• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.729-731
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• 2004

This paper deals with the design of a low order $H_{\infty}$ controller by using an iterative linear matrix inequality (LMI) method. The low order $H_{\infty}$ controller is represented in terms of LMIs with a rank condition. To solve the non-convex rank-constrained LMI problem, a linear penalty function is incorporated into the objective function so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Numerical experiments show the effectiveness of the proposed algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.1
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• pp.79-86
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• 2012

This paper proposes PID and RIC (Robust Internal-loop Compensator) based motion controller using dual learning algorithm for position control of linear synchronous motor respectively. Its gains are auto-tuned by using two learning algorithms, reinforcement learning and neural network. The feedback controller gains are tuned by reinforcement learning, and then the feedforward controller gains are tuned by neural network. Experiments prove the validity of dual learning algorithm. The RIC controller has better performance than does the PID-feedforward controller in reducing tracking error and disturbance rejection. Neural network shows its ability to decrease tracking error and to reject disturbance in the stop range of the target position and home.

• Journal of Institute of Control, Robotics and Systems
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• v.1 no.2
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• pp.94-98
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• 1995

This paper proposes an optimal design method for the weight and cost of a moving magnet typer linear DC motor (MM-LDM). The optimal design condition such as type and size of MM-LDM were determined by the trinary search algorithm after adjusting a standard function and its related parameters. In order to verigy results of the optimal design by the computer simulation, the designed values such as a thrust, a current, a velocity, and etc. of the fabricated MM-LDM were measured. And the measurement results are in good agreement with the designed ones.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.435-441
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• 2003

This paper addresses that an approximation and tracking control of realtime recurrent neural networks(RTRN) using two-dimensional iterative teaming algorithm for an electro-hydraulic servo system. Two dimensional learning rule is driven in the discrete system which consists of nonlinear output fuction and linear input. In order to control the trajectory of position, two RTRN with the same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm are able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two identical RTRN was very effective to trajectory tracking of the electro-hydraulic servo system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.3
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• pp.111-116
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• 2000

In this paper, we presented a new algebraic iterative algorithm for the optimal control of the nonlinear systems. The algorithm is based on tow steps. The first step transforms optimal control problem into a sequence of linear optimal control problem using the quasilinearization method. In the second step, TPB(two point boundary condition problem) is solved by algebraic equations instead of differential equations using BPF(block pulse functions). The proposed algorithm is simple and efficient in computation for the optimal control of nonlinear systems. In computer simulation, the algorithm was verified through the optimal control design of Van del pole system and Volterra Predatory-prey system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.1968-1975
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• 2009

The paper proposes a novel control algorithm for tracking maximum power of PV generation system.. The maximum power of PV array is determinated by a insolation and temperature. Prior considered the term in PV generation system is how maximum power point(MPP) is accurately tracked.. The paper proposes a fuzzy neural network(FNN) control algorithm so as to accurately track those maximum power points. The proposed control algorithm comprises the antecedence part of fuzzy rule and clustering method, multi-layer neural network in the consequent part. FNN has the advantages which are depicted both high performance and robustness in fuzzy control and high adaptive control in neural network.. Specially, it can show the outstanding control performance for parameter variations appling to non-linear character of PV array. In this paper, the tracking speed and the accuracy prove the validity through comparing a proposed algorithm with a conventional one.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1387-1392
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• 1990

In this paper, an iterative learning control algorithm for unknown linear discrete systems is proposed by employing a parameter estimator together with an inverse system model. Regardless of initial error and inherent parameter uncertainty, a good tracking control performance is obtained using the proposed learning control algorithm characterized by recursive operations. A sufficient condition for convergency is provided to show the effectiveness of the proposed algorithm. To investigate the performance of the algorithm a series of simulations and experiments were performed for the tracking control of a servo motor.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.598-604
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• 1999

A new fuzzy control algorithm for the control of active magnetic bearing (AMB) systems is proposed in th~sp aper. It combines PDC algorithm based on the LMI design of Joh et al. [4,5] and Mamdani-type control rules using fuzzy singletons to handle the nonlinear characteristics of AMB systems efficiently. They are named fine mode control and coarse mode control, respectively. The coarse mode control yields fast response for large deviation of the rotor and the fine mode control gives desired transient response for small deviation of the rotor. The proposed algorithm is applied to an AMB system to verify the performance of the proposed method. The comparison of the proposed method with a linear controller using a linearized model about the equilibrium point and the PDC algorithm show the superiority of the proposed algorithm.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.453-456
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• 2001

The thrust control characteristics of single-sided linear induction motor (SLIM) is achieved using PI controller in this paper. The trembling of air gap length between the primary winding and the secondary structure of the SLIM must be minimized in order to get quick response results. Also, the input voltage of SLIM is involved with the time harmonics because most SLIM is driven by inverter. According to the feedback linear control system, this paper describes with applying the non-linear control, speed and estimated flux algorithm. At the result of this experiment, we reached to the improvement of thrust characteristics with PI controller.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.6
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• pp.566-571
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• 2010

EPS (Electric Power Steering) is important device for improving vehicle's dynamics and static performances. This paper deals with simulator design for C-EPS (Colum type-EPS), development assist and returnability control algorithm. First, C-EPS system model was simply designed because EPS system is complex control system that has many unknown variables. These parameters were simplified through assumptions. Second, C-EPS simulator was designed for development of control algorithm. This simulator has SAS (Steering Angle Sensor), dual torque sensor, dual load cell for measuring rack force, dual linear actuator for generating tire force and Data Acquisition System. Using this simulator, control methods ware tested. Third, control algorithm was designed for torque assist and returnability. Assist torque map and returnability torque map were found by lots of simulation test. These torque maps were tuned for EPS actuator control. The simulation result was compared with non-EPS system result. In this research, the C-EPS simulator was designed for development of control algorithm about torque assistant and returnability. Using this simulator, control algorithm was improved.