Robust non-aggressive three-axis attitude control of spacecraft: dynamic sliding mode approach

Conventional sliding mode control (SMC) has been extensively applied in controlling spacecrafts because of its appealing characteristics such as robustness and a simple design procedure. Several methods such as second-order sliding modes and discontinuous controllers are applied for the SMC implementation. However, the main problems of these methods are convergence and error tracking in a finite amount of time. This paper combines an improved dynamic sliding mode controller and model predictive controller for spacecrafts to solve the chattering phenomenon in traditional sliding mode control. To this aim, this paper develops dynamic sliding mode control for spacecraft’s applications to omit the chattering issue. The proposed approach shows robust attitude tracking by a set of reaction wheels and stabilizes the spacecraft subject to disturbances and uncertainties. The proposed method improves the performance of the SMC for spacecraft by avoiding chattering. A set of simulation results are provided that show the advantages and improvements of this approach (in some sense) compared to SMC approaches.     

Parental injury prevention endeavors: A function of health beliefs?

Surveyed 198 parents of 8–17 yr old children to determine how parents' beliefs about injury prevention influenced their preventive efforts. A health belief model helped predict parent-reported teaching of safety skills and preventive environmental interventions. The variables most associated with parental preventive endeavors were the belief that intervention can avoid injury, a realistically high appraisal of the amount of time involved, and feelings of high knowledge and competence to teach safety skills. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Optimal planning of distributed generation from the DisCo and the DGO viewpoints considering the uncertainties in future demand and electricity price

Since electricity demand is increasing continuously, it is necessary to invest in expansion of distribution network capacity. From the asset management point of view, it is necessary to encourage the private sector to invest in distribution network. To do this, distribution network managers must provide important opportunities for private sector to profit from their investments. One of the options for private sector is to invest on distributed generations (DGs). In this regard, distribution company (DisCo) must sign power purchase agreement (PPA) with DG owners (DGOs). So, optimal siting, sizing and PPA rates from economic point of view are important challenges which are considered as the main contribution of this paper. The proposed methodology of this paper applies load and price uncertainties into the planning problem. The proposed scheme involves using non-dominated sorting genetic algorithms II (NSGA II), since it attains non-dominated solutions in which DisCo and the DGO can put their personal preferences into practice. To evaluate the effectiveness of the suggested method, the computer simulations are done on a 33-bus distribution network and the results are discussed.     

Cascaded Kalman and particle filters for photogrammetry based gyroscope drift and robot attitude estimation

Based on a cascaded Kalman–Particle Filtering, gyroscope drift and robot attitude estimation method is proposed in this paper. Due to noisy and erroneous measurements of MEMS gyroscope, it is combined with Photogrammetry based vision navigation scenario. Quaternions kinematics and robot angular velocity dynamics with augmented drift dynamics of gyroscope are employed as system state space model. Nonlinear attitude kinematics, drift and robot angular movement dynamics each in 3 dimensions result in a nonlinear high dimensional system. To reduce the complexity, we propose a decomposition of system to cascaded subsystems and then design separate cascaded observers. This design leads to an easier tuning and more precise debugging from the perspective of programming and such a setting is well suited for a cooperative modular system with noticeably reduced computation time. Kalman Filtering (KF) is employed for the linear and Gaussian subsystem consisting of angular velocity and drift dynamics together with gyroscope measurement. The estimated angular velocity is utilized as input of the second Particle Filtering (PF) based observer in two scenarios of stochastic and deterministic inputs. Simulation results are provided to show the efficiency of the proposed method. Moreover, the experimental results based on data from a 3D MEMS IMU and a 3D camera system are used to demonstrate the efficiency of the method.     

Adoption of New Irrigation Technology Under Production Risk

This paper investigates the factors affecting adoption of new irrigation technologies and also addresses the linkage between this type of technology and production risk. The risk-premium associated with the use of water is estimated by adapting a moment-based approach. Farm-level data were collected from a sample of 187 wheat farms, located in the three major districts of Fars province of Iran during the years 2002 and 2003. Results of this study demonstrate that the risk premium decrease with new irrigation technology. Also, it is concluded that the farmer-specific relative risk premium has positive and significant effect on the decision to adopt the new irrigation technologies. So, farmers that are more risk-averse with respect to their use of water are more likely to adopt new irrigation technologies that allow them to save water and decrease their production (yield) risk.