Output Feedback Concurrent Learning Control for Fully Actuated System

This paper proposes a novel concurrent-learning-based adaptive control scheme for second-order fully actuated systems with unknown parameters. Unlike conventional adaptive control approaches that depend on velocity measurements or state observers, the proposed method employs a systematic filtering structure to achieve velocity-independent parameter adaptation. A key innovation is the construction of history-stack matrices, which enable consistent parameter estimation without requiring persistent excitation in the instantaneous sense. Theoretical analysis establishes the global asymptotic stability of the closed-loop system under the interval-excitation condition. Simulation results demonstrate the effectiveness and performance of the proposed approach.