• Transactions on Control, Automation and Systems Engineering
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• v.1 no.2
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• pp.94-98
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• 1999

Optical scanning systems use glavanometers to point the laser beam to the desired position on the workpiece. The angular speed of a galvanometer is typically controlled using Proportional+Integral+Derivative(PID) control algorithms. However, natural variations in the dynamics of different galvanometers due to manufacturing, aging, and environmental factors(i.e., process uncertainty) impose a hard limit on the bandwidth of the galvanometer control system. In general, the control bandwidth translates directly into efficiency of the system response. Since the optical scanning system must have rapid response, the higher control bandwidth is required. Auto-tuning PID algorithms have been accepted in this area since they could overcome some of the problems related to process uncertainty. However, when the galvanometer is attached to a larger mechanical system, the combined dynamics often exhibit resonances. It is well understood that PId algorithms may not have the capacity to increase the control bandwidth in the face of such resonances. This paper compares the achieable performance and robustness of a galvanometer control system using a PID controller tuned by the Ziegler-Nichols method and a controller designed by the Quantitative Feedback Theory(QFT) method. The results clearly indicate that-in contrast to PID designs-QFT can deliver a single, fixed controller which will supply high bandwidth design even when the dynamics is uncertain and includes mechanical resonances.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.598-603
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• 2004

An optical head actuator of an optical disk drive consists of two servo mechanisms for the focusing and the tracking to acquire data from disk. As the rotational speed of the disk grows, the utilized lag-lead-lead compensator has known to be above its ability for precisely controlling the optical head actuator. To overcome the difficulty, this paper propose a new controller design method for optical head actuator based fuzzy proportional-integral-derivative (PID) control and the genetic algorithm(GA). It employs a two-stage control structure with a fuzzy PI and a fuzzy PD control and is optimized by the GA to yield the suboptimal fuzzy PID control performance. It is shown the feasibility of the proposed method through a numerical tracking actuator simulation.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.139-148
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• 2024

In this paper, we propose a two-degree-of-freedom snake robot head system and an I-PID (Intelligent Proportional-Integral-Derivative)-based controller utilizing RBF (Radial Basis Function) neural network and adaptive robust terms as a control strategy to reduce rotation occurring in the snake robot head. This study proposes a two-degree-of-freedom snake robot head system to avoid complex snake robot dynamics. This system has a control system independent of the snake robot. Subsequently, it utilizes an I-PID controller to implement a control system that can effectively manage rotation at the snake robot head, the robot's nonlinearity, and disturbances. To compensate for the time delay estimation errors occurring in the I-PID control system, an RBF neural network is integrated. Additionally, an adaptive robust term is designed and integrated into the control system to enhance robustness and generate control inputs responsive to signal changes. The proposed controller satisfies stability according to Lyapunov's theory. The proposed control strategy was tested using a 9-degreeof-freedom snake robot. It demonstrates the capability to reduce rotation in Lateral undulation, Rectilinear, and Sidewinding locomotion.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2252-2257
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• 2003

In this paper, we develop the so-called functional test model for magnetic bearing reaction wheels. The functional test model has three degree of freedom, which consists of one axial suspension from gravity and the other two axes gimbaling capability to small angle, and does not include the motor. For the control of the functional test model, we derive the optimal electromagnetic forces based on the least squares method, and use the proportional-integral-derivative controller. Then, we develop a hardware setup, which mainly consists of the digital signal processor and the 12-bit analog-to-digital and digital-to-analog converters, and show the experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1110-1115
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• 2004

Water covers two-thirds of the earth and has a great influence on the future existence of all human being. Thailand has extensive coastline and near shore water that contain vast biological and mineralogical resources. The rivers and canals can be found around the country especially in the Bangkok, which once called the Venice of the East. Autonomous underwater robot (AUR) will be soon a tool to help us better understand water resources and other environmental issues. This paper presents the design and basic control of a six-degree of freedom AUR "Chalawan", which was constructed to be used as a testbed for shallow. It is a simple low cost open-frame design, which can be modified easily to supports various research areas in the underwater environment. It was tested with a conventional proportional-integral-derivative (PID) controller. After fine-tuning of the controller gains, the results showed the controller's good performances. In the future, the dynamic model of the robot will be analyzed and identified. The advanced control algorithm will be implemented based on the obtained model.

• Journal of information and communication convergence engineering
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• v.2 no.1
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• pp.46-51
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• 2004

The problem of reducing the effect of an unknown disturbance on a dynamical system is one of the most fundamental issues in control design. We propose a robust PID (Proportional Integral Derivative) control method with neural network for improving the performance due to the rejection of an unknown disturbance. The proposed system consists of a model of the plant, a conventional PID controller and a multi-layer neural network, and is composed of two loop; the first loop enables the system to achieve stability of system, the second loop rejects an unknown disturbance. Simulation and experiment results show that the proposed method improves considerably on the performance of the conventional PID control method and the typical IMC method using neural network.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.298-301
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• 1999

This paper presents a design technique based on the root locus method fur a class of $n^{th}$ order plants using PID (Proportional-Integral-Derivative) x (n-1) stage PD controller. It is intended to satisfy both transient and steady state response specifications. This controller can be used instead of a conventional PID controller for the higher order plants to obtain better performances. The controlled system is approximated as a stable and robust second order controlled system. Only adjusting the controller gain, the desired performances of the controlled system are satisfied. For the stable plant including the plant with small dead time, the controlled system is made robustly stable. In case of the unstable plant, when the controller gain is adjusted higher than the critical value, the unstable plant can also be made stable. Robustness properties given by this controller proposed in this paper have also been demonstrated by numerical examples.

• Journal of Power Electronics
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• v.15 no.1
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• pp.160-176
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• 2015

This paper presents a method of designing optimal integer- and fractional-order proportional-integral-derivative (FOPID) controllers for a boost converter to gain a set of favorable characteristics at various operating points. A Pareto-based multi-objective optimization approach called strength Pareto evolutionary algorithm (SPEA) is used to obtain fast and low overshoot start-up and dynamic responses and switching stability. The optimization approach generates a set of optimal gains called Pareto set, which corresponds to a Pareto front. The Pareto front is a set of optimal results for objective functions. These results provide designers with a trade-off look-up table, in which they can easily choose any of the optimal gains based on design requirements. The SPEA also overcomes the difficulties of tuning the FOPID controller, which is an extension to the classic integer-order PID controllers and potentially promises better results. The proposed optimized FOPID controller provides an excellent start-up response and the desired dynamic response. This paper presents a detailed comparison of the optimum integer- and the fractional-order PID controllers. Extensive simulation and experimental results prove the superiority of the proposed design methodology to achieve a wide set of desired technical goals.

• Proceedings of the KIEE Conference
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• 1992.07b
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• pp.1242-1244
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• 1992

This Paper deals with a Auto-Tuning Fuzzy PID Controller used in real time and its application for induction motor. The control strategy of the controller is able to develop and improve automatically. The new Auto-Tuning Fuzzy PID Control algorithm which modifies the fuzzy control decision table is presented in this paper. It can automatically refine an initial approximate set of fuzzy rules. The possibility of applying fuzzy algorithms in faster response, and more accurate was compared with other industrial processes, such as AC Motor driver. The performance of Proportional_Integral Derivative(PID) control and this fuzzy controllers is compared in terms of steady_state error, settling time, and response time. And then, Limitations of fuzzy control algorithms are also described.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.472-477
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• 2023

In this paper, we proposes the use of Model Predictive Control (MPC) techniques to enable quadcopter drones to effectively follow paths and maintain flight safety even under dynamic external environments and disturbances. Through simulations conducted in MATLAB/Simulink, the performance of two controllers, PID and MPC, is compared in flight scenarios with disturbances. The proposed design method shows that the MPC controller, when compared to the PID controller, exhibits a difference in the Mean Squared Error between the intended flight path and the actual path of the quadcopter drone. This difference is 0.2 in performance under no disturbance, and it increases to 0.8 under disturbance, demonstrating the improved path following accuracy of the MPC controller.