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.

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.

Flight simulators as an integral component of today aviation industry, play an important role in training the pilots and development of the new equipment. Optimal motion cueing beside the positive characteristics of easy computation and implementation, due to limited performance in keeping the motion system within the workspace in complex maneuvers, is faced with serious obstacles. predictive control method featured with inherent capabilities of dealing with constraints on inputs and state variables, while maintaining the high quality of the output, is faced with progressive development. The task of model predictive control is solving the optimal problem over the control horizon to accommodate the feasible movement of the flight simulator by decreasing as could as the difference of the perception of motion between the pilots in real vehicle and the simulator. This approach is based on minimizing the quadratic cost function incorporating the sensation of motion, the motion system configuration related state variables as well as input control signal. Although in this method, the design of washout filters are not needed. In this article, the systematic design of motion cueing algorithm based on model predictive control is described and its performance in comparison with optimal washout filter cueing method is illustrated. The proposed motion cueing method posing with much limited and smoother movements in surge-pitch maneuver tends to efficiently maintaining the motion system with in its workspace while preserving the same sense of motion. This results in increasing the capabilities of the motion system to be employed in much complex maneuvers.

Typical production objectives in distillation process require the delivery of products whose compositions meet certain specifications. The distillation control system, therefore, must hold product compositions as near the set points as possible in faces of upset. In this project, inferential model predictive control, that utilizes an artificial neural network estimator and model predictive controller, is developed for an industrial multicomponent distillation column. First, composition control by direct measuring composition is used. This method because of large sampling delay has a poor performance. The selection of the temperature measurement points is done for indirect control of the column. The use of temperature loop leads to an offset in the composition, due to the fact that the temperature set-point must be changed when feed disturbances occurred. An artificial neural network estimator is designed to estimate the product compositions from tray temperature measurements. A model predictive controller is used to control column composition based on composition estimates. The performance of the developed inferential model predictive control system is tested for set-point tracking and load rejection.