Discrete-time adaptive control of robot manipulators

A discrete-time model reference adaptive control scheme is developed for trajectory tracking of robot manipulators. Hyperstability theory is utilized to derive the adaptation laws for the controller gain matrices. It is shown that asymptotic trajectory tracking is achieved despite gross robot parameter variation and uncertainties. The method offers considerable design flexibility and enables the designer to improve the performance of the control system by adjusting free design parameters. The discrete-time adaptation algorithm is extremely simple and is therefore suitable for real-time implementation

Robust adaptive kinematic control of redundant robots

The paper presents a general method for the resolution of redundancy that combines the Jacobian pseudoinverse and augmentation approaches. A direct adaptive control scheme is developed to generate joint angle trajectories for achieving desired end-effector motion as well as additional user defined tasks. The scheme ensures arbitrarily small errors between the desired and the actual motion of the manipulator. Explicit bounds on the errors are established that are directly related to the mismatch between actual and estimated pseudoinverse Jacobian matrix, motion velocity and the controller gain. It is shown that the scheme is tolerant of the mismatch and consequently only infrequent pseudoinverse computations are needed during a typical robot motion. As a result, the scheme is computationally fast, and can be implemented for real-time control of redundant robots. A method is incorporated to cope with the robot singularities allowing the manipulator to get very close or even pass through a singularity while maintaining a good tracking performance and acceptable joint velocities. Computer simulations and experimental results are provided in support of the theoretical developments

Trait-like characteristics of the sleep EEG across adolescent development

Waking and sleep data in adults show high heritability and trait-like characteristics in EEG spectra. This phenomenon has not been examined in children and adolescents where brain development influences the EEG. The present study examines whether a trait-like sleep EEG pattern is detectable across adolescent development. Two consecutive nights of standard sleep recordings were performed in 19 9-10-year-old children and 26 15-16-year-old teens, and were repeated 1.5-3 years later. EEG spectra averaged across the night for non-rapid eye movement and rapid eye movement sleep separately were classified using hierarchical cluster analysis, which showed that all 4 nights of a participant clustered together for a majority of participants. Intraclass correlation coefficients were also very high (>0.7) across nights separated by several years, indicating a trait-like feature of the sleep EEG. In summary, our results, using two measures of stability, indicate that a "trait-like" aspect can be detected in the sleep EEG across adolescent development despite considerable neurodevelopmental changes. This finding indicates that the brain oscillators responsible for generating the sleep EEG signal remain relatively stable across adolescent development

Mode-Dependent Loss and Gain: Statistics and Effect on Mode-Division Multiplexing

In multimode fiber transmission systems, mode-dependent loss and gain (collectively referred to as MDL) pose fundamental performance limitations. In the regime of strong mode coupling, the statistics of MDL (expressed in decibels or log power gain units) can be described by the eigenvalue distribution of zero-trace Gaussian unitary ensemble in the small-MDL region that is expected to be of interest for practical long-haul transmission. Information-theoretic channel capacities of mode-division-multiplexed systems in the presence of MDL are studied, including average and outage capacities, with and without channel state information.Comment: 22 pages, 8 figure

The implication of business intelligence in risk management: a case study in agricultural insurance

The increasing data scales in today’s business sectors coupled with the necessity of risk management raise the importance of business intelligence tools as an integrated solution for the insurance industry. These tools have mostly been used to achieve effective risk management. Although methods of risk management in the insurance industry have been proposed many years ago, the research effort has primarily been focused on predictive analyses. This study aimed to investigate the role of business intelligence as a solution to illustrate its potential in risk management particularly for decision-makers in agricultural insurance. We hypothesized that this would make a preferable decision in uncertain conditions. Sample data from the online transaction process system of Iran agricultural insurance fund were preprocessed in SQL server. Multidimensional online analytical processing architecture was analyzed using Targit business intelligence tool. Our results identified financial risks that lead to a framework of controlling risk based on business intelligence in the agricultural insurance fund