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Compared with battery Equivalent Circuit Models (ECM), Single Particle Model (SPM) has more appropriate physics representation and higher accuracy theoretically. However, SPM-based parameter estimation performance is restricted by the SPM model complexities. In this paper, a simplified SPM and its corresponding adaptive State of Charge (SOC)/State of Health (SOH) estimation scheme are studied. First, the SPM is simplified from Partial Differential Equation (PDE) to Ordinary Differential Equation (ODE) for a trade-off between model complexity and consistency. Second, an adaptive model observer is proposed to estimate battery parameters, which include a SOC state implying normalized lithium-ion concentration, and a SOH parameter implying the maximum lithium-ion surface concentration, both in the solid surface phase. Because the ODE-based adaptive parameter estimation is capable of avoiding complex identification procedures, this new approach can be implemented in practical applications with high accuracy. Through massive simulation scenarios, the proposed SPM model is validated based on comparison between ODE SPM and PDE SPM, as well as Benchmark Validation. Finally, both simulation and experiment demonstrate the effectiveness of the simplified SPM and the superiority of the proposed SOC/SOH estimation scheme.

A systematic fault tolerant control (FTC) scheme based on fault estimation for a quadrotor actuator, which integrates normal control, active and passive FTC and fault parking is proposed in this paper. Firstly, an adaptive Thau observer (ATO) is presented to estimate the quadrotor rotor fault magnitudes, and then faults with different magnitudes and time-varying natures are rated into corresponding fault severity levels based on the pre-defined fault-tolerant boundaries. Secondly, a systematic FTC strategy which can coordinate various FTC methods is designed to compensate for failures depending on the fault types and severity levels. Unlike former stand-alone passive FTC or active FTC, our proposed FTC scheme can compensate for faults in a way of condition-based maintenance (CBM), and especially consider the fatal failures that traditional FTC techniques cannot accommodate to avoid the crashing of UAVs. Finally, various simulations are carried out to show the performance and effectiveness of the proposed method.

DC-DC power converters such as Buck converters are susceptible to degradation and failure due to operating under conditions of electrical stress and variable power sources in power conversion applications such as electric vehicles and renewable energy. Some key components such as electrolytic capacitors degrade over time due to evaporation of the electrolyte. In this paper, a model-observer based scheme is proposed to monitor states of Buck converters and to estimate their component parameters such as capacitance and inductance. Firstly, a diagnosis observer is proposed, and the generated residual vectors are applied for fault detection and isolation. Secondly, component condition parameters such as capacitance and inductance are reconstructed using another novel observer with adaptive feedback law. Additionally, the observer structures and their theoretical availability are analyzed and proven. In contrast to existing reliability approaches applied in Buck converters, the proposed scheme perform online-estimation for key parameters. Finally, Buck converters in conventional DC-DC step-down and Photo-Voltaic applications are investigated to test and validate the effectiveness of the proposed scheme in both simulation and laboratory experiment. Results demonstrate the feasibility, performance and superiority of the proposed component parameters estimation scheme.

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A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Being reliant on Air Conditioning (AC) throughout the majority of the year, desert countries with extremely hot weather conditions such as Qatar are facing challenges in lowering weariness cost due to AC On-Off switching while maintaining an adequate level of comfort under a wide-range of ambient temperature variations. To address these challenges, this paper investigates an optimal On-Off control strategy to improve the AC utilization process. To overcome complexities of online optimization, a Elman Neural Networks (NN)-based estimator is proposed to estimate real values of the outdoor temperature, and make off-line optimization available. By looking up the optimum values solved from an off-line optimization scheme, the proposed control solutions can adaptively regulate the indoor temperature regardless of outdoor temperature variations. In addition, a cost function of multiple objectives, which consider both Coefficient of Performance (COP), and AC compressor weariness due to On-Off switching, is designed for the optimization target of minimum cost. Unlike conventional On-Off control methodologies, the proposed On-Off control technique can respond adaptively to match large-range (up to 20 ∘ C) ambient temperature variations while overcoming the drawbacks of long-time online optimization due to heavy computational load. Finally, the Elman NN based outdoor temperature estimator is validated with an acceptable accuracy and various validations for AC control optimization under Qatar’s real outdoor temperature conditions, which include three hot seasons, are conducted and analyzed. The results demonstrate the effectiveness and robustness of the proposed optimal On-Off control solution.

As a new alternative to tilting rotors or turbojet vector mechanical oriented nozzles, ACHEON (Aerial Coanda\ud High Efficiency Orienting-jet Nozzle) has enormous advantages because it is free of moving elements and highly\ud effective for Vertical/Short-Take-Off and Landing (V/STOL) aircraft. In this paper, an integrated flight/ thrust vectoring\ud control scheme for a jet powered Unmanned Aerial Vehicle (UAV) with an ACHEON nozzle is proposed to assess its\ud suitability in jet aircraft flight applications. Firstly, a simplified Thrust-Vectoring (TV) population model is built based\ud on CFD simulation data and parameter identification. Secondly, this TV propulsion model is embedded as a jet actuator\ud for a benchmark fixed-wing ‘Aerosonde’ UAV, and then a four “cascaded-loop” controller, based on nonlinear dynamic\ud inversion (NDI), is designed to individually control the angular rates (in the body frame), attitude angles (in the wind\ud frame), track angles (in the navigation frame), and position (in the earth-centered frame) . Unlike previous research on\ud fixed-wing UAV flight controls or TV controls, our proposed four-cascaded NDI control law can not only coordinate\ud surface control and TV control as well as an optimization controller, but can also implement an absolute self-position\ud control for the autopilot flight control. Finally, flight simulations in a high-fidelity aerodynamic environment are\ud performed to demonstrate the effectiveness and superiority of our proposed control scheme