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Optimal PID controller parameters for first order and second order systems with time delay using a connectionist approach

机译：使用连接器方法的具有时滞的一阶和二阶系统的最佳PID控制器参数

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摘要

This article focuses on developing a neural controller based on a proportional integral derivative (PID) law. The main objective is to show that using neural networks represents a better alternative compared with other conventional models that were used in the past to express the tuning parameters as a function of process parameters such as process gain (K_P), process time constants (τ_1, τ_2, etc.,) and process time delay (θ). A Levenberg-Marquardt backward propagation algorithm is used to get the required PID parameters corresponding to different types of processes. In the present study, the PID parameters for the first order plus time delay (FOPTD) and the second order plus time delay (SOPTD) are obtained. It was observed that very high R2 values between the actual PID parameters and the parameters obtained from the NN were achieved. Furthermore, the performance of PID control systems with FOPTD and SOPTD processes are applied on a number of case studies and compared with the conventional Zeigler-Nichols (Z-N) method of tuning PID controllers. Using the proposed NN controller was found to be efficient and the current method could be of potential use in control systems with gain scheduling also where the controller parameters are tuned according to a continuous gain schedule variable that changes based on different levels of process parameters.
机译：本文重点研究基于比例积分微分（PID）律的神经控制器。主要目的是表明，与过去使用其他传统模型来表达根据过程参数（例如过程增益（K_P），过程时间常数（τ_1， τ_2等）和处理时间延迟（θ）。 Levenberg-Marquardt反向传播算法用于获取与不同类型的过程相对应的所需PID参数。在本研究中，获得了一阶加时间延迟（FOPTD）和二阶加时间延迟（SOPTD）的PID参数。观察到，在实际的PID参数和从NN获得的参数之间获得了很高的R2值。此外，具有FOPTD和SOPTD过程的PID控制系统的性能已应用于许多案例研究中，并与调节PID控制器的传统Zeigler-Nichols（Z-N）方法进行了比较。发现使用所提出的NN控制器是有效的，并且当前方法在具有增益调度的控制系统中可能具有潜在用途，其中根据连续的增益调度变量来调整控制器参数，所述连续的增益调度变量基于过程参数的不同水平而变化。

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