Nonlinear predictive control with application to manipulator with flexible forearm
Nonlinear predictive control with application to manipulator with flexible forearm
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DOI：
10.1109/41.793340
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发表时间：
1999-10
期刊：
影响因子：
--
通讯作者：
Bumjin Song;A. Koivo
中科院分区：
文献类型：
--
作者：
Bumjin Song;A. Koivo
构造了一个神经网络来表示动力学模型的输入输出关系。通过二阶训练算法计算参数。然后，一个非线性预测控制器的设计的神经网络对象模型的基础上，使用滚动时域控制方法。基于神经模型，控制计算通过最小化的预计成本函数，惩罚未来的跟踪误差。作为该方法的一个例子，一个平面的两关节臂与柔性前臂的非线性动力学建模使用S形网络和离线估计程序的运动范围。通过计算机仿真说明了该方法的适用性。
A neural network is constructed to represent the input-output relation of a dynamical model. The parameters are calculated by means of a second-order training algorithm. Then, a nonlinear predictive controller is designed on the basis of a neural network plant model using the receding-horizon control approach. Based on the neural model, the control is calculated by minimizing a projected cost function that penalizes future tracking errors. As an illustration of the approach, the nonlinear dynamics of a planar two-joint arm with a flexible forearm are modeled using a sigmoidal network and an offline estimation procedure for a range of motions. The applicability of the approach is illustrated through computer simulations.