Unfalsified Adaptive Control (UAC) is a recently proposed robust adaptive control strategy. In this paper, the UAC principles and algorithms are reviewed and Multi-model UAC is followed as an intermediate between UAC and multiple model control. Different approaches in UAC and MMUAC are studied. Also, Multi-model Unfalsified Generalized predictive control (MMUGPC) is proposed, which is a new control design strategy in the UAC framework. For an uncertain system, by utilizing several generalized predictive controllers and discrete switching between them with unfalsified control, a new structure is proposed and appropriate equations are derived. Simulation results show the effectiveness of proposed Multi-model Unfalsified Generalized predictive control.

At the present time, police is not aware about social changes in some matters and instead of forecasting social changes for preventing from their occurrence, confronting with cultural and political violently behaviors of different stratum of people and responsible. The lack of situation forecasting arise to making a decision in police which moreover than severe increasing of organizational expenses (human resources, budget and facilities) will deteriorated the conditions. This study has tried to focus on social crisis and presented a model for their forecasting. With regards to this model, the most essential administrative organization of crisis control (police) will be capable of preventing from such crisis occurrences. This study is cross functional, applied, descriptive, compound, explorative and research. The results showed that among the important and effective factors on forecasting social changes, the majority of addressees had been concentrated on all items (time, matters, people and geography) alternatives. With regards to these results, the attitude is that the highest degree of security and social peace is established in the time that problem-solving had been upon discourse and consisted of total spectrum of social security and peace (software and holistic approach). 31 agents have considered as promotive agents of social security which increasing social welfare, expanding social justice and legal entity are settled in first to third rank for safeguarding interests of groups or special groups. At the end, the social crisis forecasting model has presented for preventing from those via Naja with emphasis to problems, people, time and geography components.

Background and objective: It is necessary to identify factors influencing smoking in order to design effective interventions to reduce this behavior. This study aimed to determine the predictive factors of smoking among adults of Arak city based on Transtheoretical model (TTM).Methods: This analytical- cross sectional study was carried out on 328 smokers 30 years of age and older in the city of Arak in 2014. Samples were selected randomly from family file numbers of Arak. Data were collected using a valid and reliable questionnaire including demographic information and questions related to Transtheoretical structure model (stages of change, processes of change, temptation, Pros and Cons of smoking) and were analyzed using oneway ANOVA, Pearson correlation and linear regression. Results: Most of the subjects (52.7%) were in the precontemplation stage of smoking cessation. Linear regression revealed that variables of change stages (R2=-0.46, p > 0.001), temptation (R2=-0.18, p>0.001), hazards of smoking (R2=-0.12, p>0.001), early age of smoking (R2=-0.14, p>0.006), dramatic relief process of change (R2=-0.12, p>0.015) and the number of friends who smoke (R2=-0.12, p>0.048) were the predictor of daily smoking rates. The model was able to predict 37% of smoking rate variance.Conclusion: Most subjects were in the precontemplation stage of quitting smoking and according to predictive factors of smoking it is necessary to plan an effective educational program for quitting smoking.

This paper proposes an MPC-based (model predictive control) scheme to control active and reactive powers of DERs (distributed energy resources) in a grid-connected mode (either through a bus with its associated loads as a PCC (point of common coupling) or an MG (micro-grid)). DER may be a DG (distributed generation) or an ESS (energy storage system). In the proposed scheme, the set-points provided to MPC are forecast for future instances by a linear extrapolation to gain smooth active and reactive power exchange under various loading conditions (e. g. balanced / imbalanced, nonlinear and dynamic loading) and voltage imbalance imposed by the upstream grid. In this scheme active and reactive power control change to current control and the references of the currents are forecast. The stability of the proposed control scheme is analyzed and discussed. The effectiveness of the proposed scheme is demonstrated by extensive time-domain simulations using PSCAD / EMTDC for various conditions (various loads, voltage imbalance, parallel operation with other DGs, parameter uncertainties and measurement noises) in several case studies. Comparing the obtained results with those of the two other schemes (PI-based and convectional MPC) shows the superiority of the proposed scheme.

In this paper autonomous modification of satellite attitude in its orbit is implemented for low earth orbit satellites using model predictive Control. MPC minimizes force which applying on the thrusters subject to hard constraints on the control input and output of the system. Here Clohessy-Wilshire equations are utilized by consideration of main disturbance of low orbits such as Atmospheric Drag and Non-Spherical earth disturbance for the design of MPC. The result of simulation proves that this MPC can amend the difference between the linear and non-linear mathematical models of the satellite. In addition, not only orbital disturbances are compensated but also the satellite maintained in orbit with high accuracy. For performance evaluation of this MPC method, A Linear Quadratic Regulator is realized for Orbit's autonomous control; the result of these two methods demonstrated that fuel consummation and control effort in MPC manner is considerably less than LQR method.

Endeavor achieving as much as realistic perception of motion in motion cueing design are shifted towards utilizing the general nonlinear models of the motion systems. However، this approach is computationally more demanding. Hence، using efficient computational algorithms are essential to fulfill the requirements of high computing speed and accuracy without any interruption in real-time operations. Takagi-Sugeno fuzzy system as a major sector of soft computing characterized by effectively dealing with nonlinear processes، high computational speed، and easy implementation is used to approximate the nonlinear inverse kinematic of the motion system model by fuzzy interpolation of the set of linear sub models. The augmented linear parameter varying motion cueing model is constructed incorporating the fuzzy approximated inverse kinematics of the motion system along with the human perception of motion. The complete motion cueing model is computed using real-time model predictive control approach considering all physical constraints. The results of simulation show remarkable improvements in terms of less and smooth movements of actuators، hence much efficient use of motion system’ s workspace compared to its general nonlinear counterpart، which reveals an effective alternative in real-time applications.

Introduction: Preeclampsia is one of the three main causes of death in pregnant women. The medical condition is identified by hypertension and proteinuria with serious effects on the health of mother and the fetus. There seems to be no precise methods to diagnose preeclampsia at its onset. This study was done to evaluate the simultaneous measurement of some variables thought to be responsible in the pathogenesis of preeclampsia for predicting or screening those at risk.Materials and Methods: In this study, 466 primiparas were selected randomly among the bulk of pregnant women who attended Maryam Hospital for prenatal care in Tehran, Iran during 2007-2008. The subjects had no history of chronic health conditions and regularly took Iron supplements. The predictive variables included age, job, education, income, number of previous marriages, BMI during the first trimester of pregnancy, age at the time of recruitment for the study, changes in hematocrit concentration at the beginning and the 24th to 28th weeks of pregnancy, blood pressure and roll-over test during 28th to 32nd week of gestation but the onset of preeclampsia was considered as a dependent variable. For analyzing the overall effects of the mentioned variables on prediction of the disease, multivariate logistic regression analysis was employed and ROC curves were used for determining a suitable cut-off point for determining the sensitivity and specificity of the model.Results: The prevalence of preeclampsia was 6.4% (95% CI: 4.2-8.6). Variables such as positive roll-over test, fulfillment of university education, marriage more than once, high blood pressure during the 24th-28th weeks of gestation, being a housekeeper, satisfaction with income, positive roll over test at a late stage of gestation and increase in BMI raised the risk of preeclampsia 8.61, 7.98, 2.65, 1.84, 1.56, 1.28, 1.21 and 1.11 times respectively. The proposed logistic regression model had a sensitivity of 83% and a specificity of 76% regarding the inclusion of all the mentioned variables.Conclusion: Regarding the serious complications and negative effects of preeclampsia on both the mother and the fetus and the high sensitivity of this logistic regression model and imposition of no costs on the person for the measurement of the variables, this model seems to be suitable for the screening of preeclampsia.