• ICROS
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• v.1 no.1
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• pp.16-16
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• 1995

Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.166-173
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• 1999

Reference models are used in many control algorithms for improvement of transient response characteristics. They provide desired trajectories that the plant should follow Most control systems have bounded control inputs to avoid saturation of the plant. If we design the reference models that do not account for limits of the control inputs, control performance of the system may be deteriorated. In this paper a new approach of avoiding saturation by varying the reference model for TDC(time delay control) based systems subject to step changes in the reference input. In this scheme, the variable reference model is determined based on the information on control inputs and the size of the step changes in the reference inputs. This scheme was verified by application to the BLDC motor position control system in simulations and experiments. The responses of the TDC with the variable reference model showed better tracking performance than that with the fixed reference model.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.165-171
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• 2006

This paper proposes a data fusion algorithm of nonlinear multi sensor dynamic systems of synchronous sampling based on filtering step by step. Firstly, the object state variable at the next time index can be predicted by the previous global information with the systems, then the predicted estimation can be updated in turn by use of the extended Kalman filter when all of the observations aiming at the target state variable arrive. Finally a fusion estimation of the object state variable is obtained based on the system global information. Synchronously, we formulate the new algorithm and compare its performances with those of the traditional nonlinear centralized and distributed data fusion algorithms by the indexes that include the computational complexity, data communicational burden, time delay and estimation accuracy, etc.. These compared results indicate that the performance from the new algorithm is superior to the performances from the two traditional nonlinear data fusion algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.116-120
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• 1999

The step response of the Manabe standard form[1］has little overshoot and shows almost same waveforms regardless of the order of the characteristic polynomials. In some situations it is difficult to control the rise time and settling time simultaneously of the step response of the Manabe standard form. To control its rise time and settling time efficiently, we develop the generalization of the Manabe standard form: we try to find out the SRFS(Slow Rise time & Fast Settling time) form which has the slower rise time and faster settling time than those of the Manabe standard form. we also consider the other three forms: FRSS(Fast Rise time & Slow Settling time), SRFS(Slow Rise time ＆ Fast Settling time) and SRSS(Slow Rise time & Slow Settling time) forms. In this paper, by using the genetic algorithm, we obtain all the coefficient of the four forms we mention above. Finally, we design a controller for a given plant so that the overall system has the performance that the rise time is faster, the settling time is slower than those of the Manabe standard form.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.3
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• pp.276-279
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• 2001

Many industrial processes that are operated by rotating machines and large air-moving fans are excellent examples to which the single channel ANC systems can be applied. In these environments, the active noise control techniques are most popular nowadays. In this paper, a modified LMS algorithm(EAC, Error Amplitude Compared) is proposed. The algorithm is a kind of variable step-size LMS-type algorithm. Computer simulations show that the proposed EAC algorithm achieves a better convergence time than a conventional VS(Variable Step-Size) algorithm, Also, this algorithm has been implemented by using and experimental duct system.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.203-206
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• 1998

This paper describes the procedure of getting information about auto-tuning of PID regulator by the injection of high step input, called limited input, during a transient time of control. The key point is that system identification and control could be continuously executed. This means that the system information obtained by limited input despite of system uncertainty can be continuously applied to the PI regulator. Simulation and experiment result of brushless DC motor system having monotone increasing step response demonstrate the usefulness of proposed auto-tuning algorithm.

• Journal of Korean Association for Spatial Structures
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• v.22 no.2
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• pp.39-46
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• 2022

Recently, machine learning is widely used to solve optimization problems in various engineering fields. In this study, machine learning is applied to development of a control algorithm for a smart control device for reduction of seismic responses. For this purpose, Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm. A single degree of freedom (SDOF) structure with a smart tuned mass damper (TMD) was used as an example structure. A smart TMD system was composed of MR (magnetorheological) damper instead of passive damper. Reward design of reinforcement learning mainly affects the control performance of the smart TMD. Various hyper-parameters were investigated to optimize the control performance of DQN-based control algorithm. Usually, decrease of the time step for numerical simulation is desirable to increase the accuracy of simulation results. However, the numerical simulation results presented that decrease of the time step for reward calculation might decrease the control performance of DQN-based control algorithm. Therefore, a proper time step for reward calculation should be selected in a DQN training process.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.97-107
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• 2002

In this paper, we propose the concept of orthonormalized compressed measurement for the stability analysis of discrete linear time-varying Kalman filters. Unlike previous studies that deal with the homogeneous portion of Kalman filters, the proposed Lyapunov method directly deals with the stochastically-driven system. The orthonorrmalized compressed measurement provides information on the a priori state estimate of the Kalman filter at the k-th step that is propagated from the a posteriori state estimate at the previous block of time. Since the complex multiple-step propagations of a candidate Lyapunov function with process and measurement noises can be simplified to a one-step Lyapunov propagation by the orthonormalized compressed measurement, a stochastic radius of attraction can be derived that would be impractically difficult to obtain by the conventional multiple-step Lyapunov method.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1189-1196
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• 2010

This paper provides an identification method for three-parameter models i.e. first order with dead time models and second order with dead time models. The proposed identification method is based on step response and can be easily implemented using digital microprocessors. The proposed method first identifies the order of the plant i.e. first order or second order from the behavior of the plant with constant input. After the order of the plant is determined, a test step input is applied to the system and the three parameters of the plant are obtained from the corresponding response of the plant. The output of the plant need not to be zero when the test signal is applied. The efficacy of proposed algorithms is verified through simulation and experiment.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.903-906
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• 1997

When large capacity generation stations that consist of several large units tend to pull out of step from main power system, stabilizing control scheme as emergency control for preventing loss of synchronism of the whole stations with the remaining system is devided into two steps that the first step is to perform on-line prediction for out-of-step and the next step is on-line calculation of the amount of generation shedding for the rest of generators to be in step when out of step is expected. This paper presents on-line prediction scheme for out-of-step based on P-$\delta$ curve estimation using real-time measurement and on-line calculation of generation shedding. The proposed stabilizing scheme was applied to case study of real power system and the results obtained by the method compare well with the results by simulation.