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.