• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.188-199
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• 2014

Although the fuzzy logic controller is superior to the proportional integral derivative (PID) controller in motor control, the gain tuning of the fuzzy logic controller is more complicated than that of the PID controller. Using mathematical analysis of the proportional derivative (PD) and fuzzy logic controller, this study proposed a design method of a fuzzy logic controller that has the same characteristics as the PD controller in the beginning. Then a design method of a fuzzy logic controller was proposed that has superior performance to the PD controller. This fuzzy logic controller was designed by changing the envelope of the input of the of the fuzzy logic controller to nonlinear, because the fuzzy logic controller has more degree of freedom to select the control gain than the PD controller. By designing the fuzzy logic controller using the proposed method, it simplified the design of fuzzy logic controller, and it simplified the comparison of these two controllers.

• Journal of Drive and Control
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• v.21 no.3
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• pp.28-35
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• 2024

This study compared performances of PID (Proportional Integral Derivative), SMC (Sliding Mode Control), and MPC (Model Predictive Control) strategies applied to a 2DOF (Degree Of Freedom) drone tracking robot. The developed 2DOF robot utilized a depth camera with an IMU (Inertial Measurement Unit), laser pointers, and servo motors to rapidly detect and track objects. Image processing was conducted using the YOLO deep learning model. Through this setup, controllers were attached to the robot to track random drone movements, comparing performances in terms of accuracy and energy consumption. This study revealed that while SMC demonstrated precise tracking without deviating from the path, both PID and MPC controllers showed deviations. Performance-wise, SMC is superior. However, considering economic aspects, PID is more advantageous due to its lower power consumption and relatively minor tracking errors.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.199-207
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• 1998

This paper presents the design and the control of three degree-of-freedom(DOF) fine positioning device based on an electro-magnetic force. The device is designed by use of a magnetic circuit theory and it is capable of fine motion due to the electro-magnetic force. The device consists of permanent magnets, yokes and coils. The magnetic fluxes generated from the permanent magnets constitute magnetic paths through steel, whereas the coils are arranged into the gap between two surfaces of the yokes. Therefore, by supplying current to the coils, the coils are capable of some motions due to Lorentz forces. For the optimal design of the actuating system, the system parameters are defined and investigated under the given constraints. From the system modeling in small displacement, three decoupled equations of motion are obtained. To get better performance of the system, a PID controller is implemented. Experimental results are presented in terms of time response and accuracy.

• International Journal of Automotive Technology
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• v.4 no.4
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• pp.181-191
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• 2003

Since the nonlinearity and uncertainties which inherently exist in vehicle system need to be considered in active suspension control law design, this paper proposes a new control strategy for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a genetic algorithm (GA) based self-tuning PID controller and a fuzzy logic controller in two loops. In the control scheme, the PID controller is used to minimize vehicle body vertical acceleration, the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors. In order to improve the adaptability to the changes of plant parameters, based on the defined objectives, a genetic algorithm is introduced to tune the parameters of PID controller, the scaling factors, the gain values and the membership functions of fuzzy logic controller on-line. Taking a four degree-of-freedom nonlinear vehicle model as example, the proposed control scheme is applied and the simulations are carried out in different road disturbance input conditions. Simulation results show that the present control scheme is very effective in reducing peak values of vehicle body accelerations, especially within the most sensitive frequency range of human response, and in attenuating the excessive dynamic tire load to enhance road holding performance. The stability and adaptability are also showed even when the system is subject to severe road conditions, such as a pothole, an obstacle or a step input. Compared with conventional passive suspensions and the active vehicle suspension systems by using, e.g., linear fuzzy logic control, the combined PID and fuzzy control without parameters self-tuning, the new proposed control system with GA-based self-learning ability can improve vehicle ride comfort performance significantly and offer better system robustness.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2006.06a
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• pp.107-108
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• 2006

This paper presents the design of a night vision system for vessels. Both a hardware system and software modules for stabilization control are developed. In order to stabilize each control axis, the two-degree of freedom(TDF) PID controller is designed and its parameters are tuned using a real-coded genetic algorithm(RCGA). Simulation demonstrates the effectiveness of the proposed system.

• Journal of Navigation and Port Research
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• v.35 no.3
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• pp.227-233
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• 2011

This paper presents the tracking and stabilization problem of a night vision system for marine surveillance. Both a hardware system and software modules are developed to control azimuth and elevation axes independently with compensation for ship motion. A two degree of freedom(2DOF) PID controller is designed and its parameters are tuned using a real-coded genetic algorithm(RCGA). Simulation demonstrates the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.10a
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• pp.123-128
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• 2006

This paper presents the design of a surveillance night vision system for marine ships. Both a hardware system and software modules for tracking control are developed. In order to control each control axis with compensation for ship motion, the two-degree of freedom(TDF) PID controller is designed and its parameters are tuned using a real-coded genetic algorithm(RCGA). Simulation demonstrates the effectiveness of the proposed system.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.839-845
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• 2013

A CSTR (Continuous Stirred Tank Reactor) is a highly nonlinear process with varying parameters during operation. Therefore, tuning of the controller and determining the transition policy of controller parameters are required to guarantee the best performance of the CSTR for overall operating regions. In this paper, a methodology employing the 2DOF (Two-Degree-of-Freedom) PID controller, the anti-windup technique and a fuzzy gain scheduler is presented for the temperature control of the CSTR. First, both a local model and an EA (Evolutionary Algorithm) are used to tune the optimal controller parameters at each operating region by minimizing the IAE (Integral of Absolute Error). Then, a set of controller parameters are expressed as functions of the gain scheduling variable. Those functions are implemented using a set of "if-then" fuzzy rules, which is of Sugeno's form. Simulation works for reference tracking, disturbance rejecting and noise rejecting performances show the feasibility of using the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.346-349
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• 2003

For a traveling crane, various control methods such as neural network predictive control and TDOFPID(Two Degree of Freedom Proportional Integral Derivative) are studied. So in this paper, we proposed improved navigation method to reduce transfer time and sway with anti-collision path for avoiding collision in its movement to the finial coordinate. And we constructed the NNPPID(Neural Network Predictive PID) controller to control the precise move and speedy navigation. The proposed predictive control system is composed of the neural network predictor, TDOFPID controller, and neural network self-tuner. We analyzed ASC(Automated Stacking Crane) system and showed some computer simulations to prove excellence of the proposed controller than other conventional controllers.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.6
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• pp.573-580
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• 2009

In this paper, we develop a functional test model for magnetic bearing wheels. The functional test model is an electromagnetic levitation system that has three degree of freedom, which consists of one axial suspension from gravity and two axes gimbaling capability to small angels. A nonlinear controller with high-gain observers is proposed and the real-time experiment results show that the rotor is accurately levitated at the desired position and well-balanced, which is a suitable result for the potential use an magnetic bearing wheels. Also, the proposed scheme exhibits better performance when it is compared with the conventional PID control method.