Pedestrians represent agile and low-observable targets, especially under adverse weather conditions, whose trajectory tracking plays a crucial role in determining pedestrian behavior in autonomous urban driving. Thus, this article presents a millimeter-wave radar-based pedestrian trajectory-tracking (MRPT) system that enables all-weather trajectory perception with high precision. More specifically, to improve the tracking performance in the presence of strong background clutters, a track-before-detection-based algorithm is proposed to address pedestrian detection and tracking jointly, instead of regarding them as two separate phases in conventional methods. After that, a continuous detection method based on the integration of target existence probability and the Markov transition matrix is proposed to achieve superior pedestrian detection performance by directly using unthresholded radar data. In addition, based on the binary-phase-multiplex (BPM) strategy, the proposed system is validated using a low-cost automotive frequency-modulated-continuous-waveform (FMCW) multiple-input–multiple-output (MIMO) radar sensor. Consequently, extensive simulation and experimental results demonstrate that MRPT exhibits better pedestrian detection and tracking performance under low signal-noise-ratio (SNR) conditions compared with traditional methods.

Aiming at the difficulty of extracting information for incipient fault symptoms from rolling bearings with strong background noise, an improved incipient fault detection method based on modified recursive least squares adaptive equalization (RLS) and a local mean decomposition algorithm (LMD) is proposed. First, an efficient RLS de-noising model is established by introducing a momentum factor together with a forgotten factor to de-noise the incipient fault signal of the bearings. Then, the local mean decomposition algorithm (LMD) is used to decompose the pre-processed signal to obtain an effective PF component, and complete the envelope demodulation to extract information from the incipient fault. Based on the above algorithm, an improved RLS and LMD identifying algorithm for incipient faults can thus be achieved. Finally, some actual fault signals of a large unit rolling bearing are used to simulate and verify the accuracy and efficiency of the proposed algorithm. The experimental comparison indicated that our algorithm can not only improve the de-noising effect but also correctly extract the features of the incipient fault and identify them with good engineering operability and expansibility.

The early trajectory prediction of micro unmanned aerial vehicles (micro-UAVs) with random behavior intentions facilitates the elimination of potential safety hazards. However, due to the property of a small radar cross-section (RCS), the backscattered radar signals from micro-UAVs may be submerged under strong background clutters, leading to distorted tracking and false prediction. To this end, this paper presents a spatial-temporal integrated framework (STIF) for end-to-end micro-UAV trajectory tracking and prediction based on a four-dimensional (4D) multiple-input-multiple-output (MIMO) radar. Especially, to obtain accurate trajectories in low signal-to-noise ratio (SNR) conditions, the target detection and tracking are considered to be interdependent and addressed jointly in this work, rather than treating them as two separate processes in conventional methods. The advantage is that with the assistance of tracking, all consecutive spatial information encoded in raw radar streams can be incorporated to enhance the continuous detection performance, avoiding information loss using only one single scan. Subsequently, to accommodate high maneuvering scenarios, an intention-aware end-to-end transformer-based prediction framework is presented to simultaneously discover both spatial and temporal dependencies hiding in long-term estimated trajectories. Consequently, a 4D frequency modulated continuous wave (FMCW) radar is utilized to evaluate the proposed system. Numerous simulation and experimental results indicate that STIF outperforms competing state-of-the-art methods and achieve superior prediction performance with the accuracy of 0.3851m in low SNR conditions.

This article presents a reduced-order observer-based simultaneous diagnosis strategy for grid-tied neutral point clamped (NPC) inverters subjected to open-switch and current sensor faults. First, the augmented descriptor system is constructed for the NPC inverter to transfer the current sensor fault into a generalized state vector. Then, the matrix transformations are applied to decouple the open-switch fault from the inverter system state and the current sensor fault. Subsequently, a reduced-order observer is developed for the transformed augmented descriptor system to achieve a simultaneous precise estimation of the phase current and the current sensor fault. Finally, using the estimation results, a diagnosis algorithm with an adaptive threshold is proposed, which can not only distinguish between current sensor faults and open-switch faults but also locate the faulty power switch and estimate different types of current sensor faults. Experimental results and comparisons are provided to verify the robustness and effectiveness of the proposed fault diagnosis algorithm.

The communication topologies between vehicles in a platoon substantially impact the platoon’s stability. This study provides a distributed linear feedback control law that considers time-varying delay with guaranteed internal and string stability of the platoon system under various communication topologies. Firstly, the vehicle dynamics linearized model was derived using the precise feedback linearization technology. Different types of communication topologies, such as vehicle-to-vehicle communication and sensor-based communication, were described using directed graphs. Secondly, the linear feedback control law was designed to establish the stable zone of the linear controller gain under the effect of different communication topologies using directed graphs and the Routh-Hurwitz stability theorem. The Lyapunov-Razumikhin theorem determines the upper bound of the time-varying delay of various communication topologies. Additionally, the string stability of leader-predecessor following topology was studied, and the results were combined with the internal stability to determine the upper bound of time-varying delay. Finally, the results were verified by conducting two numerical simulations.

This paper develops a novel sliding mode control technology and a comprehensive evaluation methodology for speed control of permanent magnet synchronous motor (PMSM). In most of the existing literature, only control precision of the speed corrector is investigated. However, the control strategy should be stable to the noise and disturbance caused by the environment and parameter uncertainties. In order to possessing faster dynamic response, stronger anti-interference ability and high stability of input, a highly stable sliding surface is proposed for siding mode control. Unlike most of reaching laws that requires the variation of designed sliding surfaces, this method takes state vectors in consideration. In addition, a comprehensive evaluation algorithm is presented to obtain reasonable assessment on speed control for synchronous PMSMs in different operating phases (starting, transient, steady-state rating). Finally, the simulation results verify the effectiveness and validity of the proposed sliding mode speed controller.