针对多轴伺服系统在高速运动急停段因系统柔性产生的残余振动问题，提出了一种适用性广的后置自适应输入整形器算法。该算法无需辨识系统模态参数，以递归最小二乘法（RLS）为基础，残余振动信号作为算法输入，优化得到当前轨迹下抑振效果最优的整形器系数向量，并引入自适应遗忘因子更新算法，以提高整形器在非平稳环境下的跟踪性能。同时建立多层全连接神经网络模型，选择多组激励轨迹作为样本对网络模型进行训练，解决了原有算法在轨迹多次变更的工况下，重新进行优化引起的时间成本显著增加的问题。实验结果表明：相比普通后置自适应输入整形器，应用带自适应遗忘因子后置输入整形器整形后的轨迹停止后的残余振动幅值平均减小了28.3%，最多的减小36.9%，残余振动收敛时间缩短28.4%。应用基于多层神经网络模型的输入整形器整形后的残余振动幅值相比普通后置自适应输入整形器平均减小了21.6%，最多的减小29.8%，残余振动收敛时间缩短23.7%。本研究提出的算法对于提高多轴伺服系统定位精度、缩短定位等待时间具有一定的应用意义，并且多层神经网络模型的引入在期望轨迹变化频繁的工况下提高了整体工作效率。

A widely applicable post-adaptive input shaper algorithm was proposed to solve the residual vibration of multi-axis servo system caused by the flexibility of the system in the emergency stop section of high-speed motion. The algorithm does not need to identify the system modal parameters and it is based on the recursive least square method (RLS). The residual vibration signal was used as the input of the algorithm to optimize the shaper coefficient vector with the best vibration suppression effect under the current trajectory. The adaptive forgetting factor updating algorithm was introduced to improve the tracking performance of the shaper in non-stationary environment. A full connection multilayer neural network model was established and trained by using multiple sets of excitation tra-jectory samples. It solves the problem of the original algorithm that the cost of time is significantly increased due to multiple changes of trajectory and reoptimization. The experimental results show that the residual vibration amplitude of the post adaptive input shaper with adaptive forgetting factor is reduced by 28.3% averagely and 36.9% maximally, and the convergence time of residual vibration is reduced by 28.4%, compared with the traditional post adaptive input shaper. The residual vibration amplitude of the input shaper predicted by the multilayer perceptron model is reduced by 21.6% averagely and 29.8% maximally, and the convergence time of residual vibration is reduced by 23.7%, compared with the ordinary post adaptive input shaper. The algorithm proposed in this paper has certain application significance for improving the positioning accuracy of multi-axis servo system and shortening the positioning waiting time. The introduction of multilayer neural network model improves the overall work efficiency under the condition that the expected trajectory changes frequently.