Objective ARX Model Order Selection for Multi-Channel Human Operator Identification

In manual control, the human operator primarily responds to visual inputs but may elect to make use of other available feedback paths such as physical motion, adopting a multi-channel control strategy. Hu- man operator identification procedures generally require a priori selection of the model structure, which can be problematic as the exact feedback organization operators adopt is not always clear in advance. This pa- per evaluates a novel method for objectively detecting the presence of additional human operator feedback responses in control tasks with multiple inputs. The approach makes use of linear-time invariant ARX mod- els for system identification, combined with an objective model selection criterion. To test the method, an experiment was conducted in which participants performed a compensatory yaw attitude tracking task in a moving-base flight simulator, with varying motion cueing settings. In addition, a pursuit tracking condition without motion feedback was tested. For all conditions, the objective ARX model-based identification method was used to verify the presence of a possible additional human operator output feedback response. With ap- propriate tuning of the penalty on model complexity in the model selection criterion, the methodology proved successful in correctly identifying the additional operator responses in experimental conditions that contained no motion or high-quality motion feedback. With low-fidelity motion feedback or a pursuit display, the results suggest that no consistent feedback response is achieved by the participants. The approach was substantiated with offline Monte Carlo simulations, which show strong correlation with the obtained experiment results.