A intelligent particle swarm optimization for short-term traffic flow forecasting using on-road sensor systems

On-road sensor systems installed on freeways are used to capture traffic flow data for short-term traffic flow predictors for traffic management, in order to reduce traffic congestion and improve vehicular mobility. This paper intends to tackle the impractical time-invariant assumptions which underlie the methods currently used to develop short-term traffic flow predictors: i) the characteristics of current data captured by on-road sensors are assumed to be time-invariant with respect to those of the historical data, which is used to developed short-term traffic flow predictors; and ii) the configuration of the on-road sensor systems is assumed to be time-invariant. In fact, both assumptions are impractical in the real world, as the current traffic flow characteristics can be very different from the historical ones, and also the on-road sensor systems are time-varying in nature due to damaged sensors or component wear. Therefore, misleading forecasting results are likely to be produced when short-term traffic flow predictors are designed using these two time-invariant assumptions. To tackle these time-invariant assumptions, an intelligent particle swarm optimization algorithm, namely IPSO, is proposed to develop short-term traffic flow predictors by integrating the mechanisms of particle swarm optimization, neural network and fuzzy inference system, in order to adapt to the time-varying traffic flow characteristics and the time-varying configurations of the on-road sensor systems. The proposed IPSO was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information can be captured on-line by the on-road sensor system. These results clearly demonstrate the effectiveness of using the proposed IPSO for real-time traffic flow forecasting based on traffic flow data captured by on-road sensor systems

Automatic Mode-Matching in MEMS Vibrating Gyroscopes Using Extremum Seeking Control

In order to enhance the sensitivity and to reduce the readout circuit complexity of any angular velocity microsensor (vibrating gyroscope), it is crucial to reduce the frequency mismatch of its resonant modes of vibration. Achieving a good matching accuracy during fabrication is rather difficult because of tolerances and process variations that detrimentally affect the manufacturing precision. Moreover, even assuming to achieve a good frequency matching through fabrication or postfabrication calibration, it is very likely that parametric variations induced by the external environment during the normal operation of the device disrupt any initial tuning. For these reasons, in this paper, an alternative way to accomplish the frequency-matching condition is suggested, which exploits a real-time adjusting mechanism based on an automatic mode-matching control loop. In particular, this paper describes the details of an adaptive controller capable of automatically matching the resonant frequencies of the two main modes of vibration of a single-axis vibrating microgyroscope, under the provision that there is an underlying mechanism through which the frequency mismatch can be controlled by adjusting a suitable tunable parameter. The controller is designed by considering the requirement of reducing its complexity, so that it can be easily implemented on cheap sensors. Owing to a key observation that allows the recast of the frequency-matching problem as a maximization problem, the proposed mode-matching controller is actually designed as a standard perturbation-based extremum-seeking controller, which can be implemented by using few analog electronic components. The proposed solution has been tested on the LISY300AL yaw-rate microelectromechanical system gyroscope manufactured by STMicroelectronics, showing that a mode matching of nearly 1 Hz or less can be easily attained

Advanced interface systems for readout, control, and self-calibration of MEMS resonant gyroscopes

MEMS gyroscopes have become an essential component in consumer, industrial and automotive applications, owing to their small form factor and low production cost. However, their poor stability, also known as drift, has hindered their penetration into high-end tactical and navigation applications, where highly stable bias and scale factor are required over long period of time to avoid significant positioning error. Improving the long-term stability of MEMS gyroscopes has created new challenges in both the physical sensor design and fabrication, as well as the system architecture used for interfacing with the physical sensor. The objective of this research is to develop interface circuits and systems for in-situ control and self-calibration of MEMS resonators and resonant gyroscopes to enhance the stability of bias and scale factor without the need for any mechanical rotary stage, or expensive bulky lab characterization equipment. The self-calibration techniques developed in this work provide 1-2 orders of magnitude improvement in the drift of bias and scale factor of a resonant gyroscope over temperature and time.Ph.D