Real-time data-driven PID controller for multivariable process employing deep neural network

The complex industrial processes exhibiting nonstationary and multivariable with time-varying dynamics result in low accuracy. Also, stability compensation is difficult to be obtained by a conventional PID controller. Hence, a deep learning-based data-driven PID controller is designed for unmodeled dynamics compensation for complex industrial processes. In this research work, a nonlinear PID controller is designed with a deep neural network (DNN) model from unmodeled dynamics of the complex industrial processes. To validate the performance, results from stability compensation and convergence of the model parameters for closed-loop systems were obtained. When tested on a real-time twin tank system, it achieved an accurate output flowrate with 97.65% accuracy and 1.89% peak overshoot compared with conventional PID controller. Both simulated and experimental results validate that proposed controller has improved stability and uniform convergence of system variables. The proposed deep learning-based PID controller was employed on a twin tank control system. This confirms the feasibility and practical application of a real-time complex process.