This paper addresses the problem of adaptive fixed-time attitude stabilization for uncertain rigid spacecraft with inertia uncertainties, external disturbances, actuator saturations, and faults. A nonsingular fixed-time sliding mode surface is constructed so that the settling time of the established surface is independent of the system initial states. By employing an exponential function in the controller design, an adaptive fixed-time control scheme is proposed to shorten the time during which the system states reach the sliding mode surface. With the proposed control method, the information on the bound of the lumped uncertainty is not needed in prior but estimated by the designed update laws. The fixed-time convergence of both the attitude and angular velocity is established, and comparative simulations are presented to illustrate the effectiveness of the proposed control scheme.

For practical mechanical systems, uncertainties/disturbances, such as unmodeled dynamics and frictions, are nonignorable factors. For existing control methods, these factors are usually neglected or addressed by a robust way. As a consequence, the nominal control performance of these methods is sacrificed. Moreover, there exists the chattering problem for some existing robust methods, such as sliding mode control laws. To deal with these drawbacks, a continuous global sliding mode controller along with a nonlinear disturbance observer is designed for the regulation and disturbance estimation control of the overhead crane system. Specifically, the original crane dynamic model is transformed into a quasi-integrator-chain form through some transformations. Then, a nonlinear disturbance observer is designed and a continuous global sliding mode control method is introduced on the basis of the constructed disturbance observer. The stability and convergence characteristics are proven through rigorous theoretical analysis. Finally, to demonstrate the performance of the designed controller, a series of experimental tests are performed, and a comparison study between the devised method here and an existing method is given.

In this article, a neural-network-based adaptive fixed-time control scheme is proposed for the attitude tracking of uncertain rigid spacecrafts. A novel singularity-free fixed-time switching function is presented with the directly nonsingular property, and by introducing an auxiliary function to complete the switching function in the controller design process, the potential singularity problem caused by the inverse of the error-related matrix could be avoided. Then, an adaptive neural controller is developed to guarantee that the attitude tracking error and angular velocity error can both converge into the neighborhood of the equilibrium within a fixed time. With the proposed control scheme, no piecewise continuous functions are required any more in the controller design to avoid the singularity, and the fixed-time stability of the entire closed-loop system in the reaching phase and sliding phase is analyzed with a rigorous theoretical proof. Comparative simulations are given to show the effectiveness and superiority of the proposed scheme.

Alzheimer’s Disease (AD) accounts for 60-70% of all dementia cases, and clinical diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify disease progression or alleviate symptoms are being developed, to assess their efficacy, novel robust biomarkers of brain function are urgently required. This study aims to explore a routine to gain such biomarkers using the quantitative analysis of Electroencephalography (QEEG). This paper proposes a supervised classification framework which uses EEG signals to classify healthy controls (HC) and AD participants. The framework consists of data augmentation, feature extraction, K-Nearest Neighbour (KNN) classification, quantitative evaluation and topographic visualisation. Considering the human brain either as a stationary or a dynamical system, both frequency-based and time-frequency-based features were tested in 40 participants. Results: a) The proposed method can achieve up to 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared to alternative machine learning approaches; b) The features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT; c) In the spatial domain, the temporal and parietal areas offer the best distinction between healthy controls and AD. The proposed framework can effectively classify HC and AD participants with high accuracy, meanwhile offering identification and localisation of significant QEEG features. These important findings and the proposed classification framework could be used for the development of a biomarker for the diagnosis and monitoring of disease progression in AD.

In this article, a predefined-time approximation-free attitude constraint control scheme is proposed for rigid spacecraft with external disturbances. By combining the backstepping technique, an approximation-free controller is systematically developed to maintain the spacecraft attitude within a prescribed small region in predefined time, and the minimum upper bound of the settling time can be exactly given by adjusting a single control parameter. Instead of employing some piecewise continuous functions, the quadratic-fraction functions are constructed in the controllers design to circumvent the possible singularity issue resulted from the differentiation of the virtual controller. With the presented approximation-free control scheme, the computational burden is reduced due to the avoidance of introducing any function approximators. The effectiveness of the proposed scheme is verified by the numerical simulations.