数据驱动的机械臂固定时间复合自适应控制OA

The control problem of manipulator with uncertain parameters was addressed by developing a data-driven fixed-time composite adaptive control method.The proposed method achieved finite-time convergence of parameter estimation errors under relaxed excitation conditions while ensuring fixed-time stability of closed-loop systems.A regression filtering technique was firstly used to construct equivalent representations of unknown parameters.To relax Persistent Excitation(PE),a novel finite-time com-posite parameter estimation law was designed.The integration of the proposed parameter estimation law with fixed-time control laws was validated through Lyapunov stability analysis.Numerical simulations confirmed the method's capability to estimate un-known parameters under relaxed excitation conditions,demonstrating high convergence accuracy,rapid convergence speed,and ro-bust performance.