• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.39 no.4
• /
• pp.6-15
• /
• 2002

A neural network disturbance observer for a robotic manipulator is derived in this paper. The neural network used as the disturbance observer is a feedforward MLP(multiple-layered perceptron) network. The uniform ultimate boundness(UUB) of the proposed neural disturbance observer and the control error within a sufficiently small compact set is guaranteed. This neural disturbance observer method overcomes the disadvantages of the existing adaptive control methods which require the tedious analysis of the regressor matrix of the given manipulator. The effectiveness of the proposed neural disturbance observer is demonstrated by the application to the three-link robotic manipulator.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.3
• /
• pp.286-293
• /
• 1999

A new control scheme using a torsional torque estimator based on a neural network is proposed and investigated for improving control characteristics of the high-performance motion control system. This control method presents better performance in the corresponding speed vibration response, compared with the disturbance observer-based control method. This result comes from the fact that the proposed neural network estimator keeps the self-learning capability, whereas the disturbance observer-based torque estimator with low pass filter should dbjust the time constant of the adopted filter according to the natural resonance frequency detemined by considering the system parameters varied. The simulation results shows the validity of the proposed control scheme.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.51 no.10
• /
• pp.573-580
• /
• 2002

This paper presents neural load disturbance observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is proposed. The parameter compensator with RLSM(recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller. The proposed estimator is combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation and experiment, are shown in this paper.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.53 no.3
• /
• pp.188-195
• /
• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.327-329
• /
• 2006

A controller is proposed for the robust adaptive backstepping control of a class of uncertain nonlinear systems using nonlinear disturbance observer (NDO). The NDO is applied to estimate the time-varying lumped disturbance in each step, but a disturbance observer error does not converge to zero since the derivative of lumped disturbance is not zero. Then the fuzzy neural network (FNN) is presented to estimate the disturbance observer error such that the outputs of the system are proved to converge to a small neighborhood of the desired trajectory. The proposed control scheme guarantees that all the signals in the closed-loop are semiglobally uniformly ultimately bounded on the basis of the Lyapunov theorem. Simulation results are presented to illustrate the effectiveness and the applicability of the approaches proposed.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.15 no.5
• /
• pp.401-410
• /
• 2010

This paper proposes an automatic door system which improves the safety of conventional automatic door systems by complementing the external safety sensors. Disturbance observer using the model of automatic door system and neural network is designed. The proposed algorithm compares the observed disturbance with the output of neural network. Experimental results are presented to illustrate the feasibility of the proposed control strategy. The proposed strategy is expected to improve the safety of an automatic door system.

• Proceedings of the KIPE Conference
• /
• 2001.07a
• /
• pp.389-392
• /
• 2001

This paper presents neural load disturbance observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is proposed. The parameter compensator with RLSM (recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller. The proposed estimator is combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• Proceedings of the IEEK Conference
• /
• 2000.06e
• /
• pp.27-30
• /
• 2000

In this paper, we propose a new approach to determination of environment forces acting on a rigid body. To estimate the output of disturbance observer due to internal torque, the disturbance observer output estimator using functional link neural network (FLANN) is designed. It is also shown by simulation results that the precise estimation of contact force is achieved for a 2-link SCARA robot performing position/force control.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.361-361
• /
• 2000

In the 2-mass system with flexible shaft, a torsional vibration is often generated because of the elastic elements in torque transmission as the newly required speed response which is very close to the primary resonant frequency. This vibration makes it difficult to achieve quick responses of speed and disturbance rejection. In this paper, 2-mass system is designed by using pole placement based on optimal control theory fur fast speed response and torsional vibration elimination and using neural network for disturbance rejection in particular. The simulation results show that the proposed controller based on neural network and full state feedback controller has better performance than 려ll state feedback controller, especially fur disturbance rejection.

• Proceedings of the KIEE Conference
• /
• 2003.07d
• /
• pp.2046-2048
• /
• 2003

In this paper, a neural disturbance observer(NDO) is developed and its application to the control of a nonlinear system with the internal and/or external disturbances is presented. To construct the NDO, a parameter tuning method is proposed and shown to be useful in adjusting the parameters of the NDO. The tuning method employes the disturbance observation error to guarantee that the NDO monitors unknown disturbances. Each of the nodes of the hidden layer in the NDO network is a radial basis function(RBF). In addition, the relationships between the suggested NDO-based control and the conventional adaptive controls reported in the previous literatures are discussed. And it is shown in a rigorous manner that the disturbance observation error converges to a region of which size can be kept arbitrarily small. Finally, an example and some computer simulation results are presented to illustrate the effectiveness and the applicability of the NDO.