论文标题
动态系统模拟和控制使用连续的复发神经网络
Dynamic Systems Simulation and Control Using Consecutive Recurrent Neural Networks
论文作者
论文摘要
在本文中，我们介绍了一种新颖的体系结构，将自适应学习和神经网络连接到任意机器的控制系统范式中。将两个连续的复发性神经网络（RNN）一起使用，以准确地对包括控制器，执行器和电动机在内的机电系统的动态特性进行建模。通过使用比例，积分和衍生常数来实现控制的古老方法被充分理解为一种简化的方法，它不会捕获复杂控制系统固有的非线性的复杂性。在控制和模拟机电系统的背景下，我们提出了使用两个复发神经网络的序列的PID控制器的替代方案。第一个RNN模拟了控制器的行为，第二个RNN是执行器/电动机的行为。第二个RNN隔离时，可能是现有机电系统测试方法的有利替代方法。
In this paper, we introduce a novel architecture to connecting adaptive learning and neural networks into an arbitrary machine's control system paradigm. Two consecutive Recurrent Neural Networks (RNNs) are used together to accurately model the dynamic characteristics of electromechanical systems that include controllers, actuators and motors. The age-old method of achieving control with the use of the- Proportional, Integral and Derivative constants is well understood as a simplified method that does not capture the complexities of the inherent nonlinearities of complex control systems. In the context of controlling and simulating electromechanical systems, we propose an alternative to PID controllers, employing a sequence of two Recurrent Neural Networks. The first RNN emulates the behavior of the controller, and the second the actuator/motor. The second RNN when used in isolation, potentially serves as an advantageous alternative to extant testing methods of electromechanical systems.
