扭矩输入有界的机器人模糊PD轨迹跟踪

Trajectory tracking control for robot manipulators with bnounded torque inputs via fuzzy PD

针对轨迹跟踪控制中机器人关节驱动器输出扭矩受限的问题,提出一种基于模糊自适应PD的输入有界轨迹跟踪控制算法。不同于以往的控制策略,该算法在控制律中引入具有饱和特性的改进反正切函数,以确保扭矩控制输入的有界性,并结合模糊自适应原理实现PD增益的在线自整定,以改善系统的动态特性。通过对位置跟踪误差进行线性滤波得到速度跟踪误差替代信号,使得整个系统的闭环控制仅需位置输出反馈。利用奇异摄动理论对系统进行了稳定性分析,证明在一定约束下的PD增益自整定过程中,仍能保证系统稳定。仿真和比较结果表明,该算法能够在严格保证控制输入有界的前提下,减小超调量,缩短系统调整时间,具有更优的轨迹跟踪性能。

To deal with the problem of trajectory tracking control of robots with bounded torque inputs, a novel tracking algorithm with bounded inputs based on fuzzy self-tuning proportional and derivative （PD） gains was proposed. Compared with the previous strategies, the boundedness of torque control inputs was guaranteed by invoking a modified arc tangent function with saturation features, and the dynamic characteristics were improved by the online self-tuning of PD gains, combining with fuzzy adaptive theory. Moreover, a pseudo signal of velocity tracking error was generated from position tracking eiTor by a linear filter, which made the whole closed-loop control with only position feedback measurements. In addition, the singular perturbation theory was applied in the stability analysis of the system, which proved that the stability is still ensured in the self-tuning procedures of PD gains. Finally, simulation results and comparisons indicate that, the proposed controller the overshoot can be reduced and the adjusting time of the system is shortened, with the boundedness of control inputs strictly assured, which contributes to gain a better tracking performance.