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A Comparison of Two Artificial Neural Networks for Modelling and Predictive Control of a Cascaded Three-Tank System

机译：两个人工神经网络对级联三箱系统建模和预测控制的比较

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Process industries are confronted with multifaceted problems, including a high degree of nonlinearity and integrated processes, high energy costs, and stringent environmental regulations. The traditional methods for solving these problems are suboptimal. The quest for an optimal solution for industrial processes with reduced product variability and increased profit margin has since birthed the need to develop efficient design methods. Hence, this study investigated two artificial neural networks (ANN) applications for modelling and predictive control of an experimental cascaded three-tank system – a 3-by-3 multivariable and nonlinear process. To achieve this, the tank process was excited by well-designed input signals to obtain input-output data at a sampling time of 5s. The datasets obtained were used to fit recurrent neural network (RNN) and feedforward neural network (FFNN) models for the system. Thereafter, the identified models were used in the design of predictive controllers. Validation results showed that FFNN gave a better fit than RNN. The closed-loop experimental results also showed the FFNN-based predictive controller displaying an overall superior performance for both servo and regulatory control problem.
机译：过程行业面临着多方面的问题，包括高度的非线性和综合工艺，高能源成本和严格的环境法规。解决这些问题的传统方法是次优。自出生以开发有效的设计方法，对产品变异性降低和利润率提高的工业流程的最佳解决方案。因此，本研究调查了两个人工神经网络（ANN）应用于实验级联三罐系统的建模和预测控制 - 一种三×3多变量和非线性工艺。为此，通过设计良好的输入信号激发罐过程，以在5S的采样时间获得输入输出数据。获得的数据集用于适用于系统的经常性神经网络（RNN）和前馈神经网络（FFNN）模型。此后，在预测控制器的设计中使用了所识别的模型。验证结果表明，FFNN比RNN更好地适应。闭环实验结果还显示了基于FFNN的预测控制器，对伺服和监管控制问题显示了整体卓越的性能。

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