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.

中文翻译：

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基于深度学习的连续搅拌釜反应器系统模型预测控制

连续搅拌釜反应器（CSTR）系统广泛应用于废水处理过程。由于难以实现准确的系统识别，其控制是一个具有挑战性的工业过程控制问题。这项工作提出了一种基于深度学习的模型预测控制（DeepMPC）来对 CSTR 系统进行建模和控制。提出的 DeepMPC 由不断增长的深度信念网络 (GDBN) 和最优控制器组成。首先，GDBN 可以通过迁移学习自动确定其大小以实现系统识别的高性能，它只是作为受控系统的预测模型。该模型可以以一致的最终有界误差准确地逼近受控系统的动力学。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 用于建模和控制二阶 CSTR 系统。在实验中，DeepMPC 在建模、跟踪和抗干扰方面表现出比其他最先进的方法更好的性能。