Prescribed Performance Based Data-Driven Iterative Learning Control for Multi-Wheeled Mobile Robots

This paper proposes a prescribed performance based data-driven iterative learning control (PP-DDILC) framework to address the challenges of transient and steady-state performance constraints in wheeled mobile robot (WMR) formation control. Focus on kinetic-level, by integrating prescribed performance control (PPC) with a data-driven strategy, the proposed PP-DDILC constructs a velocity tracking error with dynamic performance envelopes, enabling simultaneous constraints on transient overshoot and steady-state convergence without relying on precise kinetic models. Theoretical stability analysis and simulation results validate the method’s effectiveness in achieving high-precision tracking.