1. A computer implemented method for on-line auditing and updating a multiple input multiple output (MIMO) dynamical model of a process, the method comprising: given a hardware processor implementing an online controller having an existing dynamical model of the process, in a computer: a. Using the existing dynamical model in the online controller as initial model to configure a tester; b. Perturbing process variables of the process; c. Collecting and slicing process data; d. Estimating the model quality and identifying poor performing submodels of the dynamical model, the estimating and identifying comprising: dividing the MIMO dynamical model into a plurality of multiple input single output (MISO) submodels, factoring each MISO submodel into single input single output (SISO) dynamic model outputs and associated gain multipliers resulting in one or more factored SISO submodels, estimating the gain multipliers from the collected process data by use of a gain multiplier estimation algorithm, for each factored SISO submodel, calculating a respective dynamic model uncertainty by use of a dynamic uncertainty estimation algorithm, for each factored SISO submodel, calculating a respective frequency-domain uncertainty by use of an asymptotic variance algorithm, assessing overall quality of each submodel of the dynamical model by use of a set of fuzzy logic rules based on the factored SISO submodels, and identifying poor-performing submodels of the dynamical model based on the overall quality assessment; e. Further perturbing the process variables relevant to the identified poor performing submodels; f. Re-identifying those poor performing submodels, resulting in newly identified submodels; g. Updating the online controller model with the newly identified submodels, wherein model update is performed online, with the process continuing to operate within normal operating parameters of the process, and said updating resulting in an improved model for the online controller.