CAJ | 학술논문

拉索容易在外部激励下发生大幅振动，给拉索提供轴向控制力进行拉索主动控制是一种新的方法。本文拉索主动控制策略采用经典二次型线性最优控制，由于LQR控制的关键是根据状态矩阵即时求出Riccati方程，但是Riccati方程是一个矩阵非线性方程，其阶数高，变量间又相互耦合，求解十分困难，为了减小控制力输出滞后，更快的求解出控制力，更好的应用于拉索实时控制，本文基于神经网络具有很强的学习能力和非线性逼近能力，根据大量实验数据采用神经网络方法来预测下一步拉索振动状态所对应的控制力，并进行了仿真，证实了其有效性。
랍색용역재외부격려하발생대폭진동，급랍색제공축향공제력진행랍색주동공제시일충신적방법。본문랍색주동공제책략채용경전이차형선성최우공제，유우LQR공제적관건시근거상태구진즉시구출Riccati방정，단시Riccati방정시일개구진비선성방정，기계수고，변량간우상호우합，구해십분곤난，위료감소공제력수출체후，경쾌적구해출공제력，경호적응용우랍색실시공제，본문기우신경망락구유흔강적학습능력화비선성핍근능력，근거대량실험수거채용신경망락방법래예측하일보랍색진동상태소대응적공제력，병진행료방진，증실료기유효성。
Cable easily happened large amplitude vibration under external excitation,providing the axial control force to cable active control is a kind of new method. In this paper,the cable active control strategy is classical quadratic linear optimal control. Because the key of the LQR control is to calculate Riccati equation according to the real-time state matrix,but the Riccati equation is a nonlinear matrix equation,and it have a high order,mutual coupling between variables,so it is very difficult to solve. In order to reduce lag control output force and fast to solve the out of control force,we want to apply to real-time control better. In this paper,based on the neural network has strong learning ability and nonlinear approximation capability. According to a lot of experimental data using the neural network method to predict the cable vibration state of the control force,and a simulation is carried out,this paper proves its effectiveness.