In this study, a disturbance observer‐based integral fuzzy sliding‐mode control methodology for a wind turbine system with a non‐linear permanent magnet synchronous generator (PMSG) is proposed using the Takagi–Sugeno fuzzy model. To reduce the hardware cost of the fuzzy controller membership function, an imperfect premise matching condition is considered. Considering the matched disturbance, and an external disturbance acting on the system, a novel integral fuzzy sliding surface is proposed based on a fuzzy disturbance observer (FDOB). Therefore, various forms of external disturbances can be estimated using the FDOB. In addition, a sufficient condition for guaranteeing the asymptotic stability of the sliding motion with performance is proposed using a Lyapunov function and derived using a linear matrix inequality format. Furthermore, a reachability problem of the system states over the proposed sliding surface is derived through an appropriate Lyapunov function. Finally, the simulations for a wind turbine system using a PMSG are implemented to verify the effectiveness of the proposed method as compared with previous studies.

Accurate vehicle localization is important for autonomous driving and advanced driver assistance systems. Existing precise localization systems based on the global navigation satellite system cannot always provide lane-level accuracy even in open-sky environments. Map-based localization using high-definition (HD) maps is an interesting method for achieving greater accuracy. We propose a map-based localization method using a single camera. Our method relies on road link information in the HD map to achieve lane-level accuracy. Initially, we process the image—acquired using the camera of a mobile device—via inverse perspective mapping, which shows the entire road at a glance in the driving image. Subsequently, we use the Hough transform to detect the vehicle lines and acquire driving link information regarding the lane on which the vehicle is moving. The vehicle position is estimated by matching the global positioning system (GPS) and reference HD map. We employ iterative closest point-based map-matching to determine and eliminate the disparity between the GPS trajectories and reference map. Finally, we perform experiments by considering the data of a sophisticated GPS/inertial navigation system as the ground truth and demonstrate that the proposed method provides lane-level position accuracy for vehicle localization.

It is important not only to diagnosis the wiring of the vehicle in operation but also to find fault and wire mismatch during the assembly process. We propose a new method combining time-frequency domain reflectometry and deep learning to verify that the wire is connected to the proper port of underhood electrical center. Considering the frequency characteristics of each wire (black, blue, red, yellow), we develop an optimization signal design algorithm. Using the time-frequency cross correlation (TFCC), the reflected signal generated at the impedance discontinuities is acquired and converted into the Wigner-Ville distribution image. Through the proposed algorithm, the existing images are converted into new images which are easy to distinguish among groups. The new images are used as input of convolutional neural network and learned the feature of each group. The lengths, compensation filters, and the port information to be connected each wire are stored in the filter bank. If the derived distance using the TFCC is different from the stored length, the wire is considered defective and the acquired signal is restored by the compensation filter designed by overcomplete wavelet transform method. Experimental results demonstrate the effectiveness of the proposed method for detecting wire mismatch and fault location.

This paper presents an output-feedback exponential stabilization condition of sampled-data polynomial fuzzy control systems under variable sampling rates. Compared with previous work, the proposed method is less conservative because of the newly developed time-dependent fuzzy Lyapunov-Krasovskii functional that is based on the conventional fuzzy Lyapunov function. Moreover, the controller is allowed to contain polynomial gain matrices, thereby improving the control performance and design flexibility. This is realized by assuming the difference between the continuous- and discrete-time state vectors as time-varying norm-bounded uncertainties, which are manipulated using a robust control technique. A new sufficient condition is introduced to cast the stability condition containing the integral term as the sum-of-square conditions. Finally, the effectiveness of the proposed method is validated by simulations.

Semantic 3D maps are required for various applications including robot navigation and surveying, and their importance has significantly increased. Generally, existing studies on semantic mapping were camera-based approaches that could not be operated in large-scale environments owing to their computational burden. Recently, a method of combining a 3D Lidar with a camera was introduced to address this problem, and a 3D Lidar and a camera were also utilized for semantic 3D mapping. In this study, our algorithm consists of semantic mapping and map refinement. In the semantic mapping, a GPS and an IMU are integrated to estimate the odometry of the system, and subsequently, the point clouds measured from a 3D Lidar are registered by using this information. Furthermore, we use the latest CNN-based semantic segmentation to obtain semantic information on the surrounding environment. To integrate the point cloud with semantic information, we developed incremental semantic labeling including coordinate alignment, error minimization, and semantic information fusion. Additionally, to improve the quality of the generated semantic map, the map refinement is processed in a batch. It enhances the spatial distribution of labels and removes traces produced by moving vehicles effectively. We conduct experiments on challenging sequences to demonstrate that our algorithm outperforms state-of-the-art methods in terms of accuracy and intersection over union.