Abstract
Internal Model Control (IMC) technique is one of the well accepted model-based controller designing methodologies which is widely accepted in process industries due to their simplicity and ease of tuning. For controlling nonlinear processes IMC controllers are designed based on the linear approximation of nonlinear models. As a result IMC controllers sometimes fail to provide satisfactory performance under model uncertainty and large load variations with its fixed settings. Here we propose an adaptive IMC-PI controller for a level control process where the IMC tuning parameter, i.e., the close-loop time constant (\( \lambda \)) is varied based on a set of predefined fuzzy rules depending on the process operating conditions in terms of process error (\( e \)) and change of error (\( \Delta e \)). Two sets of rule bases are used consisting of 25 and 9 rules for online fuzzy tuning of the IMC-PI controller. Widely different choice of the rule bases defined on two distinct fuzzy partitions justify the effectiveness as well as general applicability of the proposed scheme.
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Nath, U.M., Dey, C., Mudi, R.K. (2017). Fuzzy-Based Adaptive IMC-PI Controller for Real-Time Application on a Level Control Loop. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_38
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DOI: https://doi.org/10.1007/978-981-10-3153-3_38
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