The performance of an advanced research version of the Weather Research and Forecasting Model (WRF) in predicting near-surface atmospheric temperature and wind conditions under various terrain and weather regimes is examined. Verification of 2-m temperature and 10-m wind speed and direction against surface Mesonet observations is conducted. Three individual events under strong synoptic forcings (i.e., a frontal system, a low-level jet, and a persistent inversion) are first evaluated. It is found that the WRF model is able to reproduce these weather phenomena reasonably well. Forecasts of near-surface variables in flat terrain generally agree well with observations, but errors also occur, depending on the predictability of the lower-atmospheric boundary layer. In complex terrain, forecasts not only suffer from the model's inability to reproduce accurate atmospheric conditions in the lower atmosphere but also struggle with representative issues due to mismatches between the model and the actual terrain. In addition, surface forecasts at finer resolutions do not always outperform those at coarser resolutions. Increasing the vertical resolution may not help predict the near-surface variables, although it does improve the forecasts of the structure of mesoscale weather phenomena. A statistical analysis is also performed for 120 forecasts during a 1-month period to further investigate forecast error characteristics in complex terrain. Results illustrate that forecast errors in near-surface variables depend strongly on the diurnal variation in surface conditions, especially when synoptic forcing is weak. Under strong synoptic forcing, the diurnal patterns in the errors break down, while the flow-dependent errors are clearly shown.

In this paper, we propose a novel offline minimumtime trajectory planning (MTTP) approach for underactuated overhead cranes. To the best of our knowledge, it is the first optimal solution to the MTTP problem for overhead crane systems, which simultaneously takes into account various constraints, including the bounded swing angle for the payload, bounded velocity, acceleration, and even jerk for the trolley. Different from existing approaches, by means of system discretization and augmentation, the quasi-convex optimization technique is successfully adopted to find the minimum-time solution while satisfying all the aforementioned constraints. Extensive simulation and experiments with comparisons to previously published methods are conducted to show the superior performance of the proposed method. Note that the results derived in this paper also serve as promising guidance in engineering applications, since it provides a performance limit, namely, the possible highest efficiency for automatic or manual operation of overhead cranes. Index Terms-Minimum-time trajectory planning (MTTP), overhead cranes, underactuated systems.0278-0046

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On the basis of the kinematic model of a unicycle mobile robot in polar coordinates, an adaptive visual servoing strategy is proposed to regulate the mobile robot to its desired pose. By regarding the unknown depth as model uncertainty, the system error vector can be chosen as measurable signals that are reconstructed by a motion estimation technique. Then, an adaptive controller is carefully designed along with a parameter updating mechanism to compensate for the unknown depth information online. On the basis of Lyapunov techniques and LaSalle's invariance principle, rigorous stability analysis is conducted. Because the control law is elegantly designed on the basis of the polar-coordinate-based representation of error dynamics, the consequent maneuver behavior is natural, and the resulting path is short. Experimental results are provided to verify the performance of the proposed approach.The vision-based robotic system is composed of a mobile robot and an onboard camera fixed on a pan motion platform. As shown in Figure 1, the robot frame F R is attached to the center of the wheel axis, which is defined as the Y R axis, while the Z R axis is perpendicular to the motion plane of the mobile robot, and the X R axis can be then obtained easily for a standard right-handed coordinate system. The camera frame F C is defined such that the camera center O C lies in the Z R axis and the frames F R and F C are parallel to each other when the rotation angle of the pan camera is zero. The From the previous analysis, it is clear that the largest invariant set M only consists of the equilibrium point in the sense that‡ It should be noted that although P O c 0 .t/ is, as can be seen from (15), merely piecewise smooth because of the usage of the projection function, it is however continuous with respect to initial conditions. Then, on the basis of Lemma 1 in [32], we can conclude that the positive limit L C of P O c 0 .t/ is invariant, which thus makes LaSalle's invariance principle applicable to the closed-loop error system of (20).

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