Composite learning control with modular backstepping and high-order tuners

This paper proposes a composite learning backstepping control (CLBC) strategy based on modular backstepping and high-order tuners to achieve closed-loop exponential stability without high-gain feedback and PE. A novel composite learning mechanism that maximizes the staged exciting strength is designed for parameter estimation, enabling parameter convergence under interval excitation (IE) or even partial IE, which is strictly weaker than PE. An extra prediction error is employed in the adaptive law to ensure the transient performance without high-gain feedback. Simulations have demonstrated the effectiveness and superiority of the proposed method in both parameter estimation and control compared to state-of-the-art methods.