基于未知系统动态估计的机器人预设性能控制

针对含有未知系统动态和外部干扰的机器人系统,提出一种不依赖于函数逼近器且能保证瞬态和稳态性能的控制算法.设计未知系统动态估计器可重构机器人系统的未知动态(向心力、重力)和外部干扰,与其他方法相比,该估计器结构简单,只需调节一个参数,且引入滤波操作可避免使用加速度信号,有利于在实际机器人控制中的运用.控制器设计中引入描述收敛速率、最大超调量和稳态误差的预设性能函数,使机器人系统跟踪误差限制在预先规定边界内,保证机器人系统的性能和安全性.通过李雅普诺夫稳定性理论证明闭环系统的稳定性,并通过数值仿真和实验结果验证所提出方法的有效性.

Unknown system dynamics estimator for prescribed performance control of robotic systems

In this paper, a prescribed performance controller is proposed for robotic systems with unknown system dynamics and external disturbances, where the widely-used function approximation can be avoided, and both the transient and steady-state performances can be retained. The unknown system dynamics (e.g., coriolis/centripetal force, gravity torque) and external disturbances are estimated simultaneously via an unknown system dynamics estimator. The salient feature of the estimator over other schemes is that its structure is simple and only one parameter needs to be tuned. Moreover, the joint accelerations are avoided by introducing filter operations, making the estimator suitable for practical robotic control application. By employing a performance function that characterizes the convergence rate, maximum overshoot and steady-state error, the tracking error of the robotic system can be retained within a prescribed bound. The stability of the closed-loop control system is proved via Lyapunov theory. Simulations and experiments are carried out to validate the effectiveness of the proposed schemes.