Observer and model predictive control for on-line parameter identification in nonlinear systems

Jun Qian (1) , Pascal Dufour (1) , Madiha Nadri (1)

1 LAGEP - Laboratoire d'automatique et de génie des procédés

This paper develops an on-line model parameter identification approach for mul- tivariable systems, which are nonlinear in terms of state representation and/or in terms of parameters. Combining the observation theory and the model based predictive control theory, an optimal closed loop experiment design for on-line identification of model parameters is given. During only one experiment, an optimal time-varying input is computed to optimize a criterion, while the unknown model parameters are estimated at the same time. The criterion is based on the sensitivities of the model outputs with respect to the unknown parameters that are estimated. The approach does not require to measure all the process state. Moreover output constraints allow to maintain the behaviour into a prescribed region and/or stabilize the process in closed loop. This approach is illustrated through an unstable rolling delta wing with one input, two measured states and five unknown constant model parameters

Mots clés: predictive control; Optimal experiment design; identification; nonlinear systems; observers; predictive control.; ACTI

Domaines: Automatique / Robotique

Jun Qian, Pascal Dufour, Madiha Nadri. Observer and model predictive control for on-line parameter identification in nonlinear systems. IFAC International Symposium on Dynamics and Control of Process Systems (DYCOPS), Dec 2013, Mumbai, India. pp.571-576, ⟨10.3182/20131218-3-IN-2045.00042⟩. ⟨hal-01025666⟩