionally, the robustness of the control schemes is also investigated against bounded disturbances. Finally, the effectiveness of the developed methods is validated through extensive simulations.