Data-driven design of two degree-of-freedom nonlinear controllers: The D2-IBC approach

作者: Carlo Novara , Simone Formentin , Sergio M. Savaresi , Mario Milanese

DOI: 10.1016/J.AUTOMATICA.2016.05.010

关键词:

摘要: In this paper, we introduce and discuss the Data-Driven Inversion-Based Control ( D 2 -IBC) method for nonlinear control system design. The relies on a two degree-of-freedom architecture, with controller linear running in parallel, does not require any detailed physical knowledge of plant to control. Specifically, use input/output data synthesize by employing convex optimization tools. We show effectiveness proposed approach benchmark simulation example, regarding Duffing system.

参考文章(43)
Lalo Magni, Davide Martino Raimondo, Frank Allgöwer, None, Nonlinear model predictive control : towards new challenging applications Springer. ,(2009)
Rolf Findeisen, Tobias Raff, Frank Allgöwer, Sampled-Data Nonlinear Model Predictive Control for Constrained Continuous Time Systems Lecture Notes in Control and Information Sciences. pp. 207- 235 ,(2007) , 10.1007/978-3-540-37010-9_7
Michel Gevers, Connecting identification and robust control Springer, Berlin, Heidelberg. pp. 35- 37 ,(1994) , 10.1007/BFB0036246
Carlo Novara, Mario Milanese, Control of nonlinear systems: a model inversion approach. arXiv: Systems and Control. ,(2014)
S. Formentin, S.M. Savaresi, L. Del Re, Non-iterative direct data-driven controller tuning for multivariable systems: theory and application Iet Control Theory and Applications. ,vol. 6, pp. 1250- 1257 ,(2012) , 10.1049/IET-CTA.2011.0204
Patrizio Tomei, Riccardo Marino, Nonlinear control design: geometric, adaptive and robust Nonlinear control design: geometric, adaptive and robust. pp. 396- 396 ,(1996)
Håkan Hjalmarsson, Michel Gevers, Franky de Bruyne, For model-based control design, closed-loop identification gives better performance Automatica. ,vol. 32, pp. 1659- 1673 ,(1996) , 10.1016/S0005-1098(96)80003-3
G. Ravi Sriniwas, Yaman Arkun, A global solution to the nonlinear model predictive control algorithms using polynomial ARX models Computers & Chemical Engineering. ,vol. 21, pp. 431- 439 ,(1997) , 10.1016/S0098-1354(96)00279-7
D.Q. Mayne, J.B. Rawlings, C.V. Rao, P.O.M. Scokaert, Survey Constrained model predictive control: Stability and optimality Automatica. ,vol. 36, pp. 789- 814 ,(2000) , 10.1016/S0005-1098(99)00214-9