Majlesi Journal of Electrical Engineering
Volume:9 Issue: 3, Sep 2015

Sliding mode control is a robust controller against modeling imprecisions and external disturbances, successfully employed to the dynamic positioning of autonomous underwater robot. In order to improve the performance of the whole system, the discontinuity in the control law must be smoothed out to avoid the undesirable chattering and unwanted ripples. One of the disadvantages of conventional sliding mode is great vulnerability in the presence of noise. However, noise and some initial condition causing undesirable chattering phenomenon and unwanted ripples in the control input. This paper describes the development of a depth control system for autonomous underwater robot. In this paper we used the sliding surface term and its derivation with adaptive gains in control law instead of the sign function with fixed gain. The proposed controller has been designed to solve great vulnerability of sliding mode control at the presence of noise. For contrast one of factors causing chattering, big controller gain, the gain of controller adapted according to state condition and uncertainties. Due to in the proposed controller there is no sign function, so our controller is not vulnerability to noise. Using this controller, ripples and unexpected sharp peak of the input control signal were canceled and control signal was smoother than conventional sliding mode controller with boundary layer. The stability and convergence properties of the closed-loop system are analytically proved using Lyapunov stability theorem. Simulation results are presented in order to demonstrate the control system performance.

Synthetic Aperture Radar (SAR) images suffer of multiplicative speckle noise, which damages the radiometric resolution of SAR images and makes the data interpretation difficult. Bayesian shrinkage in a transformed domain is a well-known method based on finding threshold value to suppress the speckle noise. This paper present a new approach to obtain the optimum threshold values for Bayesian shrinkage. For this purpose, we use undecimated wavelet transform (UWT), nonsubsampled Contourlet transform (NSCT), and nonsubsampled Shearlet transform (NSST).According to our experimental results, transformed coefficients influenced by noise differently. It means that some coefficients in transformed domain belong to the specific subband are more robust against noise. We use this new found property in order to determine the optimum threshold value and developed our proposed method named weighted Bayesian Shrinkage in transformed domain. Our experimental results show that finding the optimum threshold value in Shearlet domain outperforms both Contourlet and undecimated wavelet transform.Although the weighted Bayesian Shrinkage in NSCT used before, the weighted Bayesian Shrinkage in NSST is applied in this paper for the first time.

The performance of the multiple-estimation (ME) is examined in multiple-input multiple-output (MIMO) frequency-selective fading channels. The least squares (LS) technique, the shifted scaled least squares (SSLS) estimator, and the minimum mean square error (MMSE) estimator are probed in this paper. In the uniform and non-uniform power allocation, the closed form equations are obtained for total mean square error (TMSE) of the estimators. Analytical and numerical results show that the LS estimator has lower error in the case of ME than single-estimation (SE). Moreover, it is seen that the performance of SSLS and MMSE channel estimators in the ME case is better than SE particularly at high signal to noise ratios (SNRs). It is shown that for small numbers of sub-blocks used for channel estimation, the SSLS and MMSE channel estimators are better than LS. For large numbers of sub-blocks, inversely, the LS channel estimator is better than SSLS and MMSE. The un-equal power allocation is also examined analytically and numerically. Simulation results show that exponential power allocation is proper for SSLS channel estimator in ME case.

In this paper we present a nonlinear optimal control method based on approximating the solution of Hamilton-Jacobi-Bellman (HJB) equation. Value function is approximated as the output of Multilayer Perceptron Neural Network (MLPNN). Parameters of MLPNN are weights and biases of each layer that form structure of the proposed neural network. These parameters are unknown thus we apply an Adaptive Extended Kalman Filter to approximate unknown parameters. In so doing, the problem of solution of HJB equation is converted to estimation of MLPNN parameters. Also, convergence of the estimation error of MLPNN parameters is proven. Two examples have been brought to show the merits of the proposed approach and to compare the obtained results by applying the multilayer Perceptron and the Radial Basic Function Neural Network (RBFNN).

We have proposed an all-optical switch for logic gates application using two-dimensional Photonic Crystal (PC) waveguide based on phase difference between incident beams that is created by point defect. A PC with triangular lattice made using Si dielectric rods in the air is used as the main structure of the device. Our proposed devices are XOR and OR logic gates. We have shown if initial phase difference between two inputs is π then they interfere destructively to realize the logical functions. This optical gate has many advantages than electrical logic gates such as low power consumption, high speed about light velocity and simple structure. This device is applicable in frequency range 0.44-0.46 (a/λ), but we use frequency a/λ=0.452 for low dispersion and set the lambda to 1.55 µm. The plane wave expansion method (PWE) is used to photonic band gap (PBG) calculation, and finite-difference time-domain method (FDTD) is used to compute the electrical field distribution in the photonic devices. The designed gates can be used in the photonic integrated circuits (PIC).

Carbon nanotube field effect transistors (CNTFET) with extraordinary properties such as high carrier mobility, excellent thermal conductivity and high current carrying capability could be seen as a good replacement for CMOS technology. By connecting gate and drain in carbon nanotube field effect transistor, this device operates as a diode, similar to a diode-connected transistor in a silicon device. This paper presents a novel design of a high performance current mode (CM) precision full-wave rectifier by using just four diode-tied carbon nanotube field effect transistors. All simulations have been performed in HSPICE, and show that this circuit has an excellent performance at high frequencies.

Under-voltage load shedding is an important measurement to maintain the voltage stability in power systems. In this paper, a new combined index is proposed for under-voltage load shedding. The proposed index is weighted combination of importance, sensitivity and value of loads. This is of paramount importance, since three vital factors such as importance of load, sensitivity of minimum eigenvalue of load flow Jacobian respect to load and the amount of loads are considered for optimal under-voltage load shedding. The algorithm accounts constraints not only in present operating condition but also for predicted next interval load. The proposed method is implemented on IEEE 14-bus test system. Results have been compared with those researches based on sensitivity analysis. The results show effectiveness of proposed index.

In this paper, an inductor-less, high frequency tuning range and low power CMOS voltage controlled oscillator (VCO) is presented. The VCO can be implemented in 0.18 µm CMOS, with 1.8 V supply voltage. By using a novel structure, a high frequency tuning range, low phase noise and low power consumption VCO, is obtained. In order to increase the frequency tuning range, an active inductor is used. In addition, deep triode region transistors are employed to enhance the swing of the output voltage. By using the results of simulation with HSPISE software, the tuning range, phase noise and power consumption are 5.49-9.6 GHz, - 146 dBc/Hz and 5.99 mW, respectively.