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An Improved Scheme for Direct Adaptive Control of Dynamical Systems Using Backpropagation Neural Networks

机译:利用反向传播神经网络的动态系统直接自适应控制的改进方案。

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This paper presents an improved direct control architecture for the on-line learning control of dynamical systems using backpropagation neural networks. The proposed architecture is compared with the other direct control schemes. In this scheme the neural network interconnection strengths are updated based on the output error of the dynamical system directly, rather than using a transformed version of the error employed in other schemes. The ill effects of the controlled dynamics on the on-line updating of the network weights are moderated by including a compensating gain layer. An error feedback is introduced to improve the dynamic response of the control system. Simulation studies are performed using the nonlinear dynamics of an underwater vehicle and the promising results support the effectiveness of the proposed scheme.
机译:本文提出了一种改进的直接控制架构,用于使用反向传播神经网络的动态系统在线学习控制。所提出的体系结构与其他直接控制方案进行了比较。在该方案中,直接根据动态系统的输出误差来更新神经网络的互连强度,而不是使用其他方案中使用的误差的变换形式。通过包括补偿增益层,可以减轻受控动态对网络权重在线更新的不良影响。引入了误差反馈以改善控制系统的动态响应。仿真研究是使用水下航行器的非线性动力学进行的,有希望的结果证明了该方案的有效性。

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