• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.7
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• pp.709-715
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• 2011

Harmonic drives have been widely used in combination with joint torque sensors in order to facilitate accurate manipulator control. A harmonic drive causes a torque ripple because of its structural characteristics, and this torque ripple tends to deteriorate the performance of a controller or observer that uses torque sensors. This paper proposes a switching notch filter for reducing the torque ripple caused by a harmonic drive in a joint torque sensor; the functioning of this filter is based on the relationship between the frequency components of the torque ripple and the rotational velocity of the harmonic drive. The proposed switching notch filter is advantageous in that it requires less computational load and does not necessitate additional circuits or structures. Various experiments demonstrate that the proposed filter has good filtering performance, fast response, and good switching stability.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.5
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• pp.427-436
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• 2002

This paper presents an implementation of high-dynamic performance of position sensorless motion control system of Reluctance Synchronous Motor(RSM) drives for an industrial servo system with direct torque control(DTC). The problems of high-dynamic performance and maximum efficiency RSM drives controlled by DTC are saturation of stator linkage flux and nonlinear inductance characteristics with various load currents. The accurate estimation of the stator flux and torque are obtained using stator flux observer of which a saturated inductance $L_d$ and $L_q$ can be compensated by adapting from measurable the modulus of the stator current and rotor position. To obtain fast torque response and maximum torque/current with varying load current, the reference command flux is ensured by imposing $I_{ds} = I_{qs}$. This control strategy is proposed to achieve fast response and optimal efficiency for RSM drive. In order to prove rightness of the suggested control algorithm, the actual experiment carried out at $\pm$20 and $\pm$1500 rpm. The developed digitally high-performance motion control system shown good response characteristic of control results and high performance features using 1.0kW RSM which has 2.57 Ld/Lq salient ratio.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.4
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• pp.241-247
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• 2014

This paper propose a variable parameter estimations of five-phase squirrel-cage induction motor(IM) for speed control system. In order to high performance control of AC motor using a field oriented control(FOC) and direct torque control(DTC) algorithm, there are required precise motor parameters for slip calculation, flux observer, controller gain, rotor position, speed estimation, and so on. We are suggest a analyzed estimation results of the motor parameters that developing five-phase squirrel-cage IM have a stator of concentrated winding for experimental of variable input power frequency. There are results of stator winding test, no-load test, locked-rotor test, variable actual load test, and estimated parameters of equivalent circuits using manufactured experimental apparatus by IEEE Standard Test Procedure for Polyphase Induction Motors and Generators 112-2004.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.859-866
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• 2022

Active disturbance rejection control is a method in which the disturbance is removed from the controller by estimating the state variable using the Luenberger observer. The Luenberger observer is estimated by defining a nonlinear term including disturbance with constant characteristics in a steady state as a state variable. It can be shown that the speed tracking performance is improved by compensating the estimated state variable to the PI controller and the IP controller. The disturbance removal performance of the tracking control can be confirmed by observing that the estimated state error is within 1.9 [%] in the case of load fluctuation and the steady-state state tracking error converges to zero.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.6
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• pp.1122-1127
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• 2001

This paper proposed a speed adaptive control system with load torque observer and feed-forward compensation using neural network for air compressor system driven an induction motor. The motor receive impact load change under the influence of piston movement of up and down, and so it difficult to obtain good speed control characteristics. With real-time adjusting control gain estimated in neural network, control characteristics of motor is improved. The validity of the proposed system is confirmed through the theoretical analysis and computer simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.397-400
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• 2001

This paper proposed a speed adaptive control system with load torque observer and feed-forward compensation using neural network for air compressor system driven an induction motor. The motor receive impact load change under the influence of piston movement of up and down, and so it difficult to obtain good speed control characteristics. With real-time adjusting control gain estimated in neural network, control characteristics of motor is improved. The validity of the proposed system is confirmed through the theoretical analysis and computer simulation.

• Journal of Power Electronics
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• v.4 no.1
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• pp.1-11
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• 2004

A stable adaptive sensorless speed controller for three-level inverter fed induction motor direct torque control (DTC) system using the radial-basis function network (RBFN) is presented in this paper. Torque ripple in the DTC system for high power induction motor could be drastically reduced with the foregoing researches of switching voltage selection and torque ripple reduction algorithms. However, speed control performance is still influenced by the inherent uncertainty of the system such as parametric uncertainty, external load disturbances and unmodeled dynamics, and its exact mathematical model is much difficult to be obtained due to their strong nonlinearity. In this paper, the inherent uncertainty is approximated on-line by the RBFN, and an additional robust control term is introduced to compensate for the reconstruction error of the RBFN instead of the rich number of rules and additional updated parameters. Control law for stabilizing the system and adaptive laws for updating both of weights in the RBFN and a bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov, and the stability proof of the whole control system is presented. Computer simulations as well as experimental results are presented to show the validity and effectiveness of the proposed system.

• Journal of Power Electronics
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• v.19 no.3
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• pp.771-783
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• 2019

Due to nonlinear-synthetic uncertainty including the total unknown nonlinear load torque, the total parameter variation and the fixed load torque, a synchronous reluctance motor (SynRM) driving a continuously variable transmission (CVT) system causes a lot of nonlinear effects. Linear control methods make it hard to achieve good control performance. To increase the control performance and reduce the influence of nonlinear time-synthetic uncertainty, an admixed recurrent Gegenbauer orthogonal polynomials neural network (ARGOPNN) with a modified particle swarm optimization (MPSO) control system is proposed to achieve better control performance. The ARGOPNN with a MPSO control system is composed of an observer controller, a recurrent Gegenbauer orthogonal polynomial neural network (RGOPNN) controller and a remunerated controller. To insure the stability of the control system, the RGOPNN controller with an adaptive law and the remunerated controller with a reckoned law are derived according to the Lyapunov stability theorem. In addition, the two learning rates of the weights in the RGOPNN are regulating by using the MPSO algorithm to enhance convergence. Finally, three types of experimental results with comparative studies are presented to confirm the usefulness of the proposed ARGOPNN with a MPSO control system.

• Proceedings of the KIEE Conference
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• 1995.07a
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• pp.334-336
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• 1995

An adaptive feedback linearization technique of a PM synchronous motor with specified output dynamic performance is proposed. The adaptive parameter estimation is achieved by a model reference adaptive technique where the stator resistance and flux linkage can be estimated with the current dynamic model and the state observer. Using these estimated parameters, the linearizing control inputs are calculated and a nonlinear coupled model of a PM synchronous motor is input-output linearized. The resultant model has the load torque disturbance. To get ti perfect decoupled model, the load torque is estimated. The adaptation laws are derived by the hyperstability theory and positivity concept. The robustness of the proposed control scheme will be proven through the computer simulations.

• The Transactions of the Korean Institute of Power Electronics
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• v.8 no.4
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• pp.366-374
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• 2003

In electric motor coaches, the rolling stocks move by the adhesive effort between rail and driving wheel. Generally, the adhesive effort is defined by the function of both the weight of electric motor coach and the adhesive effort between rails and driving wheel. The characteristics of adhesive effort is strongly affected by the conditions between rails and driving wheel. When the adhesive effort decreases suddenly, the electric motor coach has slip phenomena. This paper proposes a re-adhesion control algorithm which uses the maximum adhesive effort by instantaneous estimation of adhesion force using load torque disturbance observer. Based on this estimated adhesive effort, the re-adhesion control Is peformed to obtain the maximum transfer of the tractive effort.