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

This paper develops an on-line model parameter identification approach for multivariable 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.