ABSTRACT
Internal model control (IMC) based proportional integral derivative (PID) controller design is found to be a well-explored topic over the last three decades. Possibly, this model-based controller design technique has got wide acceptance among process engineers due its simple tuning and clear functionality. Literature survey reveals that IMC has three important features, i.e. dual stability, perfect control, and zero offset. Another most interesting feature of IMC is that it has the sole tuning parameter, i.e. closed loop time constant () which is required to be adjusted for achieving the desired closed loop response. Various guidelines for choosing the appropriate value of
depending on the process characteristic and desired closed loop performance are provided in a good number of research findings. Especially, the design of the IMC controller for integrating and unstable processes is really a challenging task and hence widely explored by researchers. In this study, a brief review is provided on IMC-PID tuning for various classes of single input single output (SISO) processes along with a table containing their tuning relations. In addition, IMC controllers with adaptive, auto-tuning, and fractional-order structure have also been discussed here. However, in practice most of the industrial processes are multi input multi output (MIMO) in nature and hence, this review also includes significant discussions on IMC tuning strategies for MIMO processes. Real-time implementations of IMC techniques reported in the literature are also discussed. Finally, a comparative study is provided through real-time experimentation on a coupled tank system.
Additional information
Notes on contributors
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Ujjwal Manikya Nath
Ujjwal Manikya Nath is an assistant professor in the Department of Instrumentation Engineering, Jorhat Engineering College, Jorhat, Assam, India. He received BTech in electronics and instrumentation engineering in 2010 from Academy of Technology under West Bengal University of Technology, MTech in instrumentation and control engineering, Department of Applied Physics in 2015 from University of Calcutta and PhD from Department of Instrumentation and Electronics Engineering, Jadavpur University in 2020. From 2011 to 2012, he worked as junior project assistant in the Department of Electronics and Electrical Communication Engineering, IIT Kharagpur, India. His research interest involves designing of model-based controller, multivariable processes and intelligent process control techniques.
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Chanchal Dey
Chanchal Dey is an associate professor in the Instrumentation Engineering Section of the Department of Applied Physics, University of Calcutta, India. He received BTech in instrumentation and electronics engineering in 1996 from Jadavpur University, MTech in instrumentation and control engineering in 1999 from University of Calcutta and PhD from Jadavpur University in 2010. From 1996 to 1997, he worked as a process engineer in Gas Authority of India Ltd. He was a guest faculty in the Department of Instrumentation and Electronics Engineering at Jadavpur University from 1999 to 2005. His research interest involves design of intelligent process control techniques using conventional and soft-computing tools. Email: [email protected]
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Rajani K. Mudi
Rajani K Mudi is a professor in the Department of Instrumentation and Electronics Engineering, Jadavpur University, India. He received BTech and MTech in 1990 and 1992, respectively from University of Calcutta, and PhD from Jadavpur University in 1999. His research interests are in intelligent control, neuro-fuzzy systems, qualitative modeling and bioinformatics. He visited National Chiao Tung University and National Taiwan University, Taiwan, during October 2005 to May 2007. He was the Coordinator of AFSS-2002 and the Secretary of ICONIP-2004. He co-edited a volume ‘Neural Information Processing’ by Springer-Verlag, Germany, 2004 and served as a Guest co-editor of a special issue of International Journal of Intelligent Systems (2003). He is an Associate Editor of IET Electronics Letters. Email: [email protected]