Performance Analysis of Cascade Tank System Using Deep Learning Controller

Abstract

The conventional proportional–integral–derivative (PID) controller is used in the majority of process industries. Although the most commonly used, the classic PID controller has several drawbacks like variable performance for the non-linear system, multivariable control design is not straightforward; the response is influenced by dead time, no constraints involvement, etc. The field of process control systems has grown quickly, and other controllers have been developed that try to overcome the weakness of PID controllers. Advances in artificial neural networks, specifically deep learning, have widened the application domain of process control systems. In this paper, a cascaded tank system, which is a benchmark problem, has been simulated. The input-output data of the plant has been generated and used to train a deep-learning controller using backpropagation. Several measures, such as time response, frequency response, and signal statics performance indices, are used to evaluate the outcomes of the proposed controller. The proposed model performs better on every assessment criterion than the traditional controller.

KEYWORDS:

• Artificial intelligence
• ANN
• Automatic control
• Deep Learning
• Feedback control
• PID controller

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Bhawesh Prasad

Bhawesh Prasad received the BE and Mtech degrees in instrumentation and control engineering from SLIET Longowal, India, in 2017 and 2019. He is a research scholar with the Department of EIE, SLIET, Longowal. Punjab-148106, India. His area of research interest includes process control systems and soft computing. Corresponding author. Email: [email protected]

Raj Kumar Garg

Raj Kumar Garg received the PhD degree from IIT Delhi, India, in 2016. He is an associate professor with the Department of EIE, SLIET, Longowal. Punjab-148106, India. His area of research interest includes process control systems, soft computing techniques, digital signal processing, and power quality. Email: [email protected]

Manmohan Singh

Manmohan Singh received an ME degree in electrical engineering from the University of Roorkee, Roorkee, India, and a PhD from SLIET, Longowal, India. He is an associate professor with the Department of EIE, SLIET, Longowal. Punjab-148106, India. His current research interests include channel bank filters, linear phase filters, probability, multi-objective optimisation, metaheuristics, quadrature mirror filters and deep learning. Email: [email protected]