Practical Fixed-Time Attitude Consensus Tracking for Multi-Quadrotor Systems: A Composite Learning Backstepping Approach

This paper investigates the practical fixed-time attitude consensus tracking problem for multi-quadrotor systems that are affected by functional uncertainties. To establish the tracking error signals, fixed-time estimators and observers are developed to estimate the leader’s system matrix and state. A set of fixed-time adaptive control schemes is proposed by incorporating composite learning and command filtering into the backstepping control approach. The composite learning update rules use tracking errors and prediction errors, with the latter being constructed using auxiliary variables. It is demonstrated that, under certain mild interval excitation conditions, the fixed-time convergence of observation errors, tracking errors, and parameter estimation errors can be achieved, effectively resolving the practical fixed-time attitude consensus tracking problem. Numerical simulations are performed to validate the effectiveness of the obtained results.