Dynamic IBVS Control of an Underactuated UAV

Abstract-In this paper image based visual servo approach for 3D translational motion and yaw rotation of an underactuated flying robot is considered. Taking into account the complexity of dynamics of flying robots, main objective of this paper is to consider the dynamics of these robots in designing an image based control strategy. Inertial information of the robot orientation is combined with image information in order to have overall system dynamics in a fashion to apply full dynamic image based controller. Suitable perspective image moments are used in order to have satisfactory trajectories in image space and Cartesian coordinates. A nonlinear controller for the full dynamics of the system is designed. Simulation results are presented to validate the designed controller

A Novel Data Reduction Approach for Structural Health Monitoring Systems

The massive amount of data generated by structural health monitoring (SHM) systems usually affects the system’s capacity for data transmission and analysis. This paper proposes a novel concept based on the probability theory for data reduction in SHM systems. The beauty salient feature of the proposed method is that it alleviates the burden of collecting and analysis of the entire strain data via a relative damage approach. In this methodology, the rate of variation of strain distributions is related to the rate of damage. In order to verify the accuracy of the approach, experimental and numerical studies were conducted on a thin steel plate subjected to cyclic in-plane tension loading. Circular holes with various sizes were made on the plate to define damage states. Rather than measuring the entire strain response, the cumulative durations of strain events at different predefined strain levels were obtained for each damage scenario. Then, the distribution of the calculated cumulative times was used to detect the damage progression. The results show that the presented technique can efficiently detect the damage progression. The damage detection accuracy can be improved by increasing the predefined strain levels. The proposed concept can lead to over 2500% reduction in data storage requirement, which can be particularly important for data generation and data handling in on-line SHM systems

Multigene Genetic Programming for Estimation of Elastic Modulus of Concrete

This paper presents a new multigene genetic programming (MGGP) approach for estimation of elastic modulus of concrete. The MGGP technique models the elastic modulus behavior by integrating the capabilities of standard genetic programming and classical regression. The main aim is to derive precise relationships between the tangent elastic moduli of normal and high strength concrete and the corresponding compressive strength values. Another important contribution of this study is to develop a generalized prediction model for the elastic moduli of both normal and high strength concrete. Numerous concrete compressive strength test results are obtained from the literature to develop the models. A comprehensive comparative study is conducted to verify the performance of the models. The proposed models perform superior to the existing traditional models, as well as those derived using other powerful soft computing tools