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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Deep Learning-Based Model Predictive Control for Continuous Stirred-Tank Reactor System
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Deep Learning-Based Model Predictive Control for Continuous Stirred-Tank Reactor System

机译：基于深度学习的连续搅拌罐式反应器系统的模型预测控制

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摘要

A continuous stirred-tank reactor (CSTR) system is widely applied in wastewater treatment processes. Its control is a challenging industrial-process-control problem due to great difficulty to achieve accurate system identification. This work proposes a deep learning-based model predictive control (DeepMPC) to model and control the CSTR system. The proposed DeepMPC consists of a growing deep belief network (GDBN) and an optimal controller. First, GDBN can automatically determine its size with transfer learning to achieve high performance in system identification, and it serves just as a predictive model of a controlled system. The model can accurately approximate the dynamics of the controlled system with a uniformly ultimately bounded error. Second, quadratic optimization is conducted to obtain an optimal controller. This work analyzes the convergence and stability of DeepMPC. Finally, the DeepMPC is used to model and control a second-order CSTR system. In the experiments, DeepMPC shows a better performance in modeling, tracking, and antidisturbance than the other state-of-the-art methods.
机译：连续搅拌罐反应器（CSTR）系统广泛应用于废水处理过程中。它的控制是一个具有挑战性的工业过程控制问题，因为难以实现准确的系统识别。这项工作提出了基于深度学习的模型预测控制（DeepMPC）来模拟和控制CSTR系统。建议的深度可以由不断增长的深度信仰网络（GDBN）和最佳控制器组成。首先，GDBN可以通过传输学习自动确定其大小，以在系统识别中实现高性能，并且它就像受控系统的预测模型一样。该模型可以准确地近似于均匀最终界限误差的受控系统的动态。其次，进行二次优化以获得最佳控制器。这项工作分析了DeepMPC的收敛性和稳定性。最后，DeepMPC用于模拟和控制二阶CSTR系统。在实验中，DeepMPC在模型，跟踪和防静脉中显示出比其他最先进的方法更好的性能。

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