• Journal of Power Electronics
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• v.17 no.2
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• pp.442-452
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• 2017

This paper deals with the input-output modeling of a vector controlled PMSM drive system and design of a simple multiple model adaptive control (MMAC) scheme with desired transient responses. We present a discrete-time modeling technique using closed-loop identification that can experimentally identify the equivalent models in the d-q coordinates. A bank of linear models for the equivalent plant of the current loop is first obtained by identifying them at several operating points of the current to account for nonlinearity. Based on these models, we suggest a simple q-axis MMAC combined with a fixed d-axis controller. After the current controller is designed, another equivalent model including the current controller in the speed control loop shall be similarly obtained, and then a fixed speed controller is synthesized. The proposed approach is demonstrated by experiments. The experimental set up consists of a surface mounted PMSM (5 KW, 220V, 8 poles) equipped with a flywheel load of 220kg and a digital controller using DSP (TMS320F28335).

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.1
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• pp.62-66
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the learning control field was learning in robots doing repetitive tasks such as an assembly line works. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown for the iterative precision of each link.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.4
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• pp.92-98
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• 2005

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized teaming control based on adaptive control method. The original motivation of the teaming control field was loaming in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1080-1084
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• 2014

This paper addresses a design technique for formation tracking controllers of discrete-time Takagi-Sugeno fuzzy multi-agent systems based on consensus. The interconnection topology among the agents is expressed as a digraph. The design condition is represented in terms of linear matrix inequalities. Numerical example demonstrates the effectiveness of the proposed method.

• Journal of Industrial Technology
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• v.10
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• pp.15-20
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• 1990

A parallel processing algorithm for discrete Kalman filter, which is one of the most commonly used filtering technique in modern control, signal processing, and communication, is proposed. Previously proposed parallel algorithms to decrease the number of computations needed in the Kalman filter are the hierachical structures by distributed processing of measurements, or the systolic structures to disperse the computational burden. In this paper, a new parallel Kalman filter employing a structure similar to recursive doubling is proposed. Estimated values of state variables by the new algorithm converge with two times faster data processing speed than that of the conventional Kalman filter. Moreover it maintains the optimality of the conventional Kalman filter.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.60-63
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• 1990

This note presents a direct adaptive pole assignment control for general discrete, linear, time-invariant, nonmimum phase system.Controller parameters are estimated from the recursive least-squares algorithm, and some additional auxiliary parameters are obtained from aset of recursive equations based on a certain polynomial identity which is derived from the pole assignment equation and the Bezout identity. This scheme increase the numerical stability of the auxiliary parameters, and guarantees local convergence without any extra conditions for the external input. The effectiveness of the proposed scheme is demonstrated by the computer simulation.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.64-67
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• 1990

A parallel processing algorithm for discrete Kalman filter, which is one of the most commonly used filtering technique in modern control, signal processing, and communication. is proposed. Previously proposed parallel algorithms to decrease the number of computations needed in the Kalman filter are the hierachical structures by distributed processing of measurements, or the systolic structures to disperse the computational burden. In this paper, a new parallel Kalman filter employing a structure similar to recursive doubling is proposed. Estimated values of state variables by the new algorithm converge with two times faster data processing speed than that of the conventional Kalman filter. Moreover it maintains the optimality of the conventional Kalman filter.

• Proceedings of the KIEE Conference
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• summer
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• pp.1220-1222
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• 2004

In this paper, we propose a new discrete-time predictive current controller for a PM-LSM(permanent magnet linear synchronous motor). The main objectives of the current controllers are to ensure that the measured stator currents tract the command values accurately and to shorten the transient interval as much as possible, in order to obtain high-performance of ac drive system. A new control strategy is seen the scheme that gets the fast adaptation of transient current change, the fast transient response tracking and is proposed simplified calculation. Moreover, the simulation results will be verified the improvements of predictive controller and accuracy of the current controller.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.607-612
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• 2020

In this paper, we deal with the stability of linear discrete systems with time-varying delays and unstructured uncertainty. Stability conditions are derived based on Lyapunov stability theory, and can include the effect of uncertainty. The unstructured uncertainty in the papaer which can not be figured out its exact characteristics and only can be expreesed by its magnitude is considered. Compared with the previous results on the stability, the new results can expand the applicable systems and alleviate the stability conditions which are more effective and powerful. The proposed stability condition is expressed in the form of an simple inequality, and includes the both effects of the uncertainties and time-varying delay. We present the results comparing the new stability condition with the existing results, and verify the effectiveness and the superiority of the proposed results through numerical example.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.807-810
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• 2015

In this paper, a finite impulse response(FIR) fixed-lag smoother is proposed for discrete-time nonlinear systems. If the actual state trajectory is sufficiently close to the nominal state trajectory, the nonlinear system model can be divided into two parts: The error-state model and the nominal model. The error state can be estimated by adapting the optimal time-varying FIR smoother to the error-state model, and the nominal state can be obtained directly from the nominal trajectory model. Moreover, in order to obtain more robust estimates, the linearization errors are considered as a linear function of the estimation errors. Since the proposed estimator has an FIR structure, the proposed smoother can be expected to have better estimation performance than the IIR-structured estimators in terms of robustness and fast convergence. Additionally the proposed method can give a more general solution than the optimal FIR filtering approach, since the optimal FIR smoother is reduced to the optimal FIR filter by setting the fixed-lag size as zero. To illustrate the performance of the proposed method, simulation results are presented by comparing the method with an optimal FIR filtering approach and linearized Kalman filter.