非线性慢过程的神经自适应控制

M. Bozic, P. Maric, Jasmin Igic
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引用次数: 3

摘要

提出了一种基于神经自适应内模型的控制方法(NAIMC),利用快速聚类径向基函数网络(FCRBFN)和随机梯度下降(SGD)学习算法对慢动态非线性对象进行控制。作为该设计方法的第一步,应用经典反馈控制器来改善得到的局部闭环的整体动态特性。这种局部回路进一步被认为是可以应用NAIMC的等效工厂。等效对象的改进特性通常可以通过某种PD控制律得到,我们将这种方法应用于非线性慢过程的NAIMC设计中。为了使系统在参考参数和输出扰动分段恒定变化的情况下实现零稳态误差,在基于NAIMC的结构中,我们采用增益调度(GS)的方法来调整Q控制器的增益。我们使用双油箱系统控制的仿真结果来说明所提出的NAIMC设计的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A neuro-adaptive control of nonlinear slow processes
A Neuro-Adaptive Internal Model-based Control (NAIMC), using the Fast Clustered Radial Basis Function Network (FCRBFN) equipped by the Stochastic Gradient Descent (SGD) learning algorithm is proposed to control the nonlinear plant with slow dynamics. As a first step in this design approach, the classical feedback controller is applied to improve the overall dynamic characteristics of the obtained local closed loop. Such local loop is further on considered as an equivalent plant to which the NAIMC can be applied. The improved characteristics of the equivalent plant can be usually obtained by some kind of the PD control law and we used this approach at the NAIMC design of the nonlinear slow process. To achieve a zero-steady state error in cases of the piecewise constant changes of the reference and disturbance at output of the plant, we applied the method of Gain Scheduling (GS) for adjusting the gain of the Q controller in the NAIMC based structure. We illustrate the performance of the proposed NAIMC design using simulation results for the control of a double tank system.
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