Composite Learning-Based Adaptive Finite-Time Parameters Estimation and Control for High-Order Fully Actuated Systems

This paper addresses the adaptive finite-time parameter estimation and control problem for high-order fully actuated (HOFA) systems. First, a novel parameter estimation law is proposed, which leverages both historical and current online data to ensure accurate parameter identification under a relaxed interval excitation (IE) condition, thereby circumventing the typical stringent persistent excitation (PE) requirement. Then, a composite learning-based finite-time controller is developed for HOFA systems, ensuring the finite-time convergence of both parameter estimation error and tracking error to zero. Finally, the efficacy of the proposed approach is demonstrated through its application to the trajectory tracking control of a robotic manipulator, with its effectiveness verified by comprehensive numerical simulation results.