Bearing performance degradation assessment is of great significance for proactive maintenance and near-zero downtime. For this purpose, a novel assessment method is proposed based on lifting wavelet packet symbolic entropy (LWPSE) and support vector data description (SVDD). LWPSE is presented for feature extraction by jointing use of lifting wavelet packet transform and symbolic entropy. Firstly, the LWPSEs of bearing signals from normal bearing condition are extracted to train an SVDD model by fitting a tight hypersphere around normal samples. Then, the relative distance from the LWPSEs of testing signals to the hypersphere boundary is calculated as a quantitative index for bearing performance degradation assessment. The feasibility and efficiency of the proposed method were validated by the life-cycle data obtained from NASA’s prognostics data repository and the comparison with Hidden Markov Model (HMM). Finally, the assessment results were verified by the envelope spectrum analysis method based on empirical mode decomposition and Hilbert envelope demodulation.

In this paper, a quaternion-based adaptive dynamic surface control method is proposed for attitude tracking control for small-scale unmanned helicopters with external disturbance and uncertain dynamics. The quaternion formalism is introduced and a quaternion-based multi-input-multi-output nonlinear model is derived from the attitude dynamics of a small-scale helicopter. The low-complexity controllers are designed by the dynamic surface control method as it eliminates the problem of the explosion of items. The singularity problem is avoided by substituting Euler kinematic equations in the nonlinear model with quaternion expressions and integrating the quaternion expressions into the design process of the dynamic surface control. For improving the robustness of the control system, the radial basis function networks are applied to approximate the uncertain dynamics. The external disturbance is also compensated in the controllers' design. This paper proves that the proposed method can guarantee the uniformly ultimate boundness of this attitude system. Simulation results are presented finally and show the effectiveness of this control approach.INDEX TERMS Unmanned helicopter, quaternion-based nonlinear attitude model, dynamic surface control, neural network.

Genetic Algorithms are traditionally used to solve combinatorial optimization problems. The implementation of Genetic Algorithms involves of using genetic operators (crossover, mutation, selection, etc.). Meanwhile, parameters (such as population size, probabilities of crossover and mutation) of Genetic Algorithm need to be chosen or tuned. In this paper, we propose a hybrid Fuzzy-Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem. Based on traditional Genetic Algorithms, a fuzzy logic controller is added to tune parameters dynamically which potentially can improve the overall performance. In detail, the probabilities of crossover and mutation is tuned by a fuzzy logic controller based on fuzzy rules. Compared to the Standard Genetic Algorithm (SGA), the results of experiments clearly show that the FLGA method performs significantly better.

This paper presents a hybrid attitude control scheme of L1 adaptive and dynamic inversion for unmanned aerial vehicle with actuator faults, where both gain fault and bias fault are considered. Firstly, dynamic inversion is employed to track attitude angles in the outer loop and ensures the rapid response. Secondly, an L1 adaptive controller for the inner loop is established to compensate for system uncertainty and uncertainty caused by actuator failures. Thirdly, the analysis of steady-state and transient performance is given by Lyapunov theory. Finally, simulation results prove the effectiveness of the proposed approach.

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