• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.12
• /
• pp.1699-1704
• /
• 2018

In this paper, proposed a method to improve of control characteristics for power facility using the adaptive sizing of fuzzy inference method. In the use of the controller based the fuzzy logic, a basic mamdani fuzzy controller is applied. However, when the maximum value and the minimum value have to taken, the fuzzy controller can not take a normal value because of formalized grouping form. In this paper, we combine the conventional methods with single valued sets to compensate for the disadvantage caused by the mamdani method control. Simulation results show that the proposed method has better overshoot and steady state arrival time than the conventional control method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.14 no.3
• /
• pp.68-76
• /
• 2000

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Futhermore, a series of conventional techniques such as the pole placement and the optimal control based on the local linearizations have narrow stabilizable regions. At the same time, the fine tunings of their gain parameters are also troublesome. Thus, in this paper, an Evolving Neural Network Controller(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algorithm(RVEGA) was presented for stabilization of an IP system with nonlinearity. This proposed ENNC was described by a simple genetic chromosome. And the deletion of neuron, the according to the various flag types. Therefore, the connection weights, its structure and the neuron types in the given ENNC can be optimized by the proposed evolution strategy. And the proposed ENNC was implemented successfully on the ADA-2310 data acquisition board and the 80586 microprocessor in order to stabilize the IP system. Through the simulation and experimental results, we showed that the finally acquired optimal ENNC was very useful in the stabilization control of IP system.