• 한국공작기계학회논문집
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• 제13권3호
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• pp.126-132
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• 2004

This paper proposed trajectory tracking control based on genetic algorithm. Trajectory tracking control scheme are real coding genetic algorithm(RCGA) and back-propagation algorithm(BPA). Control scheme ability experience proposed simulation. Stable tracking control problem of mobile robots have been studied in recent years. These studies have guaranteed stability of controller, but the performance of transient state has not been guaranteed. In some situations, constant gain controller shows overshoots and oscillations. So we introduce better control scheme using real coding genetic algorithm and neural network. Using RCGA, we can find proper gains in several situations and these gains are generalized by neural network. The generalization power of neural network will give proper gain in untrained situation. Performance of proposed controller will verity numerical simulations and the results show better performance than constant gain controller.

• 오토저널
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• 제13권5호
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• pp.73-80
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• 1991

Air/fuel ratio control system for gasoline engines has been analyzed to determine the control gain of the system. In this analysis the engine is modelled to be a simple time delaying element and the ramp-and-jump method is used to control air/fuel ratio. The result shows that it is necessary to measure the air flow rate accurately to enhance the control performance. And also it is shown that the control gain must be determined in some bounded region to meet the fast dynamic response and high catalyst conversion efficiency together.

• 제어로봇시스템학회논문지
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• 제7권7호
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• pp.567-573
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• 2001

A real-time HVAC system is proposed which implements real-time control of thermal environment for virtual reality. It consists of a pair of hot and cold loops that serve as thermal reservoirs, and a mixing box to mix hot and cold air streams flowing if from loops. Their flow rates are controlled in real-time to meet a set temperature and flow rate. A cascade control algorithm along with gain scheduling is applied to the system and test results shows that the closed-loop response approached set values within 3 to 4 seconds.

• 제어로봇시스템학회논문지
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• 제12권11호
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• pp.1061-1064
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• 2006

In this study, the performance of the tracking control of a pneumatic servo motor driven position control system using sliding mode is investigated. It is usually quite difficult to obtain precise tracking control of a pneumatic servo motor driven position control system because of the nonlinear deadband and stick-slip friction of the proportional valve. Therefore, a continuous sliding mode controller with velocity feedforward gain is proposed. Experimental results show that the tracking accurracy can be remarkably improved by adding a proper velocity feedforward term to continuous sliding mode controller.

• International journal of advanced smart convergence
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• 제10권2호
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• pp.175-186
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• 2021

We have studied the speed regulation of the permanent magnet synchronous motor (PMSM) servo system in this paper. To optimize the PMSM servo system's speed-control performance with disturbances, a non-linear speed-control technique using a back-propagation neural network (BP-NN) algorithm forthe controller design of the PMSM speed loop is introduced. To solve the slow convergence speed and easy to fall into the local minimum problem of BP-NN, we develope an improved BP-NN control algorithm by limiting the range of neural network outputs of the proportional coefficient Kp, integral coefficient Ki of the controller, and add adaptive gain factor β, that is the internal gain correction ratio. Compared with the conventional PI control method, our improved BP-NN control algorithm makes the settling time faster without static error, overshoot or oscillation. Simulation comparisons have been made for our improved BP-NN control method and the conventional PI control method to verify the proposed method's effectiveness.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2004년도 봄 학술발표회 논문집
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• pp.431-438
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• 2004

This study is on control-gain estimation of energy dissipation control algorithms. Velocity feedback, bang-bang, and energy dissipation control algorithms are proposed based on the Lyapunov stability theory and their performances are evaluated and compared. Saturation problem is considered in the design of the velocity feedback and energy dissipation control algorithms, and chattering problem in bang-bang control is solved by using boundary layer. Numerical results show that the proposed control algorithms can dissipate the structural energy induced by wind loads efficiently, and thus provide good control performance.

• International Journal of Aeronautical and Space Sciences
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• 제17권2호
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• pp.195-203
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• 2016

In order to overcome the influence of system stability and accuracy caused by uncertainty, estimation errors and external disturbances in Eight-Rotor MAV, L2 gain control method was proposed based on interval type II fuzzy neural network identification here. In this control strategy, interval type II fuzzy neural network is used to estimate the uncertainty and non-linearity factor of the dynamic system, the adaptive variable structure controller is applied to compensate the estimation errors of interval type II fuzzy neural network, and at last, L2 gain control method is employed to suppress the effect produced by external disturbance on system, which is expected to possess robustness for the uncertainty and non-linearity. Finally, the validity of the L2 gain control method based on interval type II fuzzy neural network identifier applied to the Eight-Rotor MAV attitude system has been verified by three prototy experiments.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.192-194
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• 1996

This paper propose an optimal scaling gain tuning method of the fuzzy PI controller using Genetic Algorithm(GA). Scaling gains can reflect the control resolution and fuzziness of input/output variables. By the scaling gain method, the design of a fuzzy logic controller(FLC) can be simplified without affecting the system performance in comparison with multi-decision table method. In designing a fuzzy logic controller, the analytic approach method for the optimization is unavailable. Therefore GA is excellent optimization algorithms for scaling gain tuning. Using this optimal scaling gain tuning method, a good performance can be achieved both in transient and steady state.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2345-2348
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• 2003

This paper deals with a problem of automatic landing guidance and control system design. The auto-landing control system for the longitudinal motion is designed in the classical PID controller. The controller gains are properly adapted to variation of the performance using fuzzy logic as a gain scheduler for the PID gains. This control logic is applied to the problem of the automatic landing control system design. From the numerical simulation using the 6DOF nonlinear model of the associated airplane, it is shown that the auto-landing maneuver is successfully achieved from the start of the flight conditions: 1500 ft altitude, 250 ft/sec airspeed and zero flight path angle.

• 동력기계공학회지
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• 제7권4호
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• pp.38-43
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• 2003

This paper proposes an optimum design problem of structural and control systems. taking a 3-D truss structure as an example. The structure is supposed to be subjected to initial static loads and time-varying disturbances. The structure is controlled by a state feedback $H_{\infty}$ controller to suppress the effect of the disturbances. The design variables are the cross sectional areas of truss members. The structural objective function is the structural weight. As the control objective, we consider two types of performance indices. The first function represents the effect of the initial loads. The second one is the norm of the feedback gain. These objective functions are in conflict with each other. Then, first, two control objective functions are transformed into one control objective by the weighting method. Next, the structural objective is treated as the constraint. By introducing the second control objective which considers the magnitude of the feedback gain, we can per limn the design which is robust in modeling errors.