Model-Based Design of Experiments in the Presence of Continuous Measurement Systems

Model-based design of experiments (MBDoE) techniques are a useful tool to maximize the information content of experimental trials when the purpose is identifying the set of parameters of a deterministic model in a statistically sound way. Traditionally, the problem of MBDoE has been addressed for discrete measurement systems. In this case, formulation of the optimal design problem is based on maximization of the expected information, usually calculated from discrete forms of the Fisher information matrix. However, current measurement technology allows measurements to be taken at a much higher frequency than in the past, to a point that measurements may be assumed to be obtained in a continuous way. A novel design criterion allowing for the continuous model-based design of the experiments (CMBDoE) is formulated in this paper by optimizing a continuous measurement function of the Fisher information matrix, with the purpose of reaching a statistically satisfactory estimation of model parameters in a computationally efficient way. The benefits of the proposed strategy are discussed by means of two simulated case studies, where the effectiveness of the design is assessed by comparison to a standard MBDoE approach.