CAJ | 학술논문

为了提高神经网络直接逆控制方法的跟踪精度和抗干扰能力，结合前馈神经网络直接逆控制与通用模型控制策略各自的优点，提出了一种基于前馈神经网络复合逆控制的方法。该方法将控制系统的参考轨迹改造成一条规范的二阶曲线，从而使得控制器参数物理意义明确，且易于整定。仿真实验验证了系统具有良好的鲁棒性和抗扰性能。
위료제고신경망락직접역공제방법적근종정도화항간우능력，결합전궤신경망락직접역공제여통용모형공제책략각자적우점，제출료일충기우전궤신경망락복합역공제적방법。해방법장공제계통적삼고궤적개조성일조규범적이계곡선，종이사득공제기삼수물리의의명학，차역우정정。방진실험험증료계통구유량호적로봉성화항우성능。
A complex inverse control strategy was developed based on the advantages of feedforward neural network direct in-verse control and generic model control to improve the tracking precision and disturbance rejection of neural network direct inverse control methods. The strategy transforms reference trajectory of the control system into a classic second-order curve. The parame-ters of this controller have explicit physics meaning and it is very easy to tune. The results show that the system has strong robust-ness to the variation of system parameters and the disturbance of load.