In most industrial applications, attaining Minimum output variance certifies the efficiency of system and results in increasing productivity and decreasing energy consuming. Therefore, Minimum variance index is considered as one of the main subjects in control engineering field. A Minimum variance controller is an optimal controller that provides (theoretically) the Minimum possible variance. Designing this controller, however, requires exact models of both the system and disturbance. Due to existence of interactions among loops, modeling MIMO systems is often complicated. Existence of disturbance is another reason for this complication. In this paper, in order to avoid the need for an analytical model of system, VARX model has been used to simultaneous identification of the plant and disturbance. Then, the estimated model is used for designing a Minimum variance controller. In this method, operating data of inputs and outputs of the system are the only requirements for identification of the VARX model. The proposed method has been tested on an experimental benchmark such as a four-tank system which has a nonlinear behavior. The investigation results show that a Minimum variance controller can be designed, based on an identified VARX model, for a MIMO system, without any need for an exact model of the system, while at the same time, capable of providing an acceptable Minimum variance compared to that precisely obtained using the model-based “Interactor-Matrix” method.

A method for designing the implicit generalized Minimum variance self -tuning controllers is introduced so that, in addition to locating the desired poles for closed -loop system, a combination of the control and the output signal variances is also minimized.For this purpose, first, the orders of the controller and the weighting polynomials of the cost function are increased; then by applying the condition of the Minimum combination of the control and the output signal variances, controller's coefficients are calculated provided that the pole locations of the closed - loop system are not changed. The simulation results show considerable reduction in variances without any change in the pole locations.

Summary:Noise is a factor that influences the reliability of the seismic data to provide a better understanding of the hydrocarbon reservoir characteristics. Coherent and random (incoherent) noises are two important types of noise that contaminate the seismic reflection data. Power-line noise falls in the coherent type of noise (Yilmaz, 2001; Sheriff and Geldart, 1995). It is a class of noise often existing in land acquisition in populated areas. It produces a characteristic 50 or 60 Hz sinusoidal noise on seismic reflection traces and covers the seismic data from reflectors. Its amplitude is time invariant, whereas the seismic data amplitude decays with time (Xia and Miller, 2000; Dingus, 2010)…

Complex and variable nature of the river sediment yield caused many problems in prediction of the long-term sediment yield and input into the reservoirs .sediment rating curves generally used to estimate the suspended sediment load of the river and drainage basin. Since the regression equations of the sediment rating curves obtained by logarithmic retransformation and little independent variable include in this equation they also overestimate or underestimate the true load of the rivers. In this research in order to improve and develop sediment yield estimation methods the estimates of 10 kind of sediment rating curves were compared with actual loads of 5 stations in different climate zones of the country by using accuracy and precision index .the results show that in estimating long term mean load of the selected periods the Linear-MVUE sediment rating curve has relatively high accuracy and precision comparing other kinds of sediment rating curves. In average, the accuracy index of this method is about 1.23 that is reasonable among other methods. Since this kind of sediment, rating curve uses different data characteristics and information it could be recommend for application in sediment yield estimation within acceptable confidence limits.

This paper proposes a synthesized method of a Minimum variance adaptive control (MVAC) system to control the cutting force of the Computerized Numerical Control (CNC) turning machine. In this system a linearized state-space model for the CNC turning machine is derived, and a model reference adaptive system (MRAS), which includes the cutting force as a controlled output and the feed rate as a control input, is implemented. The deterministic autoregressive moving average (DARMA) model with auxiliary input is introduced to cope with any disturbances of the controlled system. The parameters of the DARMA model are obtained using the structure of a lattice adaptive control system. The robustness of the proposed controller copes with any changes in depth of cut and/or the cutting speed. These changes are embedded in the linearized model of the CNC machine and are considered as system parameters. Extensive computer simulations are made to prove the applicability of the proposed controller for controlling the cutting force over different operating conditions.

A reliable speech enhancement method is important for speech applications as a pre-processing step to improve their overall performance. In this paper, we propose a novel frequency domain method for single channel speech enhancement. Conventional frequency domain methods usually neglect the correlation between neighboring time-frequency components of the signals. In the proposed method, we take this correlation into account via: 1) considering neighboring correlation for speech signals, we break down the clean speech into two uncorrelated components; 2) considering neighboring correlation for noise, we approximate the noise as a rank-1 component. Then, we design a linearly constrained Minimum variance (LCMV) filter which aims at removing the dominant part of the noise, while keeping the speech signal undistorted. Performance of the proposed method is evaluated in terms of output signal to noise ratio (SNR) and speech distortion index under various noise environments. Evaluation results demonstrate that our method yields higher noise reduction and lower speech distortion compared to some recent methods.

Many studies have investigated the problem of external disturbance rejection and also increasing the attitude control system's robustness against the parametric uncertainties. Due to stochastic properties, noise effect minimization becomes an interesting and challenging problem in the field of spacecraft attitude control that has been underestimated, while control actuators and attitude sensors themselves are important sources of noise generation., the main purpose of this paper is to (i)control the satellite’s attitude and (ii)minimize the variance of output, simultaneously. The Minimum variance controller, which is considered the simplest type of model predictive controller, has a powerful capability for minimizing the effects of output noise. This feature makes it a suitable control scheme for space-based high-resolution photography missions. so,, we described the conventional Minimum variance regulator method at first, then an Incremental version of the regulator has been presented to solve the tracking problem. Finally, the generalized Minimum variance controller which can control both Minimum-phase and non-Minimum-phase systems is derived for a high pointing accuracy spacecraft. The simulation results show the efficiency of the proposed controller to restrain the noise effects in a high-resolution tri-stereo imaging mission.

In this paper, in order to reduce landing gear vibration two adaptive control systems are designed considering the landing and taxi phases. For this purpose, 6 degree of freedom equations of motion of the landing gear and the related transfer functions are extracted. a reduced order model of the overall transfer functions are given as a result of complicated dynamic model. a Lyapunov based model reference adaptive control is designed to absorb the vibration of front wheel of the landing gear at touchdown. In addition, a Minimum variance adaptive controller is designed and implemented on the system to reject the band level disturbances during the taxi phase. The band disturbances are modeled as a colored Gaussian noise and the system parameters as well as noise characteristics are estimated using extended least square approach. Both control systems are investigated to assess the best performance. Numerical simulations of the system in Matlab/Simulink environment show the preferences and satisfactory performances of the proposed vibration control systems. These results are calculated against various inputs including model reference adaptive control and Minimum variance approaches.