A dynamic model of an automotive powertrain system is developed by the use of eight states and two time-delays in the continuous-time domain, with careful attention given to the dynamics and kinematics of a four-stroke spark-ignition engine, an automatic transmission, and rubber tires. The model is relatively simple, yet it predicts the important dynamics (including those during a shift) quite well when compared to experimental data. The model is well suited for developing powertrain controllers and can also be used for studying the dynamic behavior of a powertrain system. A great deal of effort was directed toward preserving the generic nature of modeling so that the developed techniques can be easily adapted to different vehicles with a minimum amount of bench tests for obtaining parameters.

An experimental comparison is made between a sliding-mode controller and a classical controller for stabilizing and commanding a magnetic levitation system. The sliding-mode method can be applied to a nonlinear system in the global sense, and the issues of performance and robustness to modeling uncertainties and disturbances can be directly incorporated in the design as tradeoff parameters.

Over the past decade, synthesized nanomaterials, such as carbon nanotube, nanoparticle, quantum dot, and nanowire, have already made breakthroughs in various fields, including biomedical sensors. Enormous surface area-to-volume ratio of the nanomaterials increases sensitivity dramatically compared with macro-sized material. Herein we present a comprehensive review about the working principle and fabrication process of nanowire sensor. Moreover, its applications for the detection of biomarker, virus, and DNA, as well as for drug discovery, are reviewed. Recent advances including self-powering, reusability, sensitivity in high ionic strength solvent, and long-term stability are surveyed and highlighted as well. Nanowire is expected to lead significant improvement of biomedical sensor in the near future.

This paper develops a new approach in regulation and tracking, combining a disturbance compensator with a controller, both of which are formulated in the discrete-time domain using the variable structure concept. For single-input single-output, linear time-invariant systems, an algorithm for exactly decoupling the disturbance estimation dynamics from the sliding mode dynamics is developed. This allows the two dynamic modes to be tuned separately. It is shown that the developed approach preserves the robustness properties of the sliding mode and asymptotically achieves zero tracking error, in the presence of external disturbances and parametric uncertainties. Both computer simulation results and experimental results using a CNC milling machine are used to demonstrate those properties of the proposed method in both regulation and tracking.

This paper presents a distributed design scheme of the Luenberger-type state observer for continuous-time linear dynamical systems. The proposed observer consists of networked local observers, and each local observer computes the estimate by the local measurements, which may not be sufficient to recover the full state. Therefore, by communicating with the neighboring observers, the proposed observer compensates for the insufficient information. The novelty of the proposed method is the design of two observer gains; one for local measurements and the other for the information exchange. By exploring the structure of gain matrices, our design can assign the injection gain only to “detectable” part of the estimate for the local measurements, and for the “undetectable” portion of the estimate, the gain of the information from neighboring observers is set to be relatively higher than others. It turns out that this intuitive idea leads to a simple design of distributed Luenberger-type observers.

This paper presents a new implementation method for efficient simultaneous localization and mapping using a forward-viewing monocular vision sensor. The method is developed to be applicable in real time on a low-cost embedded system for indoor service robots. In this study, the orientation of a robot is directly estimated using the direction of the vanishing point. Then, the estimation models for the robot position and the line landmark are derived as simple linear equations. Using these models, the camera poses and landmark positions are efficiently corrected by a local map correction method. The performance of the proposed method is demonstrated under various challenging environments using dataset-based experiments using a desktop computer and real-time experiments using a low-cost embedded system. The experimental environments include a real home-like setting. These conditions contain low-textured areas, moving people, or changing environments. The proposed method is also tested using the robotics advancement through web-publishing of sensorial and elaborated extensive data sets (RAWSEEDS) benchmark dataset.