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The civil aviation sector plays an increasingly significant role in transportation sustainability in the environmental, economic, and social dimensions. Driven by the concerns of sustainability in the aviation sector, more electrified aircraft propulsion technologies have emerged and form a very promising approach to future sustainable and decarbonized aviation. This review paper aims to provide a comprehensive and broad-scope survey of the recent progress and development trends in sustainable aviation electrification. Firstly, the architectures of electrified aircraft propulsion are presented with a detailed analysis of the benefits, challenges, and studies/applications to date. Then, the challenges and technical barriers of electrified aircraft propulsion control system design are discussed, followed by a summary of the control methods frequently used in aircraft propulsion systems. Next, the mainstream energy management strategies are investigated and further utilized to minimize the block fuel burn, emissions, and economic cost. Finally, an overview of the development trends of aviation electrification is provided.

Understanding the neurophysiological signals underlying voluntary motor control and decoding them for controlling limb prostheses is one of the major challenges in applied neuroscience and rehabilitation engineering. While pattern recognition of continuous myoelectric (EMG) signals is arguably the most investigated approach for hand prosthesis control, its underlying assumption is poorly supported, i.e., that repeated muscular contractions produce consistent patterns of steady-state EMGs. In fact, it still remains to be shown that pattern recognition-based controllers allow natural control over multiple grasps in hand prosthesis outside well-controlled laboratory settings. Here, we propose an approach that relies on decoding the intended grasp from forearm EMG recordings associated with the onset of muscle contraction as opposed to the steady-state signals. Eight unimpaired individuals and two hand amputees performed four grasping movements with a variety of arm postures while EMG recordings subsequently processed to mimic signals picked up by conventional myoelectric sensors were obtained from their forearms and residual limbs, respectively. Off-line data analyses demonstrated the feasibility of the approach also with respect to the limb position effect. The sampling frequency and length of the classified EMG window that off-line resulted in optimal performance were applied to a controller of a research prosthesis worn by one hand amputee and proved functional in real-time when operated under realistic working conditions.

This paper proposes a coordinated control of doubly fed induction generator (DFIG)-based wind farms' (WFs) post-fault active power and synchronous generators' (SGs) tripping with the aim of improving transient stability of both the first swing and multi swings. To achieve it, the impact mechanism of the WFs' post-fault active power and SGs' tripping on the stability margin of each swing is first presented by using extended equal area criterion (EEAC). Based on this, a principle of the coordinated control is put forward. The WFs' control period is designed as six stages, and the value of post-fault active power in each stage is suggested to improve the stability of the first five swings and maintain the post-fault steady state. To decrease the tripping amount of the SGs, the SGs are tripped only when WFs' control effect is not sufficient to avoid the instability. Then, by utilizing an “online pre-decision and real-time matching”scheme, an engineering application method for the control principle is proposed, where the control procedures and detailed parameters calculation are demonstrated. The numerical simulations show that the coordinated control has a better damping effect and a less control cost (less amount of tripped SGs) than the traditional DFIG control or traditional SG tripping.

In this paper, a variable-structure multi-resonant soft-switching DC&ndash, DC converter and its transient smooth control method are proposed. Through the introduction of auxiliary switches, the converter can flexibly adjust its structure among three operating modes. Two switching processes can be obtained. Thus, a wide voltage gain range is achieved within a narrow frequency range. Moreover, to eliminate the large voltage fluctuation during modes switching, a drive signal gradual adjustment control method is proposed. Consequently, smooth switching between different modes can be realized and the voltage fluctuation is suppressed effectively. Finally, a 200 W experimental prototype is established to verify the theoretical analyses. Soft-switching performances for power switches and diodes are both guaranteed. The highest efficiency is 98.2%. With the proposed transient control method, a basically constant 400 V output voltage is ensured within a wide input voltage range (80 V&ndash, 600 V). In particular, the transient voltage fluctuations during two switching processes decrease from 38.4 V to 10.8 V and from 37.2 V to 8.4 V, respectively.

Time and fuel optimal control for a diesel-electric powertrain in transient operation is studied using a four state, three controls non-linear mean value engine model. In the studied transients the engine starts at idle and stops when the generated energy fulfills the driving mission requirement. During the driving mission both the engine speed and output power are allowed to vary, but with a constraint on power. Two strategies are then developed and evaluated. One where the driving mission is optimized with the generator power considered a free variable, and a second strategy where the accelerating phase of the transient is first optimized and then the optimal controls for a fixed generator power are used. The time optimal control is shown to be almost as fuel efficient as the fuel optimal control even though the gain in time is large. The time optimal control also has the advantage of using constant power output, making it simple and easily implementable, whilst the fuel optimal control is more complex and changes with the length of the driving mission.