• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.11
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• pp.990-998
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• 2007

Adaptive output feedback control technique using Neural Networks(NN) is proposed for uncertain nonlinear Multi-Input Multi-Output(MIMO) systems. Modified Dynamic Inversion Model(MDIM) is introduced to decouple uncertain nonlinearities from inversion-based control input. MDIM consists of approximated dynamic inversion model and inversion model error. One NN is applied to compensate the MDIM of the system. The output of the NN augments the tracking controller which is based upon a filtered error approximation with online weight adaptation laws which are derived from Lyapunov's direct method to guarantee tracking performance and ultimate boundedness. Several numerical results are illustrated in the simulation of Van der Pol system and unmanned helicopter with model uncertainties.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.173-176
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• 2003

In this paper, a DLC method is suggested for linear feedback systems to improve the tracking performance when the task of the system is repetitive. DLC can generate the desired control input directly from the previously teamed control inputs corresponding to other output trajectories. It is assumed that all outputs considered in this paper have some relations called "proportionality. " To show the validity and tracking performance of the proposed method, some simulation are performed for the tracking control of a linear system with a PI controller.

• Journal of Power Electronics
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• v.11 no.5
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• pp.719-725
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• 2011

This paper describes a novel Direct Torque Control (DTC) method for adjustable speed Doubly-Fed Induction Machine (DFIM) drives which is supplied by a two-level Space Vector Modulation (SVM) voltage source inverter (DTC-SVM) in the rotor circuit. The inverter reference voltage vector is obtained by using input-output feedback linearization control and a DFIM model in the stator a-b axes reference frame with stator currents and rotor fluxes as state variables. Moreover, to make this nonlinear controller stable and robust to most varying electrical parameter uncertainties, a two layer recurrent Artificial Neural Network (ANN) is used to estimate a certain function which shows the machine lumped uncertainty. The overall system stability is proved by the Lyapunov theorem. It is shown that the torque and flux tracking errors as well as the updated weights of the ANN are uniformly ultimately bounded. Finally, effectiveness of the proposed control approach is shown by computer simulation results.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.236-248
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• 2017

A direct stator flux vector control scheme in discrete-time domain is proposed in this paper for the interior permanent magnet synchronous motor (IPMSM) drive to remove the proportional-integral (PI) controller from the direct torque control (DTC) scheme applied to IPMSM and to obtain faster dynamic response and lower torque ripple output. The output of speed outer loop is used as the desired torque angle instead of the desired torque in the proposed scheme. The desired stator flux vector in dq coordinate is calculated with a given amplitude. The state-space equations in discrete-time for IPMSM are established, the actual stator flux vector is estimated in deadbeat manner by a full-order state observer, and then the closed-loop control is achieved by the pole placement. The stator flux error vector is utilized to calculate the reference stator voltage vector. Extracting the angle position and amplitude from the estimated stator flux vector and estimating the output torque are eliminated for the direct feedback control of the stator flux vector. The proposed scheme is comparatively investigated with a PI-SVM DTC scheme by experiment results. Experimental results show the feasibility and advantages of the proposed control scheme.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.346-349
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• 1995

In this paperwe develop a real-time simulator for direct drive cooperative robot by using OpenGL in a Windows NT based system. This simulator is composed of 2 parts, a display part and an interface part. In the display part the robot is modelled and rendered in 3D space. To do this OpenGL, a kind of graphic library, is used for rendering and animating robots and kinematics gives the information of the current robot configuration. The control and the feedback data are sent and received via the interface part. In real time simulation interfacing part needs fast data transfer rate and good nosic immunity. In experiment we have simulated 2-link direct drive cooperative robots using the trajectory tracking algorithm proposed in reference.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.325-327
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• 2005

In this paper, we propose the design of active queue management (AQM) control system using the self-recurrent wavelet neural network (SRWNN). By regulating the queue length close to reference value, AQM can control the congestions in TCP network. The SRWNN is designed to perform as a feedback controller for TCP dynamics. The parameters of network are tunes to minimize the difference between the queue length of TCP dynamic model and the output of SRWNN using gradient-descent method. We evaluate the performances of the proposed AQM approach through computer simulations.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1140-1146
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• 1999

In this paper, a force estimation method is proposed for force control without force sensor. For this , a disturbance observer is applied to each joint of an {{{{ { n}_{ } }}}} degrees of freedom manipulator to obtain a simple equivalent robot dynamics(SERD) being represented as an n independent double integrator system. To estimate the output of disturbance observer due to internal torque, the disturbance observer output estimator(DOOE) is designed, where uncertain parameters of the robot manipulator are adjusted by the gradient method to minimize the performance index which is defined as the quadratic form of the error signal between the output of disturbance observer and that of DOOE. when the external force is exerted, the external force is estimated by the difference between the output of disturbance observer and DOOE, since output of disturbance observer includes the external torque signal as well as the internal torque estimated by the output of DOOE. And then, a force controller is designed for force feedback control employing the estimated force signal. To verify the effectiveness of the proposed force estimation method, several numerical examples and experimental results are illustrated for the 2-axis direct drive robot manipulator.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.439-442
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• 2000

An adaptive nonlinear control of a brushless direct drive motor(BLDDM) is proposed. Comparing to the traditional PMSM the direct drive motor has smaller number of per pole and per phase slots to provide higher torque in low speed. This generic construction generates flux harmonics and finally results in unwanted torque harmonics. To control the speed a feedback linearization method is applied by choosing the $i_{ds}$ and $\omega_{m}$ as the output variables. The control of the flux harmonics is provided by using a flux observer with MRAC technique. As shown in the simula-tion results the proposed nonlinear speed controller has a good speed response in the steady state and robust to the flux variation

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.7
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• pp.56-65
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• 1997

In this paper, we propose a reference input odification-type iterative learning control law for a class of discrete-time nonlinear systems and prove the convergence of the output error. We can get the high-precision in case of the trajectroy control when the proposed control law is properly combined with a feedback controller, and we can easily implement the learning control law compared to the control input modification-type learning control law. To show the validity and the convergence perfodrmance of the proposed control law, we perform experimentations on the trajectroy control and rejection of periodic disturbance for a 2-axis SCARA-type direct drive robot.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.3
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• pp.228-235
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• 2010

This paper deals with an autonomous flight controller design problem for a tilt-rotor aircraft in rotary-wing mode. The inner-loop algorithm is designed using the output-based approximate feedback linearization. The model error originated from the feedback linearization is cancelled within allowable tolerance by using single-hidden-layer neural network. According to Lyapunov direct stability theory, the adaptive update law is derived to run the neural network on-line, which is based on the linear observer dynamics. Moreover, the outer-loop algorithm is designed to track the trajectory generated from way-point guidance. Especially, heading and flight-path angle line-of-sight guidance are applied to the outer-loop to improve accuracy of the landing tracking performance. The 6-DOF nonlinear simulation shows that the overall performance of the flight control algorithm is satisfactory even though the collective input response shows instantaneous actuator saturation for a short time due to the lack of the neural network and the saturation protection logic in that loop.