The quality and longevity of an asphalt pavement is influenced by several factors including, the design of the mix and environmental factors at the time of compaction. These factors are difficult to control during the construction process and often result in inadequate compaction of the pavement. Intelligent Compaction (IC) technologies address this issue by providing continuous real-time estimation of the compaction level achieved during construction. This information can then be used to address quality issues during construction and improve the overall quality of the pavement. One of the goals of IC is the dynamic adjustment of the compaction effort of the vibratory roller in order to achieve uniform density and stiffness of the pavement. However, complex dynamics of the compaction process and lack of computationally tractable dynamical models hamper the development of such controllers of vibratory rollers. In this study, the interaction between the vibratory roller and the underlying pavement is studied. A two-dimensional lumped element model that can replicate the compaction in the field is developed and its parameters are determined using the visco-elastic plastic properties and the shear deformation properties of the asphalt mix. Numerical simulation results show that the model is capable of capturing the coupled vibration dynamics of the asphalt-roller system in both the vertical and longitudinal direction. Comparison of numerical studies with the field compaction data also indicates that the model can be helpful in the development of control algorithms to improve the quality of pavements during their construction.

Proper and uniform compaction during construction is of utmost importance for the long term performance of asphalt pavement. Variations in the conditions of freshly laid pavements require adjustment of the compaction effort in order to obtain uniform and adequate density. One of the goals of on-going research in Intelligent Compaction (IC) is the development of adaptive feedback control mechanism to adjust the compaction effort according to the field and pavement conditions. Such feedback control systems require a good understanding of compaction dynamics. In this study, a dynamical model is developed to study the interaction between a moving vibratory roller and the underlying asphalt pavement during compaction. The asphalt pavement is represented as a lumped element model with visco-elastic-plastic properties. A procedure is presented to estimate the parameters of this model from standard tests on asphalt mix conducted in the laboratory. The combined roller-pavement dynamical model is used to replicate field compaction of an asphalt pavement using a vibratory roller. Numerical simulation results indicate good agreement with results observed during compaction of pavements in the field. Comparison between the simulation results and the results collected from the actual pavement construction job show that the model could be used as a mathematical basis for the development of advanced compaction methods.

Recent advances in computing technology have led to the production of a new class of computing device: the wireless, battery powered, smart sensor. A Wireless Sensor Network (WSN) is an ad-hoc network composed of densely-populated tiny electronic sensing devices, distributed over an area or volume. Deployment of a sensor network can be in a random fashion or can be planted manually. These networks promise a maintenance free, fault-tolerant platform for gathering different kinds of data. In this paper, contrary to existing techniques, the deployment problem in a three dimensional space is rigorously analyzed. The problem of determining the minimum number of sensor nodes that guarantee complete coverage is studied which will serve as an optimality measure for the coverage problem.

A Wireless Sensor Network (WSN) is an ad-hoc network composed of densely-populated tiny electronic sensing devices, distributed over an area or volume. One of the fundamental issues in sensor networks is the coverage problem. In this paper, contrary to existing techniques, the coverage problem in a three dimensional space is rigorously analyzed. Given a distribution of sensor nodes, an algorithm to choose a subset of working nodes for full coverage is derived. A computationally efficient algorithm to check for complete coverage is also proposed.

This paper presents a practical method of achieving improved performance from existing industrial robot controllers using intelligent control techniques. While there have been many solutions to dealing with the unmodeled dynamics and disturbances in a robotic system, these results have not found much acceptance in the industrial community as they require a complete redesign of the controller. The strategy presented here uses concepts and results from the intelligent control literature, to build on the existing linear controller. It is shown that the existing conventional linear controller can be augmented by a control component that can compensate for the unmodeled dynamics and disturbances. This component can be manufactured by neural networks, fuzzy logic controllers, or adaptive controllers as long as they satisfy key approximation properties. In this paper, this concept is demonstrated by implementing the design using CMAC neural networks (CMAC NN). The adaptation laws for this controller are designed and the closed-loop performance of the overall system is rigorously proved.

This paper presents a methodology for the design of fuzzy logic controllers (FLC) that guarantees prescribed performance for a general robotic system. It is shown that the proposed online learning algorithm learns the stabilizing membership functions (MFs) online from initial MFs that are selected using simple design criteria. From a practical standpoint, the controller structure leads to efficient implementation and fills a void that existed in the lack of repeatable design methodologies for FLC implementation.