Robust estimation of a SOPDT model from highly corrupted step response data

Most industrial processes, even though complex in nature, can be represented using second order plus dead-time models, usually determined from step response data, which capture the essential process dynamics. The parameters of these models are computed based on specific algorithms. However, the great majority of these algorithms require system identification basic knowledge and are thus difficult to be used by the process engineer. The focus of this paper is to introduce a new method for computing the parameters of such models. The major advantage over existing methods is that it does not require any system identification expertise, being fully automatic. Additional advantages include robustness to noise, disturbances and system order. All of these are emphasized through several numerical examples, as well as an experimental validation