This paper addresses the robust and accurate trajectory tracking problem for unmanned helicopters in the presence of model uncertainties and external disturbances. First, the helicopter's model is simplified to a six-degrees-of-freedom rigid body augmented with a simplified rotor dynamic model, with the model uncertainties and the external disturbances being treated as lumped unknown disturbances. Second, a nonlinear disturbance observer is designed to estimate this lumped disturbance. Then, a backstepping controller with disturbance compensation is designed to ensure robust and highly trajectory tracking. After that, the theoretical analysis of the efficiency of the designed disturbance observer-based backstepping controller (Backstepping+DO) is shown by the Lyapunov theory. Finally, simulation results and conclusions are presented and discussed.

The rising number of traffic accidents has become a major issue in our daily life, which has attracted the concern of society and governments. To deal with this issue, in our previous study, we have designed a real-time driving fatigue detection system using power spectrum density and sample entropy. By using the wireless technology and dry electrodes for EEG collection, we further integrated virtual reality simulated driving environment, which made our study more applicable to realistic settings. However, the high accuracy of classification for driving fatigue has not been obtained. To measure the time series complexity of the EEG signal, we proposed a fusion entropy (sample entropy, approximate entropy, and spectral entropy) analysis method of EEG and EOG. First, a sample entropy was applied for feature extraction from the horizontal and vertical EOG. Second, an approximate entropy, sample entropy, and spectral entropy features of each sub-band of EEG are extracted. Third, feature fusion for sub-band is performed by canonical correlation analysis (CCA). Finally, the features of EOG and EEG are classified using a relevant vector machine (RVM). Twenty-two subjects participated in the driving fatigue experiments for a duration of 90 min. The results demonstrated that the fusion entropy analysis combining EOG and EEG could provide an alternative method for driving fatigue detection, and the average accuracy rate was up to 99.1 ± 1.2%. The authors further analyzed the effect of feature fusion in four sub-bands (δ, α, β, and θ) and compared with every single sub-band on classification performance, it is proved that the former is superior to the latter presenting the proposed method can provide effective indicators for driving fatigue detection. INDEX TERMSDriving fatigue, electroencephalogram (EEG), electrooculogram (EOG), sample entropy, approximate entropy, spectral entropy.

Brain-computer interface (BCI) is a novel human-computer interaction model, which does not depend on the conventional output pathway (peripheral nerve and muscle tissue). In the past three decades, it has attracted the interest of researchers and gradually become a research hotspot. As a typical BCI application, the brain-controlled wheelchair (BCW) could provide a new communicating channel with the external environment for physically disabled people. However, the main challenge of BCW is how to decode multi-degree of freedom control instruction from electroencephalogram (EEG) as soon as possible. The research progress of BCW has been developed rapidly over the past fifteen years. In this review, we investigate the BCW from multiple perspectives, include the type of signal acquisition, the pattern of commands for the control system and the working mechanism of the control system. Furthermore, we summarize the development trend of BCW based on the previous investigation, and it is mainly manifested in three aspects: from a wet electrode to dry electrode, from single-mode to multi-mode, and from synchronous control to asynchronous control. With the continuous development of BCW, we also find new functions have been introduced into BCW to increase its stability and robustness. It is believed that BCW will be able to enter the real-life from the laboratory and will be widely used in rehabilitation medicine in the future.

Multiple degrees of freedom (DOF) commands are required for a brain-actuated virtual automatic car, which makes the brain-computer interface (BCI) control strategy a big challenge. In order to solve the challenging issue, a mixed model of BCI combining P300 potentials and motor imagery had been realized in our previous study. However, compared with single model BCI, more training procedures are needed for the mixed model and more mental workload for users to bear. In the present study, we propose a multiple patterns of motor imagery (MPMI) BCI method, which is based on the traditional two patterns of motor imagery. Our motor imagery BCI approach had been extended to multiple patterns: right-hand motor imagery, left-hand motor imagery, foot motor imagery, and both hands motor imagery resulting in turning right, turning left, acceleration, and deceleration for a virtual automatic car control. Ten healthy subjects participated in online experiments, the experimental results not only show the efficiency of our proposed MPMI-BCI strategy but also indicate that those users can control the virtual automatic car spontaneously and efficiently without any other visual attention. Furthermore, the metric of path length optimality ratio (1.23) is very encouraging and the time optimality ratio (1.28) is especially remarkable. Graphical Abstract The paradigm of multiple patterns of motor imagery detection and the relevant topographies of CSP weights for different MI patterns.

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