Model-based design of experiment (MBDoE) techniques are a useful tool to maximise the information content of experimental trials when the purpose is identifying the set of parameters of a deterministic model in a statistically sound way. In a conventional MBDoE procedure, the information gathered during the evolution of an experiment is exploited only at the end of the experiment itself. Conversely, online model-based redesign of experiment (OMBRE) techniques have been recently proposed to exploit the information as soon as it is generated by the running experiment, allowing for the dynamic update of the experimental conditions to yield the most informative data in order to improve the parameter identification task. However, the effectiveness of MBDoE strategies (including OMBRE) may be severely affected by the presence of systematic modelling errors as well as by disturbances acting on the system. In this paper, a novel experiment design approach (DE-OMBRE) is presented, where a model updating policy including disturbance estimation (DE) is embedded within an OMBRE strategy in order to achieve a statistically satisfactory estimation of the model parameters as well as to estimate the possible discrepancy between the real system and the model being identified. The procedure allows reducing (or even avoiding) constraint violations, preserving the optimality of the redesign even in the presence of systematic errors and/or unknown disturbances acting on the system. Two simulated case studies of different levels of complexity are used to illustrate the benefits of the novel approach. (c) 2011 Elsevier Ltd. All rights reserved.