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This article presents a real-time force-controlled pneumatic compliant polishing tool with self-supplied abrasives making the abrasives added to the contact area between the polishing pad and workpiece, which can lead to good polishing performance. And a force–position decoupling pneumatic servo system for polishing force control is presented to solve the problem of force–position coupling in the traditional polishing process. However, it is difficult to realize satisfactory control performance due to the problems of time variance, compliance, hysteresis and nonlinearity in pneumatic servo system. This article proposes an online tuning neural proportional–integral–derivative controller to improve force control performance. The real-time polishing force control results of pneumatic servo control system are investigated by adopting proposed neural proportional–integral–derivative control strategies with data acquisition card and RTW toolbox in MATLAB/Simulink. The real-time experimental results of polishing force control indicate that the excellent control performance can be achieved by the proposed neural proportional–integral–derivative controller no matter the reference force is constant or varied. Compared with those by traditional proportional–integral–derivative controller, the duration for stabilizing the polishing force is shorter and the error between the output force and the reference is smaller by the proposed neural proportional–integral–derivative controller.

This study addressed the modeling of route travel times (including their associated uncertainty) in urban networks based on taxi floating car data. The model decomposes observed link travel speeds into the expected speed (modeled with daily and seasonal profiles) and deviations thereof. The latter were shown to be strongly heteroscedastic by providing an explicit model for the time variance. Temporal and spatial correlations were considered with a vector autoregression framework. Modeling was supported by automatic model selection methods for identifying the relevant effects and providing one-step-ahead predictions. The potential of the proposed model was investigated with taxi floating car data from a real-world test site near the city core of Vienna, Austria. Various specifications of the vector autoregression model were tested and compared. Taxi floating car measurements of local speeds were found to be strongly heteroscedastic, a factor that must be considered in estimation of models for expected travel speeds. The modeling of the mean suggested no remaining daily or weekly patterns, and it was superior to simple models explaining travel speeds as a linear function of the travel speeds in the last time period. The variance model successfully captured heteroscedasticity. More complex models for link travel speeds, including temporal and spatial correlation, do not increase prediction accuracy consistently; the lack indicates that a sampling frequency of 15 min for floating car data in urban settings is too low for use of temporal dependencies for prediction. An introduced method for computing route travel time uncertainty showed variability over the day for a highly frequented route.

Transportation Research Record, 2461, ISSN:0361-1981, ISSN:2169-4052

This paper uses (linearized) Kalman filters to estimate first-order geometric parameters (i.e., orientation of contact normals and location of contact points) that occur in force-controlled compliant motions. The time variance of these parameters is also estimated. In addition, transitions between contact situations can be monitored. The contact between the manipulated object and its environment is general, i.e., multiple contacts can occur at the same time, and both the topology and the geometry of each single contact are arbitrary. The two major theoretical contributions are 1) the integration of the general contact model, developed previously by the authors, into a state-space form suitable for recursive processing; and 2) the use of the reciprocity constraint between ideal contact forces and motion freedoms as the “measurement equation” of the Kalman filter. The theory is illustrated by full 3-D experiments. The approach of this paper allows a breakthrough in the state of the art dominated by the classical, orthogonal contact models of Mason that can only cope with a limited (albeit important) subset of all possible contact situations.

Performance on a signal detection task was explainable by differences between the two dimensional Fourier transforms of the background and target stimuli. A signal detection experiment by Marshak and Osarczuk (1984) used target and background stimuli designed to systematically differ in spatial frequency and orientation. They found that the hypothesized Fourier differences increased sensitivity and decreased decision time. The present paper reports the Fourier analysis of those stimuli which verify and quantify the stimulus manipulation. Multiple regressions were computed using differences in frequency and orientation to explain performance. The results were that 83 percent of d-prime and 74 percent of the decision time variance could be explained by the Fourier differences. These findings indicate that Fourier descriptions of symbols may be used to predict their effectiveness in work station environments.