Approximate solution of Hamilton-Jacobi inequality by neural networks
Approximate solution of Hamilton-Jacobi inequality by neural networks
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DOI：
10.1016/s0096-3003(96)00053-7
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发表时间：
1997-06
影响因子：
4
通讯作者：
Xiaofeng Yang;T. Shen;K. Tamura
中科院分区：
文献类型：
--
作者：
Xiaofeng Yang;T. Shen;K. Tamura
讨论了非线性H∞控制应用中的一个主要难点--Hamilton-Jacobi不等式的求解问题。提出了一种神经网络方法来获得Hamilton-Jacobi不等式的一个近似解。它将被证明，该问题可以制定为一个最大值函数优化问题，然后解决所提出的学习算法。该算法基于不可微优化理论。数值算例表明了该方法的有效性。
This paper discusses a problem of solving Hamilton-Jacobi inequality which is a main difficulty in nonlinear H∞control application. A neural networks approach is presented to obtain an approximate solution of Hamilton-Jacobi inequality. It will be shown that the problem can be formulated as a maximum value function optimization problem and then be solved by the proposed learning algorithm. The algorithm is developed based on nondifferentiable optimization theory. The effectiveness of the proposed method is demonstrated by numerical examples.