• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1950-1955
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• 1991

A fuzzy dynamic learning controller is proposed and applied to control of time delayed, non-linear and unstable chemical processes. The proposed fuzzy dynamic learning controller can self-adjust its fuzzy control rules using the external dynamic information from the process during on-line control and it can create th,, new fuzzy control rules autonomously using its learning capability from past control trends. The proposed controller shows better performance than the conventional fuzzy logic controller and the fuzzy self organizing controller.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.559-569
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• 2007

In this paper, the dynamic controller design problem of a redundant planar 2-dof parallel manipulator is studied. Using the Euler-Lagrange equation, we formulate the dynamic model of the parallel manipulator in the joint space and propose an augmented PD controller with forward dynamic compensation for the parallel manipulator. By formulating the controller in the joint space, we eliminate the complex computation of the Jacobian matrix of joint angles with end-effector coordinate. So with less computation, our controller is easier to implement, and a shorter sampling period can be achieved, which makes the controller more suitable for high-speed motion control. Furthermore, with the combination of static friction model and viscous friction model, the active joint friction of the parallel manipulator is studied and compensated in the controller. Based on the dynamic parameters of the parallel manipulator evaluated by direct measurement and identification, motion control experiments are implemented. With the experiments, the validity of the dynamic model is proved and the performance of the controller is evaluated. Experiment results show that, with forward dynamic compensation, the augmented PD controller can improve the tracking performance of the parallel manipulator over the simple PD controller.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1972-1977
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• 2005

In this work, an inverse dynamic control method is developed to enhance tracking performance of industrial robots, which effectively deal with the nonlinear dynamic interferential forces. In general, the DFF (Dynamic Feed-Forward) controller and the CTM (Computed-Torque Method) controller are used for dynamic control for industrial robots. We study on the practical issues for implementing these inverse dynamic controllers via simulations and experiments. We develop the dynamic models in two different ways. One is a model designed through Newton-Euler method for real time computation and the other is a model designed through SimMechanics for evaluating the developed controller via simulations. We evaluate the nominal performance and robustness of the controller via simulations and experiments using serial 4-DOF HyRoHILS (Hyundai Robot Hardware-In-the-Loop Simulation) system. The results show that the inverse dynamic controller is effective and practically useful for a real control structure.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.6
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• pp.272-279
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• 2000

This paper describes a controller design and dynamic performance analysis of UPFC based on 3-level inverters. Major attention is focused on the controller design for both shunt and series inverters, including regulator design for the dc link voltage sharing across the dc capacitors. An energy-based approach was investigated for effectively designing the controller. A detailed UPFC model has been developed with EMTP using 24-pulse 3-level inverters to verify this approach. Simulation results about dynamic performance of UPFC confirm effects for increasing transmission capacity and damping low-frequency oscillation. The developed simulation model would be very effective to analyze the dynamic performance of UPFC.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.109-114
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• 2008

In this paper, a dynamic learning controller for robot manipulator is implemented using real-time operating system with capabilities of multitasking, intertask communication and synchronization, event-driven, priority-driven scheduling, real-time clock control, etc. The controller hardware system with VME bus and related devices is developed and applied to implement a dynamic learning control scheme for robot manipulator. Real-time performance of the proposed dynamic learning controller is tested and evaluated for tracking of the desired trajectory and compared with the conventional servo controller.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2188-2190
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• 2004

This paper presents an implementation of power system stabilizer using inverse dynamic neuro controller. Traditionally, mutilayer neural network is used for a universal approximator and applied to a system as a neuro-controller. In this case, at least two neural networks are used and continuous tuning of neuro-controller is required. Moreover, training of neural network is required considering all possible disturbances, which is impractical in real situation. In this paper, Taylor Model Based Inverse Dynamic Neuro Model (TMBIDNM) is introduced to avoid this problem. Inverse Dynamic Neuro Controller (IDNC) consists of TMBIDNM and Error Reduction Neuro Model (ERNM). Once the TMBIDNM is trained, it does not require retuning for cases with other types of disturbances. The controller is tested for one machine and infinite-bus power system for various operating conditions.

• Journal of Mechanical Science and Technology
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• v.19 no.2
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• pp.618-624
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• 2005

We present a new state-space approach to construct a dynamic output feedback controller which stabilizes a class of linear time invariant systems. All the states of the given system are not measurable and only the output is used to design the stabilizing control law. In the design scheme, however, we first assume that the given system can be stabilized by a feedback law composed of the output and its derivatives of a certain order. Beginning with this assumption, we systematically construct a dynamic system which removes the need of the derivatives. The main advantage of the proposed controller is regarding the controller order, which may be smaller than that of conventional output feedback controller. Using a simple numerical example, it is shown that the order of the proposed controller is indeed smaller than that of reduced-order observer based output feedback controller.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.5-8
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• 1999

The main stream of researches on the mobile robot is planning motions of the mobile robot under nonholonomic constraints while only considering kinematic model of a mobile robot. These researches, however, assume that there is some kind of dynamic controller which can produce perfectly the same velocity that is necessary for the kinematic controller. Moreover, there are little results about the problem of integrating the nonholonomic kinematic controller and the dynamic controller for a mobile robot. Also the literature on the robustness of the controller in the presence of uncertainties or external disturbances in the dynamical model of a mobile robot is very few. Thus, in this paper, the robust adaptive controller which can achieve velocity tracking while considering not only kinematic model but also dynamic model of the mobile robot is proposed. The stability of the dynamic system will be shown through the Lyapunov method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.303-311
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• 2012

In this study, we introduce a dynamic process model as well as the design methodology of optimized fuzzy controller for its efficient application to vacuum production system to produce a semiconductor, solar module and display and so on. In a vacuum control field, PID control method is widely used from the viewpoint of simple structure and preferred performance. But, PID control method is very sensitive to the change of environment of control system as well as the change of control parameters. Therefore, it's difficult to get a preferred performance results from target system which has a complicated structure and lots of nonlinear factors. To solve such problem, we propose the design methodology of an optimized fuzzy PID controller through a following series of steps. First a dynamic characteristic of the target system is analyzed through a series of experiments. Second the process model is built up and its characteristic is compared with real process. Third, the optimized fuzzy PID controller is designed using genetic algorithms. Finally, the fuzzy controller is applied to target system and then its performance is compared with that of other conventional controllers(PID, PI, and Fuzzy PI controller). The performance of the proposed fuzzy controller is evaluated in terms of auto-tuned control parameters and output responses considered by ITAE index, overshoot, rise time and steady state time.

• Journal of Mechanical Science and Technology
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• v.19 no.10
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• pp.1846-1855
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• 2005

This paper presents a new stochastic controller applied on the vibration control system under irregular disturbances based on the mode selection scheme. Measured displacement and frequency characteristics are simultaneously used in designing a mode selecting controller. This technique is validated by applying to the suppression problem of a flexible beam with randomly vibrated circumstances. The presented method, called Mode Selecting Stochastic Controller, uses the frequency measurement of the flexible system based on a Fast-Fourier transformation algorithm. This controller is constructed by combining mode selection method with previous known Stochastic Controller in real time: Numerical simulations and several experiments are conducted to validate the proposed method. The performance of the proposed method is compared with a stochastic controller developed recently. This method was improved compared with previous one.