Composite learning control with modular backstepping and high-order tuners
For control systems requiring adaptive parameter estimation, this method relaxes the stringent persistent excitation condition, making it more practical for real-world applications.
The paper proposes a composite learning backstepping control strategy that achieves closed-loop exponential stability without requiring high-gain feedback or persistent excitation, using a novel learning mechanism that enables parameter convergence under weaker interval excitation. Simulations show improved parameter estimation and control performance over state-of-the-art methods.
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.