• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 1999.05a
• /
• pp.348-354
• /
• 1999

In recent years, a PID controller has been used as a major control method in real control processes. This controller requires a determination of PID control gains. But it is difficult to select the best gains theoretically. Thus there have been many approaches to determine them empirically Most of them are based on experience and knowledge. In this paper, we proposed a tuning method of the PID Parameters by using neural network. To show effectiveness of the proposed method, the simulation of DC motor and one link manipulator position control is carried out.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.550-552
• /
• 1999

This paper describes a control technique of coupled tank level using Evolutionary Neural Network. In general, the control of tank level without a dangerous overflow and with a high accuracy is difficult because of higher order time delay and nonlinearity. Nonetheless, proposed Evolution Neural Network controller in this paper was successfully implemented and simulation results of the superiority over a conventional PID one was investigated.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.7 s.172
• /
• pp.94-101
• /
• 2005

This work proposes a new method for describing the hysteresis non-linearity of a piezoelectric actuator. The hysteresis behaviour of piezoelectric actuators, including the minor loop trajectory, are modeled by geometrical relationship between a reference major loop and its minor loops. This hysteresis model is transformed into inverse hysteresis model in order to output compensated voltage with regard to the given input displacement. A feedforward neural network, which is trained by a feedback PID control module, is incorporated to the inverse hysteresis model to compensate unknown dynamics of the piezoelectric system. To show the feasibility of the proposed feedforward-feedback controller, some experiments have been carried out and the tracking performance was compared to that of simple PTD controller.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.798-800
• /
• 1998

In recent years, advances in construction techniques and materials have given rise to flexible light-weight structures. Because these structures extremely susceptib environmental loads, these random loadings u produce large deflection and acceleration on structures. Vibration control system of structur becoming an integral part of the structural syst the next generation of tall building. The proposed control system is applied to s degree of structure with mass damping and com with conventional PID and neural network PID system.

• Proceedings of the KIEE Conference
• /
• 1997.07c
• /
• pp.1021-1024
• /
• 1997

This paper has presented a method for self-tuning tile PID controller using a BP method of multilayered NNs. The proposed controller employ input signal as a learning signal of PID control. The proposed controller is applied to load-frequency control of power system and it is investigated a dynamic characteristic. The simulation results shows that proposed NN STPID controller has the good dynamics responses against load disturbances.

• Proceedings of the KIPE Conference
• /
• 2005.07a
• /
• pp.333-335
• /
• 2005

In this paper, supposed to solve these problem to PID parameters controller algorithm using ANN. In the proposed algorithm, the parameters of the controller were adjusted to reduce by on-line system the error of the speed of IM. In this process, EBPANN was constituted to an output error value of an IM and conspired an input and output. The performance of the self-tuning controller is compared with that of the PID controller tuned by conventional method (Ziehler-Nichols). The effectiveness of the proposed control method is verified thought the Matlab Simulink and experimental results.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.12 no.1
• /
• pp.78-84
• /
• 1998

The fluid system such as, the quantity control of raw water, chemicals control in the purification, the waste water system as well as in the feed water or circulation system of the power plant and the ventilation system is controlled with the valve and moter pump. The system's performance and the energy saving of the fluid systems depend on control of method and delicacy. Until, PI controller use in these system but it cannot control delicately because of the coupling in the system loop. In this paper we configure a single flow system to the multi variable system and suggest the application of 2-DOF PID controller and the tuning methods by the neural network to the electrical power of the flow control system. the 2-DOF controller follows to a setpoint has a robustness against the disturbance in the results of simulation. Keywords Title, Intelligent control, Neuro control, Flow control, 2 - DOF control., 2 - DOF control.

• Proceedings of the KIEE Conference
• /
• 2005.05a
• /
• pp.109-112
• /
• 2005

This paper presents an optimal and robust PI-PD controller design method for the second-order systems both with dead time and without dead time to satisfy the design specifications in the time domain via LQR design technique. The optimal tuning method of PI-PD controller are also developed by setpoint weighting and neural networks. It is shown that the simulation results show significantly improved performance by proposed method.

• Proceedings of the KIEE Conference
• /
• 2004.07d
• /
• pp.2501-2503
• /
• 2004

In this paper, we propose an auto-tuning control algorithm for a mobile robot. This controller consists of a three layer neural networks and a PID controller. In order to compensate for uncertainties from unknown dynamics and ignored dynamic effects such as slip conditions, neural network based position schemes are proposed. The results of simulations show the validity of proposed method. This controller learns quickly the model and has good position control performance.

• Journal of Aerospace System Engineering
• /
• v.11 no.1
• /
• pp.14-21
• /
• 2017

A Linear parameterized Sigma-Pi neural network (SPNN) is applied to a tilt-duct unmanned aerial vehicle (UAV) which has a very large longitudinal stability ($C_{L{\alpha}}$). It is uncontrollable by a proportional, integral, derivative (PID) controller due to heavy stability. It is shown that the combined inner loop and outer loop of SPNN controllers could overcome the sluggish longitudinal dynamics using a method of dynamic inversion and pseudo-control to compensate for reference model error. The simulation results of the way point guidance are presented to evaluate the performance of SPNN in comparison to a PID controller.