Advancing Industrial Process Control With Deep Learning-Enhanced Model Predictive Control for Nonlinear Time-Delay Systems

In the process industries, nonlinear and large time-delay systems pose significant challenges for efficient model predictive control (MPC). The advent of deep learning offers innovative techniques for precise modeling and control; however, deep neural architectures have limited application in control problems. This study introduces a deep neural networks-based model predictive control (DNNs-MPC) that can utilize various gradient-based neural network models as predictors, enhancing the predictive capabilities of MPC and improving performance for nonlinear systems with large time-delay. To achieve this, we first employ dilated convolution and recurrent neural networks to develop a dynamic system modeling predictor, effectively capturing the system's nonlinear characteristics. Concurrently, to address challenges associated with the objective function, we propose an optimization strategy that incorporates three objective functions and employs a multistage weight optimization method to improve control performance and ensure output stability. Furthermore, to derive the optimal control strategy, an adaptive gradient descent method is applied to accelerate the solution process and quickly obtain optimal control signals. Finally, the effectiveness of our method is validated through numerical simulations and a case study of an industrial rotary kiln, demonstrating significant improvements in control performance, system stability, and response accuracy.