Time-Varying Human-Operator Identification with Box-Jenkins Models

Abstract

The identification of time-varying, adaptive behaviour of a human operator in basic manual control tasks is undoubtedly under development since most methodologies only account for time-invariant systems. Previous authors have proved that estimation techniques based on ARX structures can generally identify the HO model parameters. Nonetheless, ARX methods present several problems, such as the persistent bias in estimates that may increase due to coupled noise and system models. Therefore, a novel identification technique based on Box Jenkins models is proposed to achieve a more adequate match between the estimator structure and the HO model. The identification process can be conducted offline by the Ordinary Least Squares and Prediction Error Method, or online, when Recursive Least Squares and Recursive PEM are employed, respectively, in ARX and BJ models. The BJ estimator has excellent potential as an identification tool due to its bias reduction capabilities, as clearly shown in batch-fitting, although non-linear optimization processes decrease its convergence speed by 500%. An RPEM algorithm with forgetting factor 𝝀 = 0.99609 and first-order remnant BJ structure is implemented and tested under Monte Carlo simulation and experimental data. Recursive BJ algorithms could help to achieve the ideal identification method by diminishing the Neuro-Muscular parameter bias in ARX.