• Proceedings of the KIEE Conference
• /
• 1993.07a
• /
• pp.224-226
• /
• 1993

Multi-layered neural networks offer an exciting alternative for modelling complex non-liner systems. This paper investigates the identification of continuous time nonliner system using neural networks with a single hidden layer. The digital low - pass filter are introduced to avoid direct approximation of system derivatives from sampled data. Using a pre-designed digital low pass filter, an approximated discrete-time estimation model is constructed easily. A continuous approximation liner model is first estimated from sampled input-out signals. Then the modeling error due to the nonlinearity is decreased by a compensator using neural network. Simulation results are given to demonstrate the effective of the proposed method.

• 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 Korean Society of Precision Engineering Conference
• /
• 2002.05a
• /
• pp.424-427
• /
• 2002

Pneumatic control systems have the potential to provide high output power to weight and size ratios at a relatively low cost. However, they are mainly employed in open-loop control applications where positioning repeatability is not of great importance. This paper presents precision positioning control of pneumatic servo cylinder with on-off valve, Pneumatic low-friction cylinder with servo valve and DC servo motor under parameter variations. Basically positioning control uses PID controller, where needs a linearized model. A neural network is added to a PID controller to compensator nonlinearity of the system and an influence of friction force is consider as disturbance. The performances of the proposed algorithms were compared by experiments with them of PID controller. From those experiments is was shown that the proposed algorithms are more efficient about settling time, steady 7tate error and overshoot than PID control algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2004.10a
• /
• pp.505-507
• /
• 2004

This paper attempts to compensate the nonlinearity between the input voltage and the output displacement of the piezoelectric stack in dynamic actuation by the following two ways. Firstly, the charge steering by circuit configuration reduces the hysteresis of piezoelectric actuator remarkably. However, it makes the ripple in positioning due to the phase lag and noise induced from the elements of the long closed loop. Secondly, the feedforward control by neural network compensates the hysteresis of the piezoelectric actuators effectively with the appropriate selection of the input variables for the training. The improvement of the dynamic performance of the piezoelectric actuators by the developed linearization technique is verified by experiments.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.50.5-50
• /
• 2002

In this paper, we propose a method using neural network (NN) to improve the motion control of a decentralized control system for cooperative transportation. In our former work, a decentralized control system for transporting a single object by multiple nonholonomic mobile robots has been developed. One of these mobile robots acts as a leader, who is assumed to be able to plan and to manipulate the omnidirectional motion of the object. Other robots, referred to as followers, cooperatively transport the object by keeping a constant position relative to the object. in this work, it is assumed that the leader can not only plan but also broadcast the local velocity of the object. Then...

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.1759-1760
• /
• 2008

This paper proposes a neural network(NN)-based adaptive sliding mode controller for discrete-time nonlinear systems. By using disturbance estimation technique, a sliding mode controller is designed, which forces the sliding variable to be zero. Then, NN compensator with hidden-layer-to-output-layer weight update rule is combined with sliding mode controller in order to reduce the error of the estimates of both disturbances and nonlinear functions. The whole closed loop system rejects disturbances excellently and is proved to be ultimately uniformly bounded(UUB) provided that certain conditions for design parameters are satisfied.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 1999.11a
• /
• pp.256-261
• /
• 1999

Many control techniques have been proposed in order to improve the control performance of discrete-time domain control system. In the position control system using a DC servo motor as control system, the response-characteristic of system is controlled by the I-PD controller. In the I-PD longer if gains of I-PD controller are unsuitable. In this paper, therefore, a expanded I-PD control system is constructed by inserting a pre-compensator at out terminal of I-PD controller. It is implemented by neural network with two hidden layers. From the result of computer simulation in the proposed control algorithm, its usefulness is verified.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2473-2475
• /
• 2001

In trajectory tracking methods, the error values of current position and velocity are compensated to follow the given reference path and velocity. The path tracking for a wheeled mobile robot is treated in this paper. It is very difficult to implement stable trajectory tracking algorithms because mobile robots have kinematically non-holonomic constraints. For solving this problem, a velocity controller is presented in this paper. This velocity controller is designed by a PID controller which could be easily employed. In this case, velocity errors caused by system uncertainties or internal and external disturbances could exist. A neural network is used for compensating the velocity errors. Input variables of this neural network compensator are defined by differences between the velocities of the posture controller and the real velocities of the mobile robot. Simulation results show the effectiveness of the proposed controller.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2000.08a
• /
• pp.105-108
• /
• 2000

Many control techniques have been proposed in order to improve the control performance of discrete-time domain control system. In the position control system using a DC servo motor as a driver, the response-characteristic of system is controlled by the PID controller. In the PID control system, the transient response characteristic is more increased and settling time gets longer if gains of PID controller are unsuitable. In this paper, therefore, a expended PID control system is constructed by inserting a pre-compensator at output terminal of PID controller. It is implemented by using the double layers neural network. Form the results of computer simulation in the proposed control algorithm, its usefulness is verified.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.7
• /
• pp.21-31
• /
• 1996

An intelligent integrated control system is designed for the active steering and the left/right traction force distribution control of electric vehicles, where input-output linearization is employed. Also, a fuzzy-rule-based cornering force estimator is suggested to avoid using an uncertain highly nonlinear expression, and a neural network compensator is additively utilized for the estimator to correctly find cornering forece. With these techniques, the proposed control system is shown by simulation results to be robust against drastic change of the external environments such as road conditions.