Learning from neural control
Learning from neural control
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DOI：
10.1109/cdc.2003.1271916
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发表时间：
2003-12
期刊：
影响因子：
--
通讯作者：
Cong Wang;D. Hill
中科院分区：
文献类型：
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作者：
Cong Wang;D. Hill
生物系统的惊人成功之一是它们能够“边做边学”，从而适应环境。在本文中，我们首先提出了一种自适应神经控制器，它能够学习系统的动态跟踪控制过程中的周期参考轨道。部分持续激励（PE）的条件被证明是满意的，和准确的NN近似的未知动态得到在局部区域沿着跟踪轨道。其次，提出了一种神经网络学习控制方案，能够有效地回忆和重用学习到的知识，以达到局部稳定和更好的控制性能。本文的意义在于提出了一种动态确定性学习理论，它可以实现类似于生物系统的学习和控制能力。
One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, we firstly present an adaptive neural controller which is capable of learning the system dynamics during tracking control to periodic reference orbits. A partial persistent excitation (PE) condition is shown to be satisfied, and accurate NN approximation for the unknown dynamics is obtained in a local region along the tracking orbit. Secondly, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve local stability and better control performance. The significance of this paper is that it presents a dynamical deterministic learning theory, which can implement learning and control abilities similarly to biological systems.