• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.15-17
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• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of nonlinear IIR filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate nonlinear IIR filter parameter using the genetic algorithm.

• Journal of Sensor Science and Technology
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• v.30 no.2
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• pp.88-93
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• 2021

In this paper, a folding sensor based on capacitance is proposed. The sensor was developed to sense the length and angle data for the milli-scale actuators without causing any interference to the actuating joints. For the sensing and testing the robotic joint with reducing the cost and complexity aspects of manufacturing, a simple composition was adopted. The sensor comprises a pair of copper tapes, papers, and wires. The complete sensing unit is constructed by bonding the tapes with the papers and soldering the wire to the copper parts. For accuracy, a teensy 4.0 board, which has a 12-bit ADC resolution, is employed. Furthermore, the sensed analog data is not translated into the unit of capacitance for accuracy; however, it is filtered using a low-pass filter and subsequently, a Butter-worth filter. The data obtained demonstrate a periodic waveform, which implies that the data are in good agreement with the hypothesis set prior to the experiments. Compared to other milli-scale sensors, this could be a better option for sensing the length and angle data for milliscale actuators.