RESUMEN
In this study, a novel estimation scheme is proposed for identifying extended Wiener-Hammerstein systems with hysteresis nonlinearity subject to quantised measurements. The proposed scheme is established in a self-error learning framework to achieve high-performance parameter estimation compared with classic error feedback learning estimation algorithms. Initially, the useful identification data can be extracted from contaminated system data by introducing an adaptive filter. Then, with the help of the filtered data, the identification error expression used to establish the estimator is derived. Subsequently, an online compensation estimation error variable is proposed to eliminate the effect of the regression vector on the convergence performance. A new adaptive law is designed with adaptive recursive gain, considering the compensation estimation error data and parameter initial error data. Under general persistent excitation (PE) condition, the PE condition of the regressor information is verified online, and the estimator convergence is strictly proven. Finally, the statistical results of two illustrated examples and a real-word example are provided to validate the positive features and effectiveness of the proposed estimation scheme.
RESUMEN
Objective. Mental workload is the result of the interactions between the demands of an operation task, the environment in which the task is performed, and the skills, behavior and perception of the performer. Working under a high mental workload can significantly affect an operator's ability to choose optimal decisions, judgments and motor actions while operating an unmanned aerial vehicle (UAV). However, the effect of mental schema, which reflects the level of expertise of an operator, on mental workload remains unclear. Here, we propose a theoretical framework for describing how the evolution of mental schema affects mental workload from the perspective of cognitive processing.Approach. We recruited 51 students to participate in a 10-day simulated quadrotor UAV flight training exercise. The EEG power spectral density (PSD)-based metrics were used to investigate the changes in neural responses caused by variations in the mental workload at different stages of mental schema evolution.Main results. It was found that the mental schema evolution influenced the direction and change trends of the frontal theta PSD, parietal alpha PSD, and central beta PSD, which are EEG indicators of mental workload. Initially, before the mental schema was formed, only the frontal theta PSD increased with increasing task difficulty; when the mental schema was initially being developed, the frontal theta PSD and the parietal alpha PSD decreased with increasing task difficulty, while the central beta PSD increased with increasing task difficulty. Finally, as the mental schema gradually matured, the trend of the three indicators did not change with increasing task difficulty. However, differences in the frontal PSD became more pronounced across task difficulty levels, while differences in the parietal PSD narrowed.Significance. Our results describe the relationship between the EEG PSD and the mental workload of UAV operators as the mental schema evolved. This suggests that EEG activity can be used to identify the mental schema and mental workload experienced by operators while performing a task, which can not only provide more accurate measurements of mental workload but also provide insights into the development of an operator's skill level.