分析了初始控制量对迭代学习控制（ ILC）算法收敛速度及跟踪精度的影响，为保证ILC算法对任意期望轨迹的跟踪性能，提出了一种基于T-S模糊模型的ILC算法初始控制量确定方法。利用模糊系统理论对未知非线性对象进行离线逆建模，实现对象逆模型参数化，根据模糊逆模型求得任意期望轨迹下的初始控制量，将其作为ILC算法的理想初始控制量进行迭代学习。倒立摆系统仿真实验表明，算法能快速跟踪上设定点，与任意选取初始控制量相比能有效减少迭代次数，提高跟踪精度，具有更好的动态性能。%The effects of the initial value of iterative learning control inputs on the convergence rate and tracking accuracy are discussed .In order to ensure tracking performance of iterative learning control ( ILC) for every desired trajectory,an initial control value determination method based on T-S fuzzy model was proposed .Fuzzy system theory was used to establish the off-line inverse model of unknown nonlinear object in order to realize inverse model parameterization .Based on fuzzy inverse model,the initial control value was obtained for any desired trajectory,which was then used as the ideal initial control value of ILC algorithm . Simulation results of inverted pendulum system show that the new algorithm can track the set point quickly . It can reduce iteration times,improve tracking precision,and has better dynamic performance when compared with the method of choosing arbitrary initial control value .
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