This brief deals with the output control problem of Gear Transmission Servo (GTS) systems with asymmetric deadzone nonlinearity between states. To overcome the difficulty of controller design due to the non-differentiability of the deadzone, a brand new differentiable asymmetric deadzone model is put forward, which provides an additional design degree of freedom to approximate the real deadzone with any prescribed accuracy. Based on the differentiability of the new model, a global differential homeomorphism and a simple state feedback controller are proposed to solve the output control problem. Finally, simulation studies are included to demonstrate the main results in this brief.
Localization is an essential technique in mobile robotics. In a complex environment, it is necessary to fuse different localization modules to obtain more robust results, in which the error model plays a paramount role. However, exteroceptive sensor-based odometries (ESOs), such as LiDAR/visual odometry, often deliver results with scene-related error, which is difficult to model accurately. To address this problem, this research designs a scene-aware error model for ESO, based on which a multimodal localization fusion framework is developed. In addition, an end-to-end learning method is proposed to train this error model using sparse global poses such as GPS/IMU results. The proposed method is realized for error modeling of LiDAR/visual odometry, and the results are fused with dead reckoning to examine the performance of vehicle localization. Experiments are conducted using both simulation and realworld data of experienced and unexperienced environments, and the experimental results demonstrate that with the learned scene-aware error models, vehicle localization accuracy can be largely improved and shows adaptiveness in unexperienced scenes.
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