• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.1
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• pp.75-82
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• 2014

For accurate gain tuning of the lab-manufactured six-axis articulated robot RS2 with less noise, in this study, a program routine using dynamic signal analyzer, which is a realization of a controller design algorithm in the frequency domain, is programmed using LabVIEW$^{(R)}$. The contribution of this paper is the proposal of a simulation technique based on SolidWorks$^{(R)}$ and LabVIEW$^{(R)}$ for the gain tuning of a six-axis articulated robot. To realize the simulation, the LabVIEW$^{(R)}$ program used for experimental gain tuning is incorporated in to SolidWorks$^{(R)}$. A comparison shows that the results of simulation-based gain tuning and experimental gain tuning are almost the same within a 5% error bound. On the basis of the comparison, it can be suggested that the simulation-based technique for gain tuning can be applied instead of experimental gain tuning to a six-axis articulated robot by interlocking SolidWorks$^{(R)}$ and LabVIEW$^{(R)}$.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.744-747
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• 2005

This research studied 6-Axes Articulated Robot Manipulator's gain Tuning in consideration of dynamic. First of all, search fur proportional gain of velocity control loop by dynamic signal analyzer. Proportional gain of velocity control loop is connected to dynamic signal analyzer. Next Select free Proportional Gain value. And Select amplitude X of sinusoidal properly so that enough Velocity Feedback Signal may be paid as there is no group to utensil department. Next step, We can get Bode Diagram of Closed loop transfer function response examination in interested frequency. Integral calculus for gain of velocity loop is depended on integral calculus correction's number. We can obtain open loop transfer function by integrator. And we can know bode diagram's special quality from calculated open loop transfer function. With this, Velocity Control Loop's Parameter as inner loop is controlled. Next In moving, when vibration occurs, it controls notch filter. And finally, we have to control fred-forward filter parameter for elevation of control performance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.271-272
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• 2006

This paper presented a new experimental gain tuning technique for a Multi-Axis Servo System. First, the investigation for proportional gain of velocity control loop by using a Dynamic Signal Analyzer (DSA) was performed. Using the FUNCTION characteristic of DSA based on the Bode plot, the Bode plot of open loop transfer function was obtained. In turn, the integral gain of a servo controller can be found out by using the Integration time constant extracted from the Bode plot of open loop transfer function. In the meanwhile, the positional gain of the servo controller has been obtained by using the Bode plot of the closed loop transfer function. We have also proposed the technique to find out an optimal parameter of a notch filter, which has a great influence on vibration reduction, by using the damping factor extracted from the Bode plot of closed loop transfer function.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.480-483
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• 2003

This paper deals with speed control of Sensorless DC servo motor using a FTHGO(FuzEy-Tuning High Gain observer). In this paper, we improved the problem from row speed section, the problem of sensor for detecting speed of motor, using FTHGO(Fuzzy-Tuning High-Gain Observer) with fuzzy control technique which is a class of adaptive control technique. In order to verify the performance of the FTHGO(Fuzzy-Tuning High Gain Observer) which is proposed in this paper, it is proved from the experiment to compare the case with a speed sensor to the case with FTHGO(Fuzzy-Tuning High Gain observer) in the speed control of DC servo motor.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.11
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• pp.1207-1213
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• 2012

Typically commercial controllers do not give data of the controller gains. Therefore, it is very hard to determine the optimal controller gain even though the dynamic model is derived. In this case, design of experiment (DOE) methodology can be a powerful tool for gain tuning. In this research, gain tuning process is proposed based on the DOE. Micro parallel mechanism platform with 3 degrees-of-freedom (DOF) is used for the experiments. Controller gains are measured indirectly from the voltages of adjustable resistors. The controller gains of three actuators are optimized by two or three steps, respectively. The correlations of the controller gains are also analyzed. The process and methodology can be adopted in gain tuning of other mechanical systems.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.192-194
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• 1996

This paper propose an optimal scaling gain tuning method of the fuzzy PI controller using Genetic Algorithm(GA). Scaling gains can reflect the control resolution and fuzziness of input/output variables. By the scaling gain method, the design of a fuzzy logic controller(FLC) can be simplified without affecting the system performance in comparison with multi-decision table method. In designing a fuzzy logic controller, the analytic approach method for the optimization is unavailable. Therefore GA is excellent optimization algorithms for scaling gain tuning. Using this optimal scaling gain tuning method, a good performance can be achieved both in transient and steady state.

• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.39-43
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• 2016

In this paper, we propose PID gain auto tuning method in steering type mobile robot. PID controller gain select method are various methods. Ziegler-Nichols step tuning method is one method tuning in PID controller. Use step tuning method find a the first gain and did experiment in steering mobile robot. and Make a new the second gains from the first gains. After appling the second gain in PID controller, Where perform observe for convergence time and stabilization error. Experiments result the second gain are useful in real steering mobile robot system.

• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.813-819
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• 2009

Designing actuator controllers of aircraft, which control aileron, flap, elevator and so on, is quiet difficult, because they have time variant nonlinear mechanical structures and also have many kinds of disturbances which are not been able to model easily. This paper reports about the performance of Fuzzy Auto gain tuning Control algorithm applied unmaned aerial vehicle. Fuzzy Auto gain tuning PID control uses PID control and Fuzzy control, therefore It can be applied very easily and it also has advances of PID control. It can control a unmaned aerial vehicle actuators adaptively even though the designer does not have enough information of plant.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.395-396
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• 2013

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.3
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• pp.318-324
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• 2014

Recent years have witnessed a growing demand for a wide variety of high-performance industrial robots. In this paper, for accurate gain tuning of a 6-axis articulated industrial robot with reduced noise, a program routine for a dynamic signal analyzer (DSA) using the frequency response method will be programmed using $LabVIEW^{(R)}$. Then, robot transfer functions can be obtained experimentally using the frequency response method with the DSA program. Data from the robot transfer functions are transformed into Bode plots, based on which an optimal gain tuning will be executed. Gain tuning can enhance the response quality of the output signal for a given input signal during real-time control of the robot. The effectiveness of our proposed technique will be verified by implementation with a (lab-manufactured) 6-axis articulated industrial robot (hereinafter called "RS2") and comparison with the zero position gain tuning, as well as other positions.