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To estimate the continuous-discrete nonlinear dynamic systems with non-Gaussian non-zero mean noises, a square-root maximum correntropy cubature Kalman filter with adaptive kernel width and its corresponding smoother are proposed in this paper. Based on the statistical linear regression (SLR) technique, the maximum correntropy criterion (MCC) instead of minimum mean square error (MMSE) is applied to our previously derived continuous-discrete Gaussian estimation method. Compared with MMSE, MCC can not only capture the second-order statistical information of non-Gaussian error, but also utilize its higher-order information. In addition, in order to ensure that MCC can work with an appropriate kernel width, an adaptive kernel width adjustment strategy is given by using the sliding window methodology. Further, for a reliable implementation of the proposed continuous-discrete MCC-based algorithms, they are structurally modified into the square root form. The newly proposed approaches are tested and compared with conventional estimation methods in three commonly used numerical applications. Experimental results show that the proposed algorithms are not only accurate and robust, but also have low computational complexities.

The dual-motor driving servo system is continuously developed to satisfy strict safety and reliability requirements. However, several factors may degrade the system's performance, such as transmission backlash, parameter drift, and motor dynamic characteristic differences. To overcome these factors, this study proposes a finite-time tracking and synchronization control method for dual-motor servo systems that suffer from backlash and time-varying uncertainties. Our solution utilizes an adaptive dynamic surface and cross-coupling control scheme to deal with tracking and synchronization control issues and compensate for the unknown time-varying uncertainties. Through synchronizing the speed and acceleration states, the proposed controller guarantees high control performance and eliminates the force fighting caused by the motor's dynamic characteristic differences. In addition, finite-time control ensures the tracking error converges to an arbitrarily small neighborhood of zero in finite time. Moreover, the singularity problem in the derivative of the virtual control signal is avoided by introducing a new compensation term. Several simulations prove the proposed controller's stability and effectiveness.

A major reason for compromised audio quality with a coil-type loudspeaker is its complex impedance profile, which disturbs the intended algebraic linearity or linear phase behavior between the amplifier input and speaker driving force. Though switch-mode amplifiers are increasingly preferred in such audio systems owing to their superior energy efficiency, their inherent non-linearity and finite internal impedance further degrade audio reproduction. This paper investigates the effect of stated impedance on the loudspeaker driving current, hence on the loudspeaker driving force, and proposes a Hybrid controller-amplifier to enhance the proportionality between the audio reference and driving force without compromising on loudspeaker damping. The Hybrid amplifier combines a sliding mode controlled amplifier (SMCA) and a linear controller, designed after exhaustive modeling of the input-output behavior of the SMCA and best-fit approximation of the loudspeaker impedance. An 8 W, 500 Hz to 20 kHz prototype of the proposed amplifier is fabricated on the basis of the presented design. Time and frequency-domain results obtained with the experimental hardware are presented to validate the performance improvement.

This paper proposes a control algorithm for controlling the position and attitude of a coaxial rotor drone without knowing the model dynamics. To overcome the major drawback of model-dependent approaches, an optimal model-free fuzzy controller (OMFFC) based on the estimation of the unknown dynamic function of the system is proposed. A time-delay estimation (TDE) technique is effectively exploited to approximate the unknown dynamic function of the system. The estimation error is then offset using a robust adaptive fuzzy logic compensator. Based on Lyapunov stability arguments, the global asymptotic stability of the coaxial rotor drone system is proven. Moreover, a flower pollination-based algorithm is also proposed to generate the optimal parameters to address the trade-off between optimal tracking performance and the design conditions related to the closed-loop stability requirements. The numerical simulations illustrate how the proposed methodology leads to the best performance, as well as less computational complexity compared to the standard proportional-integral-derivative and time-delay estimation-based controllers in the presence of external disturbances.

In this paper, the development and implementation of a novel approach for fault detection of an aircraft auxiliary power unit (APU) has been demonstrated. The developed approach aims to target the proactive identification of faults, in order to streamline the required maintenance and maximize the aircraft’s operational availability. The existing techniques rely heavily on the installation of multiple types of intrusive sensors throughout the APU and therefore present a limited potential for deployment on an actual aircraft due to space constraints, accessibility issues as well as associated development and certification requirements. To overcome these challenges, an innovative approach based on non-intrusive sensors i.e., microphones in conjunction with appropriate feature extraction, classification, and regression techniques, has been successfully demonstrated for online fault detection of an APU. The overall approach has been implemented and validated based on the experimental test data acquired from Cranfield University’s Boeing 737-400 aircraft, including the quantification of sensor location sensitivities on the efficacy of the acquired models. The findings of the overall analysis suggest that the acoustic-based models can accurately enable near real-time detection of faulty conditions i.e., Inlet Guide Vane malfunction, reduced mass flows through the Load Compressor and Bleed Valve malfunction, using only two microphones installed in the periphery of the APU. This study constitutes an enabling technology for robust, cost-effective, and efficient in-situ monitoring of an aircraft APU and potentially other associated thermal systems i.e., environmental control system, fuel system, and engines.