In this paper, an Adaptive PID with dynamic sliding mode control technique is proposed to control the speed of a separately excited DC motor. Speed control is done using Sliding mode control (SMC) and Dynamic sliding mode control techniques and to overcome their drawbacks the new controller is designed. The performance of the designed controller is evaluated via MATLAB simulations using linear model of the DC motor with known disturbance. Improvement in the performance of SMC and elimination of chattering is achieved by the proposed adaptive PID with dynamic sliding mode controller. Keywords-DC motor; speed control; PID controller; Sliding mode controller; Dynamic sliding mode controller; Adaptive PID with dynamic sliding mode controller978-1-4799-7800-7/15/$31.00 ©2015 IEEE

Power flow control is the critical factor affecting power transmission system. Unified Power Flow Controller (UPFC) is used to regulate the power flow control in power transmission line. The UPFC is represented with two voltage sources named Voltage Source Model (VSM), which is used to study the behaviour of the UPFC in regulating the active, reactive power and voltage profile in the system. These VSM is incorporated in Newton Raphson (N-R) algorithm. The equations of UPFC and the power balance equations of network are combined in to one set of non-linear algebraic equations by employing Simultaneous method and is calculated according to the Newton raphson algorithm and Performed on the IEEE 30-bus system. Simulation is done in Matlab. The results are compared with and without UPFC in terms of active and reactive power flows in the line and check bus voltages to analyze the performance of UPFC.  Index Terms-Newton-Raphson algorithm, load flow, unified power flow controller, voltage source model

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The purpose of this study is to predict the mass loss of newly developed aluminium based alloy. Two different alloys are prepared by cladding process and the sliding friction and wear properties of this alloy against high carbon high chromium steel are investigated at different normal loads (50 N, 60 N and 70 N) under different sliding distances. Tests are carried at a constant speed of 1 m/sec under oil lubricated conditions by preheating the circulating engine oil 20w40 at a temperature of 80 0 C. The mass losses are measured and recorded for every interval. An artificial neural network (ANN) model is developed to predict the mass loss of newly developed aluminium-based alloy. It is observed that the predicted values have shown good agreement with experimental values with a correlation coefficient of 0.999973. This model can also be used to predict the mass loss of any material.