Abstract
Predictive functional control (PFC) has emerged as a popular industrial choice owing to its simplicity and cost-effectiveness. Nevertheless, its efficacy diminishes when dealing with challenging dynamics because of prediction mismatch in such scenarios. This paper presents a proposal for reducing prediction mismatch and thus improving behaviour for simple unstable processes; a two-stage design methodology pre-stabilises predictions via proportional compensation before introducing the PFC component. It is demonstrated that pre-stabilisation reduces the dependency of the closed-loop pole on the coincidence point and also improves robustness to uncertainty. Simulation results verify the improved performance as compared to conventional PFC.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Richalet, J., O’Donovan, D.: Elementary predictive functional control: a tutorial. In: 2011 International Symposium on Advanced Control of Industrial Processes (ADCONIP), pp. 306–313 (2011)
Richalet, J., O’Donovan, D.: Predictive Functional Control: Principles and Industrial Applications. Springer Science and Business Media, London (2009). https://doi.org/10.1007/978-1-84882-493-5
Rossiter, J.A.: A First Course in Predictive Control. Second Edition. CRC Press, Boca Raton (2018). https://doi.org/10.1201/9781315104126
Rossiter, J., Haber, R.: The effect of coincidence horizon on predictive functional control. Processes 3(1), 25–45 (2015). https://doi.org/10.3390/pr3010025
Rossiter, J.: Input shaping for PFC: how and why? J. Control Decis. 3(2), 105–118 (2016)
Abdullah, M., Rossiter, J.A.: Using Laguerre functions to improve the tuning and performance of predictive functional control. Int. J. Control. 93(9), 1–13 (2019)
Zhang, Z., Rossiter, J.A., Xie, L., Su, H.: Predictive functional control for integral systems. In: 13th International Symposium on Process Systems Engineering (2018)
Rossiter, J.A., Kouvaritakis, B., Rice, M.: A numerically robust state-space approach to stable-predictive control strategies. Automatica 34(1), 65–73 (1998)
Mayne, D.Q., Rawlings, J.B., Rao, C.V., Scokaert, P.O.: Constrained model predictive control: stability and optimality. Automatica 36(6), 789–814 (2000)
Abdullah, M., Rossiter, J.A.: The effect of model structure on the noise and disturbance sensitivity of predictive functional control. In: 2018 European Control Conference (ECC). IEEE (2018). https://doi.org/10.23919/ecc.2018.8550374
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Aftab, M.S., Rossiter, J.A., Zhang, Z. (2021). Predictive Functional Control for Unstable First-Order Dynamic Systems. In: Gonçalves, J.A., Braz-César, M., Coelho, J.P. (eds) CONTROLO 2020. CONTROLO 2020. Lecture Notes in Electrical Engineering, vol 695. Springer, Cham. https://doi.org/10.1007/978-3-030-58653-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-58653-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58652-2
Online ISBN: 978-3-030-58653-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)