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Fuzzy-Adaptive Control with Gain and Phase Margins

机译:增益和相位利润的模糊自适应控制

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摘要

This paper presents a fuzzy-adaptive control method that results in the same gain and phase margins as a known linear control. The method uses fuzzy switching to transition between a traditional fuzzy-adaptive control and the linear control. Our novel switching algorithm changes the structure of the fuzzy approximator itself, so that the fuzzy approximator completely transforms into a pure integrator e.g. becoming the integral term in a PID control. The fuzzy inference takes both state error and adaptive-parameter drift as the inputs, so as to detect possible instabilities resulting from unmodeled dynamics, disturbances, and/or adaptive-parameter drift. The fuzzy inference determines how close the error and adaptive-parameter drift are to imposed limiting parameters. In our simulation we find that when the limiting parameters are tuned for peak performance, the proposed method doubles the performance compared to the traditional robust adaptive weight update methods of deadzone, leakage, and e-modification. Moreover, the limiting parameters can be tuned to find this peak performance by trial-and-error without fear of instability, unlike with traditional robust update methods.
机译:本文提出了一种模糊的自适应控制方法的结果在相同的增益和相位裕度作为已知的线性控制。该方法使用模糊切换到传统的模糊自适应控制和线性控制之间的转换。我们的新的切换算法改变模糊逼近本身的结构,从而使模糊逼近完全转变成纯积分例如成为PID控制的积分项。模糊推理需要两个状态误差和自适应参数漂移作为输入,以便检测从未建模动态,干扰,和/或自适应参数漂移产生的可能的不稳定性。模糊推理确定错误和自适应参数漂移有多接近强加限制参数。在我们的模拟,我们发现,当有限的参数调整为最佳性能,所提出的方法相比,死区,泄漏和电子修改了传统的鲁棒自适应权重更新方法一倍的性能。此外,限制参数可以调整,以找到试错这个峰值性能,而不必担心不稳定的,不像传统的稳健的更新方法。

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