Abstract
Multi-model approaches for non-linear control usually consist in partitioning the operating space of a given non-linear process, in such a way, it is possible to approximate a local linear model for each partition. That allows using standard control and tuning techniques for each local linear model. After that, a global controller merging the set of local controllers is defined to control the original non-linear process. This global controller merges the local controllers, given that a single controller will exhibit different trade-offs in its performance in different operating regions; therefore, a single controller could exhibit a deficient performance in the overall operating space. In this paper, we exploit this fact from a multi-objective optimisation point of view, to tune a single controller using a parametric multi-model, looking for a suitable controller with a preferable trade-off along with the overall operating space.







Similar content being viewed by others
Notes
A maximisation problem can be converted to a minimisation problem. For each of the objectives that have to be maximised, the transformation: \(\max J_i({\varvec{x}}) = -\min (-J_i({\varvec{x}}))\) could be applied.
Available at: https://es.mathworks.com/matlabcentral/fileexchange/65145.
References
Ali MMA, et al. Multi-objective Lyapunov-based controller design for nonlinear systems via genetic programming. Neural Comput Appl. 2021;34(2):1345–57.
Åström KJ, Hägglund T. Revisiting the Ziegler–Nichols step response method for pid control. J Process Control. 2004;14(6):635–50.
Böling JM, Seborg DE, Hespanha JP. Multi-model adaptive control of a simulated ph neutralization process. Control Eng Pract. 2007;15:663–72.
Carrau JV, Reynoso-Meza G, García-Nieto S, Blasco X. Enhancing controllerś tuning reliability with multi-objective optimisation: from model in the loop to hardware in the loop. Eng Appl Artif Intell. 2017;64:52–66.
Coello CAC, Lamont GB. Applications of multi-objective evolutionary algorithms, vol. 1. Singapore: World Scientific; 2004.
Das S, Suganthan PN. Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput. 2010;15:4–31.
Du J, Johansen TA. Integrated multimodel control of nonlinear systems based on gap metric and stability margin. Ind Eng Chem Res. 2014;53:10206–15.
Du J, Johansen TA. Control-relevant nonlinearity measure and integrated multi-model control. J Process Control. 2017;57:127–39.
Du J, Song C, Li P. Application of gap metric to model bank determination in multilinear model approach. J Process Control. 2009;19:231–40.
Du J, Song C, Yao Y, Li P. Multilinear model decomposition of mimo nonlinear systems and its implication for multilinear model-based control. J Process Control. 2013;23:271–81.
Falcón-Cardona JG, Coello CAC. Indicator-based multi-objective evolutionary algorithms: a comprehensive survey. ACM Comput Surv (CSUR). 2020;53(2):1–35.
Galán O, Romagnoli JA, Palazoglu A. Robust h\(\infty\) control of nonlinear plants based on multi-linear models: an application to a bench-scale ph neutralization reactor. Chem Eng Sci. 2000;55:4435–50.
Galan O, Romagnoli JA, Palazoglu A. Real-time implementation of multi-linear model-based control strategies–an application to a bench-scale ph neutralization reactor. J Process Control. 2004;14:571–9.
Galán O, Romagnoli JA, Palazoglu A, Arkun Y. Gap metric concept and implications for multilinear model-based controller design. Ind Eng Chem Res. 2003;42:2189–97.
Galán O, Romagnoli JA, Palazoğlu A, Arkun Y. Experimental verification of gap metric as a tool for model selection in multi-linear model-based control. IFAC Proc Volumes. 2004;37:257–61.
Garpinger O, Hägglund T, Åström KJ. Performance and robustness trade-offs in PID control. J Process Control. 2014;24:568–77.
Ge M, Chiu MS, Wang QG. Robust pid controller design via lmi approach. J Process Control. 2002;12:3–13.
Hlava J, Hubka L, Tuma L. Multi model predictive control of a power plant heat exchanger network based on gap metric. In: booktitle2012 16th International Conference on System Theory, Control and Computing (ICSTCC); 2012. p. 1–6.
Hosseini S, Fatehi A, Johansen TA, Sedigh AK. Multiple model bank selection based on nonlinearity measure and h-gap metric. J Process Control. 2012;22:1732–42.
Hu H, Xu L, Goodman ED, Zeng S. Nsga-ii-based nonlinear pid controller tuning of greenhouse climate for reducing costs and improving performances. Neural Comput Appl. 2014;24:927–36.
Jeyasenthil R, Nataraj P. A multiple model gap-metric based approach to nonlinear quantitative feedback theory. IFAC-PapersOnLine. 2016;49:160–5.
Marques T, Reynoso-Meza G. Applications of multi-objective optimisation for pid-like controller tuning: a 2015–2019 review and analysis. IFAC-PapersOnLine. 2020;53:7933–40.
Meza GR, Ferragud XB, Saez JS, Durá JMH. Controller Tuning with evolutionary multiobjective optimization: a holistic multiobjective optimization design procedure, vol. 85. Berlin: Springer; 2016.
Miettinen K. Nonlinear multiobjective optimization. In: International series in operations research and management science, vol. 12. Springer, New York, NY, 1999. https://doi.org/10.1007/978-1-4615-5563-6
Murray-Smith R, Johansen T. Multiple model approaches to nonlinear modelling and control. Boca Raton: CRC Press; 1997.
Perez J, Odloak D, Lima E. Multi-model mpc with output feedback. Braz J Chem Eng. 2014;31:131–44.
Pourbabaee B, Meskin N, Khorasani K. Sensor fault detection, isolation, and identification using multiple-model-based hybrid Kalman filter for gas turbine engines. IEEE Trans Control Syst Technol. 2016;24:1184–200.
Puschke J, Zubov A, Kosek J, Mitsos A. Multi-model approach based on parametric sensitivities - a heuristic approximation for dynamic optimization of semi-batch processes with parametric uncertainties. Comput Chem Eng. 2017;98:161–79.
Reynoso-Meza G. Extended multi-objective differential evolution with spherical pruning, ¡ spmodex ¿ algorithm. 2018. https://www.mathworks.com/matlabcentral/fileexchange/65145. Accessed 1 Dec 2021
Reynoso-Meza G, Sanchis J, Blasco X, García-Nieto S. Physical programming for preference driven evolutionary multi-objective optimization. Appl Soft Comput. 2014;24:341–62.
Reynoso-Meza G, Sanchis J, Blasco X, Martínez M. Controller tuning using evolutionary multi-objective optimisation: current trends and applications. Control Eng Pract. 2014;28:58–73.
Rodríguez-Molina A, Mezura-Montes E, Villarreal-Cervantes MG, Aldape-Pérez M. Multi-objective meta-heuristic optimization in intelligent control: a survey on the controller tuning problem. Appl Soft Comput. 2020;93: 106342.
Sánchez HS, Padula F, Visioli A, Vilanova R. Tuning rules for robust fopid controllers based on multi-objective optimization with fopdt models. ISA Trans. 2017;66:344–61.
Shaghaghi D, Fatehi A, Khaki-Sedigh A. Multi-linear model set design based on the nonlinearity measure and h-gap metric. ISA Trans. 2017;68:1–13.
Tan GT, Chiu MS. A multiple-model approach to decentralized internal model control design. Chem Eng Sci. 2001;56:6651–60.
Toscano R. A simple robust pi/pid controller design via numerical optimization approach. J Process Control. 2005;15:81–8.
Vinnicombe G. Frequency domain uncertainty and the graph topology. IEEE Trans Autom Control. 1993;38:1371–83.
Zhang Q, Li H. Moea/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput. 2007;11(6):712–31.
Zitzler E, Thiele L, Laumanns M, Fonseca CM, Da Fonseca VG. Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans Evol Comput. 2003;7(2):117–32.
Zribi A, Chtourou M, Djemel M. Multiple model reduction approach using gap metric and stability margin for control nonlinear systems. Int J Control Autom Syst. 2017;15:267–73.
Acknowledgements
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and the Fundação Araucária (FAPPR)—Brazil—Finance Codes: 310079/2019-5-PQ2, 4408164/2021-2-Univ and 51432/2018-PPP.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author declares no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Reynoso-Meza, G., Carrillo-Ahumada, J., Alves Ribeiro, V.H. et al. Multi-objective PID Controller Tuning for Multi-model Control of Nonlinear Systems. SN COMPUT. SCI. 3, 351 (2022). https://doi.org/10.1007/s42979-022-01236-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-022-01236-4