• East Asian mathematical journal
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• v.31 no.3
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• pp.311-319
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• 2015

This paper is concerned with the optimal control for the forest kinematic model. That is, we show the exiatence of the strong solution for the forest kinematic model and then show the existence of the optimal control.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.138-142
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• 2004

This paper proposes the design of the model following control system using the PID control structure. PID control system became model following control by inserting new pre-compensator in order to improve control performance in discrete-time region. Gain of the PID controller needs to be readjusted when response of system changes due to disturbance or load fluctuation. Performance of control system improves by joining neural network to PID control system because performance of control system depends largely on each PID gain in PID control system. And the games of the PID controller in the proposed control system are automatically adjusted by back-propagation algorithm of the neural network. Angular position of DC servo motor is selected as a plant in order to verify control performance in model following control. After it is applied to the position control system, it's performance is verified through computer experiment.

• Journal of Bio-Environment Control
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• v.5 no.2
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• pp.111-119
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• 1996

The purpose of this study is to construct the main system of simulator for the environment control of agricultural production facilities. The model describing the system was based on the energy and mass balance in an unsteady - state situation. The model consist of the three major parts : the main model, the light model, and the environmental control model, and each part was separated to be developed individually. The main model which is the core of this system includes the thermal model, the soil model, the ventilation model, the cultivation model, and the carbon dioxide model. And also the environmental control model includes the thermal curtain model, the heater/cooler model and the underground heat exchanger model. The equations used in this model were written in analog programming methods using PCSMP The simulator was evaluated through comparison between simulated and measured temperatures controlled during daytime and night. The results showed good agreements between the predicted and measured temperatures.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.6 s.165
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• pp.931-942
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• 1999

This paper develops a 3 DOF vehicle model which includes lateral, roll and yaw motion to study a 4WS vehicle. The model is used for the simulation of a 4WS vehicle behavior, and to derive a control algorithm for rear wheel steering. This paper uses a feedforward plus feedback control scheme to compute a rear wheel steering angle. The feedforward control scheme for computing the first rear wheel steering angle uses a gain which is acquired by multiplying a proper value on a gain to maintain a zero sideslip angle. The feedback control scheme for computing the second rear wheel steering angle uses fuzzy logic and model following control scheme. A linear 2 DOF model is used as a reference model for model following control, and is derived from the developed 3 DOF model by neglecting sprung mass roll motion. A reference state variable is yaw rate, and is computed using the linear 2 DOF model. J-turn and lane change maneuver simulation are performed to show the effectiveness of the developed control scheme. The simulation results show that the 4WS vehicle with the developed control scheme has much better performance in yaw rate, lateral acceleration, roll angle, and sideslip angle than the 2WS vehicle. Also, the results show that the performance of the developed control is close to the one of an optimal control which assumes all states are perfect.

• Journal of Drive and Control
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• v.14 no.2
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• pp.16-22
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• 2017

This paper presents the algorithm of an adaptive model-free-control-based steering control for multi-axle all-terrain cranes for which the recursive least squares with forgetting are applied. To optimally control the actual system in the real world, the linear or nonlinear mathematical model of the system should be given for the determination of the optimal control inputs; however, it is difficult to derive the mathematical model due to the actual system's complexity and nonlinearity. To address this problem, the proposed adaptive model-free controller is used to control the steering angle of a multi-axle crane. The proposed model-free control algorithm uses only the input and output signals of the system to determine the optimal inputs. The recursive least-squares algorithm identifies first-order systems. The uncertainty between the identified system and the actual system was estimated based on the disturbance observer. The proposed control algorithm was used for the steering control of a multi-axle crane, where only the steering input and the desired yaw rate were employed, to track the reference path. The controller and performance evaluations were constructed and conducted in the Matlab/Simulink environment. The evaluation results show that the proposed adaptive model-free-control-based steering-control algorithm produces a sound path-tracking performance.

• 연구논문집
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• s.23
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• pp.81-92
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• 1993

In this study, quarter vehicle model is used to analyse vibration control effects for ride comfort and handling safety according to this three kinds of control methods, which are the modal control, the sky-hook control and the linear viscous damping control. We performed theoretical analysis and experiments and compared two results. In experiments, electro-magnetic actuator was employed as a force actuator. It is shown that all three methods can effectively control the vehicle model. The modal control method gives similar control results using gain less than the viscous damping control.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1133-1145
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• 2000

In this paper a model following flight control system design using the discrete time quasi-sliding mode control method is described. The quasi-sliding mode is represented as the sliding mode band, not as the sliding surface. The quasi-sliding mode control is composed of the equivalent control for the nominal system without uncertainties and disturbances and the additive control compensating the uncertainties and disturbances. The linearized plant on the equilibrium point is used in designing a flight control system and the stability conditions are proposed for the model uncertainties. Pseudo-state feedback control which uses the model variables for the unmeasured states is proposed. The proposed method is applied to the design of the roll attitude and pitch load factor control of a bank-to-turn missile. The performance is verified through the nonlinear six degrees of freedom flight simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.387-387
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• 2000

In this paper, we present a model reference sliding mode control for the system with input/output disturbance. The proposed model reference sliding mode control makes always the error remain on the surface in finite time. Therefore the system is insensitive to external disturbance. Simulation results are included to illustrate the effectiveness of proposed scheme.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.261-262
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• 2007

This paper proposed a model algorithm control (MAC) method for trajectory tracking control of the differentially steeredwheeled mobile robots (WMRs) subject to nonholonomic constraint. The dynamic model of the wheeled mobile robot is presented and used as the model to be controlled. The performance of the proposed control algorithm is verified via computer simulations in which the WMR is controlled to track several different reference paths. It is shown that the control strategy is feasible.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.30
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• pp.107-116
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• 1994

This paper formulates a dynamic monitoring and control model with a machine state by quality variations in a single lot production system. A monitoring model is based on estimate of machine state obtained using control theory. The model studied in this paper has a great advance from a point of view the combination between quality control (Sampling, Control Chart) and automatic control theory, and can be extended in a several ways.