A Laboratory-Scale Application of Incremental Estimation and Stochastic Embedding

There are three key elements that iterative estimation/controller design techniques require: a closed loop idenification procedure to yield an improved model, an uncertainty quantification procedure that captures the validity range of the new model, and a design procedure that utilises both the model and its uncertainty to generate an improved controller. This paper reports a novel iterative procedure based on estimation of multiplicative increments to generate the successive models, stochastic embedding for quantifying the associated uncertainties and IMC for controller design. The procedure is illustrated on a laboratory process.