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Adaptive Neuro-Controller Based on Hybrid Multi Layered Perceptron Network for Dynamic Systems

机译：基于混合多层感知器网络的动态系统自适应神经控制器

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摘要

In this paper, an intelligent controller namely Adaptive Neuro-controller (ANC) based on Hybrid Multi Layered Perceptron (HMLP) network has been developed for dynamic systems. The performance of ANC has been compared with the ANC based on Multi Layered Perceptron (MLP) network and Adaptive Parametric Black Box (APBB) Controller. The comparison are based on the time response and the capability of the controlled output to track the model reference output. All controllers are based on a black box approach that offers simpler design approach. The Model Reference Adaptive System (MRAS) has been used to generate the desired output path and to ensure the output of the controlled system follows the output of the reference model. Weighted Recursive Least Square (WRLS) algorithm has been used to adjust the controller parameters in order to minimize the error between the plant output and the model reference output. The controllers have been tested using a linear plant and a nonlinear plant with several varying operating conditions such as varying gain, noise and disturbance. Based on the simulation results and performance analysis for all controllers, it is observed that ANC based on HMLP network is controllable and more stable than ANC based on MLP network and APBB controller. It is also can be signify that the ANC based on HMLP network is sufficient to control the plants with unpredictable conditions.
机译：本文针对动态系统开发了一种基于混合多层感知器（HMLP）网络的智能控制器，即自适应神经控制器（ANC）。 ANC的性能已与基于多层感知器（MLP）网络和自适应参数黑匣子（APBB）控制器的ANC进行了比较。比较基于时间响应和受控输出跟踪模型参考输出的能力。所有控制器均基于黑盒方法，可提供更简单的设计方法。模型参考自适应系统（MRAS）已用于生成所需的输出路径，并确保受控系统的输出遵循参考模型的输出。加权递归最小二乘（WRLS）算法已用于调整控制器参数，以最小化工厂输出和模型参考输出之间的误差。已使用具有多种变化的工作条件（例如变化的增益，噪声和干扰）的线性设备和非线性设备对控制器进行了测试。通过对所有控制器的仿真结果和性能分析，发现基于HMLP网络的ANC比基于MLP网络和APBB控制器的ANC可控且更稳定。这也可以表明基于HMLP网络的ANC足以控制条件不可预测的工厂。

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