• Journal of Advanced Marine Engineering and Technology
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• v.41 no.1
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• pp.105-110
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• 2017

This paper introduces a heave compensation system for offshore crane when it subjected to unexpected disturbances such as ocean waves, tidal currents or winds and their external force. The dynamic model consists of a crane which is considered to behave in the same manner as a rigid body, a hydraulic driven winch, an elastic rope and a payload. To keep the payload from moving upwards and downwards, PD(Proportional-Derivative) control was applied by using linearization. In order to achieve a better performance, the sliding mode control and the nonlinear generalized predictive control algorithm was applied according to the time-delay. As a result, the oscillating amplitude of the payload was reduced by the control algorithm. Considering the time-delay involved in the system to be one second, nonlinear generalized predictive controller with a robust controller was a suitable control algorithm for this heave compensation system because it made the position of te payload reach the desired position with the minimum error. This paper presented a control algorithm using the robust control and its simulation results.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.53-62
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• 1997

In this study, a controller design method is proposed for controlling the discrete-time chaotic systems efficiently. The proposed control method is based on Generalized Predictive Control and uses NARMAX models as controlled models. In order to evaluate the performance of the proposed method, a proposed controller is applied to discrete-time chaotic systems, and then the control performance and initial sensitivity of the proposed controller are compared with those of the conventional model-based controler through computer simulations. Through simulations results, it is shown that the control performance of the proposed controller is superior to that of the conventional model-based controller and shown that the peorposed controller is less sensitive to initial values of discrete-time chaotic systems in comparison with the conventional model-based controller.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.176-181
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• 1992

In this paper, an adaptive generalized predictive control(GPC) algorithm which minimizes a N-stage cost function is proposed. The resulting controller is based on GPC algorithm and can be used in unknown plant parameters as the parameters of one step ahead predictor are estimated by recursive least squares method. The estimated parameters are extended to G,P, and F amtrix which contain the parameters of N step ahead predictors. And the minimization of cost function assuming no constraints on future controls results in the projected control increment vector. Hence this adaptive GPC algorithm can be used for either unknown system or varing system parameters, and it is also shown through simulations that the algorithm is robust to the variation of system parameters. This adaptive GPC scheme is shown to have the same stability properties as the deterministic GPC, and requires small amount of calculation compared to other adaptive algorithms which minimize N-stage cost function. Especially, in case that the maximum output horizon is 1, the proposed algorithm can be applicable to direct adaptive GPC.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.268-272
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• 2007

These days many scientists make studies of feedback control system for stability on non-linear state and for the maneuver of flying objects. These feedback control systems have to satisfy trajectory condition and angular conditions, that is to say, controllability and stability simultaneously to achieve mission. In this paper, a design methods using model based control system which consists of spiral predictive model, Q-function included into generalized-work function is shown. It is made a clear that the proposed algorithm using SPM maneuvers for controllability and stability at the same time is successful in attaining our purpose. The feature of the proposed algorithm is illustrated by simulation results. As a conclusion, the proposed algorithm is useful for the control of moving objects.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.111-112
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• 2006

A new MR cylinder with built-in valves using Magneto - Rheological fluid (MR valve) is proposed for fluid power control systems. The MR fluid is a newly developed functional fluid whose obvious viscosity is controlled by the applied magnetic field intensity. This MR cylinder, which is composed of cylinder with small clearance and piston with electromagnet, has the characteristics of simple, compact and reliable structure. This paper presents a method to control the pressure of MR cylinder by using Generalized Predictive Control (GPC) algorithm. The differential pressure is controlled by applying magnetic field intensity to MR fluid. The use of GPC controller is to generate a control sequence by minimizing a cost function in such a way that the future system output is driven close to reference over finite prediction horizons. Experimental results from real time control using GPC method compared with conventional PID control method are also shown in this paper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.183-186
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• 2002

In this paper, a predictive control method using wavelet neural network for chaotic nonlinear systems is presented. In our method, we use the adjusting method of the parameter for the training a wavelet neural network. The control signals are directly obtained by minimizing the difference between a reference signal and the output of a wavelet neural network. To verify the efficiency of our method, we apply it to the Duffing and the Henon system, which are a representative continuous and discrete time chaotic nonlinear system respectively.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.326-329
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• 1995

PID control has been widely used for real control system Further, there are muchreasearches on control schemes of tuning PID gains. However, there is no results for discrete-time systems with unknown time-dealy and unknown system parameters. On the other hand, Generalized predictive control has been reported as a useful self-tuning control technique for systems with unknown time-delay. So, in this study, based on minimization of a GPC criterion, we present a self-tuning PID control algorithm for unknown parameters and unknown tiem-delay system. A numerical simulation was presented to illuatrate the effectiveness of this method.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1838-1842
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• 2004

Gain and phase margins of a first order plus delayed time (FOPDT) process controlled by generalized predictive controller (GPC) are related to the control parameters ${\lambda}$ (control move suppression parameter) and ${\alpha}$ (smoothing filter coefficient) and the normalized delay of the process. Variation ranges of gain and phase margins are determined. It is shown that the margins cannot be assigned independently for a wide range of variation and the range is narrowing by increase of the normalized delay of the process. And finally curves are given to use for adjustment of the controller parameters in order to obtain a specific pair of gain and phase margins.

• Nuclear Engineering and Technology
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• v.27 no.6
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• pp.833-844
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• 1995

In self-tuning predictive control algorithm for steam generator is presented. The control algorithm is derived by suitably modifying the generalized predictive control algorithm. The main feature of the unposed method relies on considering the measurable disturbance and a simple adaptive scheme for obtaining the controller gain when the parameters of the plant are unknown. This feature makes the proposed approach particularly appealing for water level control of steam generator when measurable disturbance is used. In order to evaluate the performance of the proposed algorithm, computer simulations are done for an PWR steam generator model. Simulation result show satisfactory performances against load variations and steam flow rate estimation errors. It can be also observed that the proposed algorithm exhibit better responses than a conventional PI controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.457-466
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• 2005

In this paper, we propose a wavelet neural network(WNN) based predictive control method for path tracking of mobile robots with multi-input and multi-output. In our control method, we use a WNN as a state predictor which combines the capability of artificial neural networks in learning processes and the capability of wavelet decomposition. A WNN predictor is tuned to minimize errors between the WNN outputs and the states of mobile robot using the gradient descent rule. And control signals, linear velocity and angular velocity, are calculated to minimize the predefined cost function using errors between the reference states and the predicted states. Through a computer simulation for the tracking performance according to varied track, we demonstrate the efficiency and the feasibility of our predictive control system.