Abstract
Orthonormal basis function (OBF) models have several advantages and recently find applications in model-based and fault tolerant controllers, due to its computational efficiency, consistency, linearity and parsimonious nature of parameters. OBF models use a priori knowledge of system dynamics in the form of dominant poles to reduce the model order. The OBF model accuracy improves and becomes more parsimonious as the estimate of poles used in the OBF filters is more closer to the system dynamics. The optimal class of OBF model is also selected from the knowledge of nature of dominant poles. The available methods are mainly based on simple step response or graphical analysis. In this paper, an optimisation-based approach is proposed and then validated for different processes for estimating the dominant poles of the process from a broad input–output identification data. The method also discussed an iterative approach to separately compute process time delay and further improve the estimation of dominant poles. It can be further extended to develop the approximate process first-order plus time delay (FOPTD) or second-order plus time delay (SOPTD) model.
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Seban, L., Roy, B.K. (2019). Development of Parsimonious Orthonormal Basis Function Models Using Particle Swarm Optimisation. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_43
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DOI: https://doi.org/10.1007/978-981-13-1132-1_43
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