Data-Driven Adaptive Control for Trajectory Tracking in Nonlinear Systems

This study investigates a data-driven approach for traj ectory tracking in nonlinear systems with both fixed and switching communication topologies, employing a model-free adaptive control (MFAC) strategy. The system dynamics are unknown and nonlinear, with only a subset of systems having direct access to the reference trajectory. By applying a dynamical linearization technique based on the pseudo partial derivative, an MFAC algorithm is developed to enable all systems to track the desired trajectory. The analysis shows that the tracking error can be effectively minimized for both static and time-varying reference traj ectories. A key advantage of this approach is that traj ectory tracking relies exclusively on input-output data from each system. The proposed method's effectiveness is validated through simulation results.