Optimal input design for on-line identification: a coupled observer-MPC approach

This paper presents a parametric sensitivity based controller for on line optimal model parameter identification using constrained closed loop control tools and an observer. In optimal input design problem, analytical solution exists for few particular cases based on a relatively simple model. The approach proposed here may be used for a process based on a continuous model in the time domain, with two assumptions on the observability and the general structure of the model. The new proposed approach is to solve a model predictive control problem coupled with an on line process parameter estimation at each time using an observer. A dynamic parametric sensitivity model (derived from the process model) is also used on line to get the parametric sensitivity that has to be optimized. Both optimal input and estimated model parameter are therefore obtained on line. The case study presented here is a powder coating curing process where the main thermal parameter to identify influences the powder curing. First simulation results show here the efficiency of the approach in the control software (MPC@CB) developed under Matlab.