Time-optimal control by means of neural networks
Time-optimal control by means of neural networks
复制标题
通过神经网络进行时间最优控制
DOI：
10.1109/37.466265
复制
发表时间：
1995
影响因子：
5.7
通讯作者：
J. D. Plessis
中科院分区：
文献类型：
--
作者：
T. Niesler;J. D. Plessis
时间最优控制器的开发常常因分析设计过程的复杂性而受阻。为避免这一困难，有人提出借助神经网络对高度非线性的最优控制律进行数值逼近，并开发了一种算法，该算法能让网络通过迭代优化过程学习所需的控制动作。特别是，这一过程涉及同时最小化完成控制动作所需的时间以及最终状态误差。它具有通用性很强的优点，因为无需对被控对象进行先验假设，而且灵活性很高，因为它允许纳入特定问题的约束条件。通过将该技术应用于二阶和四阶测试被控对象，对其性能进行了研究，在两种情况下都获得了非常积极的结果。
The development of time-optimal controllers is often hindered by the complexity of the analytical design process. In order to avoid this difficulty, the numerical approximation of the highly nonlinear optimal control law by means of a neural network has been proposed, and an algorithm which allows the network to learn the required control actions by means of an iterative optimization process has been developed. In particular, this process involves the simultaneous minimization of both the time necessary to complete the control action as well as the final state error. It has the advantage of being very general, since no a-priori assumptions are made about the plant, and of being very flexible in that it permits the inclusion of problem-specific constraints. The performance of the technique has been investigated by applying it to both a second- and a fourth-order test plant, with very positive results being obtained in both cases. >