Though the wireless in-flight charging is an ideal way for the energy supply of drones, it also faces the practical challenges, namely dual-disturbance of continuous fluctuation of mutual inductance and battery load variation, changed expected charging power, and the lightweight and no communication design demands for pickup, which have been nearly unexplored in previous article on wireless power transfer (WPT) system. To address the issue, this article proposes a novel communication-free output power control algorithm based on the load identification scheme with automatic frequency adjust. Only the primary current needs to be measured in the proposed algorithm, which can reduce the system complexity with the enhanced real-time performance for WPT system. Simulated and experimental results validate the feasibility of the proposed control algorithm with the identification accuracy of more than 92%, control accuracy of 95%, and average response time within 100 ms. Furthermore, the follow-up of desired output power and the implementation of lightweight pickup demonstrate the flexibility of the power control algorithm, which ensures the rapid and safe energy supply for drones in the drone wireless in-flight charging system.

Distributed architecture is expected to be an effective solution for large-scale edge computing tasks in terminal devices. However, it remains a great challenge to resolve the conflict between parallel efficiency and constrained physical resources in a specific embedded structure. This article proposes a universal scalable off-chip parallel computing architecture to maximize the computing efficiency for distributed embedded computing clusters. This architecture is based on an improved Message Passing Interface (Improved-MPI). To address the limited communication speed in embedded environments, a multilevel communication mechanism is employed to alleviate the communication pressure on nodes. By flexibly allocating computing tasks, efficient utilization of every embedded cluster node is ensured, while also solving the problem of single point of failure. In addition, to overcome the challenge of limited RAM in embedded devices, the architecture utilizes the interleaved memory initialization mechanism to run larger computing tasks. Based on this architecture, a specific embedded cluster platform is constructed using the RK3399 board. Various large-scale tasks are deployed on this platform to validate the performance of the architecture. First, a large-scale randomly connected neural network is executed, which serves to verify the architecture's outstanding computational performance and communication capability. Secondly, a functional model of Small-World Spiking Neural Network is constructed, achieving real-time and efficient digital speech recognition. Finally, the implementation of Large Language Models demonstrates that the embedded clusters can achieve performance comparable to modern computers.

Distributed edge computing platforms are of great significance for the implementation of brain-like computing research. Due to the limited power consumption and real-time requirements, hardware acceleration of computing units is a challenging task. Taking advantage of both scalable hardware framework and lower cost, this article designs a multicore distributed computing platform for spiking neural networks. Particularly, a shared memory partition structure is utilized to participate in hardware acceleration. Through the spike-queue-based synaptic mapping mechanism, each parallel computing unit deals with efficient point-to-point connections. In addition, this platform provides a basic community unit (BCU) that encapsulates a standard neuron model library and rich peripheral interfaces. With the support of GUI, users can quickly build large-scale systems. The experimental results show that a single BCU can accommodate more than 10 000 neurons updated in real-time at a power consumption of 273.6 mW. The extended BCU group is able to perform network dynamic simulations in the basal ganglia-thalamus plausible biological network composed of Hodgkin–Huxley neurons as well as MNIST dataset classification in the leaky-integrate-fire network. The outstanding flexibility and real-time performance of the proposed hardware architecture provide great potential for embedded applications of neural computation.

Variations in brain activity patterns reveal impairments of motor and cognitive functions in the human brain. Electroencephalogram (EEG) microstates embody brain activity patterns at a microscopic time scale. However, current microstate analysis method can only recognize less than 90% of EEG signals per subject, which severely limits the characterization of dynamic brain activity. As an application to early Parkinson's disease (PD), we propose an enhanced EEG microstate recognition framework based on deep neural networks, which yields recognition rates from 90% to 99%, as accompanied by a strong anti-artifact property. Additionally, gradient-weighted class activation mapping, as a visualization technique, is employed to locate the activated functional brain regions of each microstate class. We find that each microstate class corresponds to a particular activated brain region. Finally, based on the improved identification of microstate sequences, we explore the EEG microstate characteristics and their clinical associations. We show that the decreased occurrences of a particular microstate class reflect the degree of cognitive decline in early PD, and reduced transitions between certain microstates suggest injury in motor-related brain regions. The novel EEG microstate recognition framework paves the way to revealing more effective biomarkers for early PD.

In order to mitigate the periodic disturbances in voltage-source converter (VSC)-based applications, different techniques have been comprehensively investigated to mitigate these periodic disturbances, either using additional disturbance-mitigation controller or by means of modifying the modulation stage. As well as providing an in-depth analysis of various periodic disturbances in VSC-based applications, this article presents an overview of the controller-based periodic disturbance mitigation techniques. And these techniques, different in concept, can be categorized into two types, i.e., the internal-model-based methods and the feedforward-based methods. The controller prototypes and their variants are comprehensively introduced with motivations and distinct features. The characteristics of different strategies and the corresponding implementation methods are compared and summarized. And the practical issues such as computational burden and frequency adaptability are simultaneously included as well to present a comprehensive illustration. In addition, the characteristics of different strategies are compared and summarized with the aim of providing a clear illustration.

The special section presents the recent advances on sliding mode control and its applications in modern industrial systems. With the increased utilization of modern equipment in wide industry applications such as renewable energy systems, distributed generation, smart grid, transportation, robot manipulators, power systems and automotive, emphasis on better performance in terms of the robustness, optimization, reliability and implementation simplicity has become an important requirement. Sliding mode control is recognized as one of the popular and powerful tools in achieving these requirements in industrial systems. The robustness feature eliminates the burden of the necessity of system parameters required for accurate modelling in most applications. In spite of these attractive advantages, the sliding mode control method suffers from chattering existing due to unmodelled dynamics and switching time delays. The effort of the researchers and industry has led to a rapid development of different sliding mode control methods in terms of sliding surface design, sliding surface coefficient selection, sliding-mode observers, chattering reduction methods, and modulation techniques. Especially, the observer and disturbance estimation techniques are widely studied and substantial new observation and estimation techniques have been proposed in the literature. Hence, the objective of this special section is to share the new ideas of researchers and industry on sliding mode control and its applications in modern industrial systems. The summaries of thirteen papers accepted for publication are given below.

The twelve papers in this special section present relevant research works concerning identification and observation informatics for energy generation, conversion, and applications. It has always been known that high-performance operation of machines and power converters requires fast dynamic response and accurate regulation of controlled variables. As well as proper design of specific control schemes, accurate acquisition, and knowledge of system information based on parameter identification and state observation is an effective measure to obtain enhanced performances regarding disturbance rejection, sensorless operation, parameter adaption, etc. With the increased emphasis on higher efficiency, effectiveness, reliability, and flexibility in energy generation, conversion, and applications, the level of interest and pace of developments in area of identification and observation have further accelerated and witnessed great breakthrough.

Model predictive control (MPC) strategies have attracted wide attention due to its inherently high dynamic characteristics and flexible control capability. When the MPC is adopted to regulate the currents of permanent magnet synchronous machines, the cost function is typically constructed based on tracking errors of current components. Reduced switching frequency can be obtained by means of minimizing a revised cost function which includes a penalty item related to the predicted switching frequency. This will inevitably affect current tracking performances and, meanwhile, increase the computational burden due to the required long-horizon minimization. In order to solve this problem, this paper proposes a predictive current controller which inherently takes the converter zero-order-hold characteristics and system delay into account. Besides, with the aim of simultaneously realizing system behavior prediction and disturbance estimation, a structure of parallel extended-state observers is proposed. Theoretical analyses and experimental validations are both conducted to confirm its effectiveness. It is demonstrated that, under the situation of reduced switching frequency, improved control performances can be expected when the proposed controller is adopted, including highly dynamic responses inherently presented by typical predictive controllers and enhanced steady-state performances with the help of parallel observation structure.

By comprehensively analyzing the effective working voltage range of electric springs (ESs), this paper proposes a new control strategy to improve the absorption capacity of ES-based flexible loads for mains voltage fluctuations; thus, enhancing the stability of the microgrid fed by intermittent renewables. Analogous to a mechanical spring, the effective working voltage range of ESs is also restricted by the electric elasticity limit (EEL). In specific circumstances, the ES-based flexible load is incapable of dealing with excessive fluctuations of mains voltage caused by intermittent renewable energy sources. Accordingly, this paper investigates the impact of key parameters on the EEL of ESs, such as the distribution line, the voltage reference, and the loads. Besides, by adjusting the proportion between the critical and the noncritical loads, this paper proposes and implements an EEL controller to regulate the absorption capacity of ES-based flexible loads for mains voltage fluctuations; thus, effectively ensuring the voltage stability of critical loads. Finally, the simulated and experimental results are both given to verify the theoretical analysis for ES-based flexible loads and the feasibility of the proposed EEL controller.

This paper presents a quantitative comparison of three topologies of double-sided long-stator type permanent magnet linear machines (PMLMs) as possible candidates for the ropeless elevator propulsion system. First, the parameters of each PMLM topology are designed using the same criteria. Then the finite element method (FEM) is employed to evaluate the performance of each topology. Specifically, the translator mass, propulsion forces, detent forces, and no-load EMFs are analyzed and compared. The quantitative comparison results show that the Halbach array PMLM configuration is preferable for the ropeless elevator application because of its small detent force as well as low total mass.

This paper presents a comparison of two chaoized PWM algorithms for motor drives in the electric vehicle (EV), which are the chaotic sinusoidal pulse width modulation (SPWM) and the chaotic space-vector pulse width modulation (SVPWM). The SPWM scheme can be chaoized by three modulation methods, including the chaotically amplitude-modulated frequency modulation (CAFM), the chaotically position-modulated position modulation (CPPM), and the hybrid chaotic frequency modulation (HCFM), while the chaotic SVPWM can be fulfilled by the chaotically frequency-modulated frequency modulation (CFFM) and the CAFM methods. The performance indexes used in the comparative analysis are the electromagnetic interference (EMI) and the mechanical resonance (MR). The chaotic PWM algorithm is designed and implemented to increase the electromagnetic compatibility (EMC) and the mechanical performance for EV motor drives, and the aforementioned performance indexes are compared for the practical applicability.