A new tuning method for internal model controllers (IMCs) is presented. The parameters of an IMC can be structurally assigned to two groups: 1) parameters of the internal model and 2) parameters of the controller. The method described in this brief suggests a sequential tuning of the two parameter groups. For both groups, the parameter values are found by minimizing a predefined cost function. The optimization is run with a gradient-based minimization procedure where, analogously to the well-known iterative feedback tuning (IFT) scheme, the gradients are computed from signals obtained from closed-loop experiments. Thus, for the calculation of the gradient, the unknown plant is utilized, whereas other ?local? tuning methods suggest the replacement of the real plant by its model to calculate the gradient. The main advantages of the suggested algorithm are its inherent operation in the closed control loop and the fact that, for the tuning of the internal model, no information about the disturbance model is required. The method can be used either for an initial tuning of the controller or for autotuning during operation.
['Daniel Rupp', 'Lino Guzzella']
Iterative Tuning of Internal Model Controllers With Application to Air/Fuel Ratio Control