• Journal of the Korea Institute of Information Security & Cryptology
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• v.9 no.4
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• pp.11-22
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• 1999

Least-mean-square(LMS) adaptive filters have proven to be extremely useful in a number of signal processing tasks. However LMS adaptive filter suffer from a slow rate of convergence for a given steady-state mean square error as compared to the behavior of recursive least squares adaptive filter. In this paper an efficient signal interference control technique is introduced to improve the convergence speed of LMS algorithm with tap weighted vectors updating which were controled by reusing data which was abandoned data in the Adaptive transversal filter in the scheme with data recycling buffers. The computer simulation show that the character of convergence and the value of MSE of proposed algorithm are faster and lower than the existing LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and increase speed of convergence in avoidance burden of computational complexity in reality when it was experimented having the same condition of LMS algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.3
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• pp.299-308
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• 2011

In this study, we develop a pressure observer to measure the cylinder length of a harbor-construction robot. For the robot control, sensors are required to measure the length of a hydraulic cylinder. The cylinder-position sensor is relatively expensive when the operating environment prohibits external approaches for the measurement of the cylinder position. LVDT or linear scales are usually mounted on the outside of the cylinder, which causes poor durability on a construction site. We use a pressure sensor to indirectly estimate the length of the cylinder. The pressure sensor is mounted inside a hydraulic valve box so that it is protected by the box and easy to waterproof for an underwater robot. By treating oil as a compressible fluid, we derive the nonlinear pressure dynamics as a function of the cylinder position, velocity, and pressure. The recursive least squares (RLS) algorithm is applied to identify the dynamic parameters, and the pressure observer estimates the cylinder position through the pressure acting on the head and the rod of the hydraulic cylinder. The position accuracy is relatively low, but it is acceptable for a construction robot that handles large armor stones.