The micro-turbojet engine (MTE) is especially suitable for unmanned aerial vehicles (UAVs). Because the rotor speed is proportional to the thrust force, the accurate speed tracking control is indispensable for MTE. Thanks to its simplicity, the proportional–integral–derivative (PID) controller is commonly used for rotor speed regulation. However, the PID controller cannot guarantee superior performance over the entire operation range due to the time-variance and strong nonlinearity of MTE. The gain scheduling approach using a family of linear controllers is recognized as an efficient alternative, but such a solution heavily relies on the model sets and pre-knowledge. To tackle such challenges, a single neural adaptive PID (SNA-PID) controller is proposed herein for rotor speed control. The new controller featuring with a single-neuron network is able to adaptively tune the gains (weights) online. The simple structure of the controller reduces the computational load and facilitates the algorithm implementation on low-cost hardware. Finally, the proposed controller is validated by numerical simulations and experiments on the MTE in laboratory conditions, and the results show that the proposed controller achieves remarkable effectiveness for speed tracking control. In comparison with the PID controller, the proposed controller yields 54% and 66% reductions on static tracking error under two typical cases.

A semi-active vibration control technique using nonlinear synchronized switch damping on negative capacitance (SSDNC) is proposed in this article. SSDNC, as one of the effective nonlinear vibration damping techniques, has been intensively investigated and discussed in recent years. In comparison with the classical SSDNC technique, the proposed self-sensing approach realizes the SSDNC technique by using analog circuits, which require neither external sensors nor controllers. In particular, the stability of the switching shunt circuit using negative capacitance (NC) is addressed. The theoretical and experimental analysis reveals that the saturation of the operational amplifier in synthetic NC can prevent the proposed SSDNC circuit from trapping into instability and yielding the best attenuation performance. The final experiments show that the proposed approach exhibits excellent single-mode damping performance and is sufficiently robust.

Long-time-series climate prediction is of great significance for mitigating disasters; promoting ecological civilization; identifying climate change patterns and preventing floods, drought and typhoons. However, the general public often struggles with the complexity and extensive temporal range of meteorological data when attempting to accurately forecast climate extremes. Sequence disorder, weak robustness, low characteristics and weak interpretability are four prevalent shortcomings in predicting long-time-series data. In order to resolve these deficiencies, our study gives a novel hybrid spatiotemporal model which offers comprehensive data preprocessing techniques, focusing on data decomposition, feature extraction and dimensionality upgrading. This model provides a feasible solution to the puzzling problem of long-term climate prediction. Firstly, we put forward a Period Division Region Segmentation Property Extraction (PD-RS-PE) approach, which divides the data into a stationary series (SS) for an Extreme Learning Machine (ELM) prediction and an oscillatory series (OS) for a Long Short-term Memory (LSTM) prediction to accommodate the changing trend of data sequences. Secondly, a new type of input-output mapping mode in a three-dimensional matrix was constructed to enhance the robustness of the prediction. Thirdly, we implemented a multi-layer technique to extract features of high-speed input data based on a Deep Belief Network (DBN) and Particle Swarm Optimization (PSO) for parameter searching of a neural network, thereby enhancing the overall system’s learning ability. Consequently, by integrating all the above innovative technologies, a novel hybrid SS-OS-PSO-DBN-ELM-LSTME (SOPDEL) model with comprehensive data preprocessing was established to improve the quality of long-time-series forecasting. Five models featuring partial enhancements are discussed in this paper and three state-of-the-art classical models were utilized for comparative experiments. The results demonstrated that the majority of evaluation indices exhibit a significant optimization in the proposed model. Additionally, a relevant evaluation system showed that the quality of “Excellent Prediction” and “Good Prediction” exceeds 90%, and no data with “Bad Prediction” appear, so the accuracy of the prediction process is obviously insured.

Conventional direct lift control (DLC) design in carrier landing is not accepted by pilots since it is not fully decoupled and has heavy control burden. Nevertheless, when using dynamic inversion, developed in recent years as the baseline control framework, the control architecture can dynamically decouple control state channels and simplify control logic. In this study, the incremental dynamic inversion control (INDI) method is used to realize the DLC of the carrier-based aircraft precision landing control law and compare the dynamic inversion control (NDI). Meanwhile, the robustness of the INDI control framework is analyzed. A landing simulation system is completed to verify that using the NDI/INDI+DLC framework can fully exploit and reflect the advantage of DLC in carrier landing. The results show that the proposed NDI/INDI+DLC, i.e., the precision landing control architecture, can use the rapidity of DLC to correct minor errors of landing and improve landing accuracy quickly. At the same time, decoupling of DLC based on INDI can also reduce the complexity of the landing operation and improve the landing success rate.