• Journal of Power Electronics
• /
• v.9 no.5
• /
• pp.800-808
• /
• 2009

This paper presents a practical method of pressure control for a hydraulic oil-pump system using an SR (Switched Reluctance) drive. For a 6Mpa grade hydraulic oil-pump, a 2.6kW SR drive is developed. In order to get high performance pressure dynamics in actual applications, a data based PID control scheme is proposed. The look-up table from a pre-measured data base produces an approximate current reference based on motor speed and oil-pressure. A PID controller can compensate for the pressure error. With the combination of the two references, the proposed control scheme can achieve fast dynamics and stable operation. Furthermore, a suitable current controller considering the nonlinear characteristics of an SRM (Switched Reluctance Motor) and practical test methods for data measuring are presented. The proposed control scheme is verified by experimental tests.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.142-142
• /
• 2000

During the operation of crane system in container yard, it is necessary to control the crane trolley position so that the swing of the hanging container is minimized. Recently an automatic control system with high speed and rapid transportation is required. Therefore, we designed a controller to control the crane system with disturbances. In this paper, Ive present the neural network two degree of freedom PID controller to control the swing motion and trolley position. Then we executed the computer simulation to verify the performance of the proposed controller and compared the performance of the neural network PID controller with our proposed controller in terms of the rope swing and the precision of position control . Computer simulation results show that the proposed controller has better performances than neural network PID with disturbances.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2129-2131
• /
• 2001

Induction motor has been using for industrial field. Up to the present time, the PID controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain without any experience, since the gain of the PID controller has to be manually tuned by trial and error procedures. This paper focuses on the fuzzy control for optimal control of the induction motor in plant. In order to attain optimal control, flux, torque and speed controller has been used and an fuzzy logic based controller has been applied to this system. The results of the fuzzy are compared with the PID controller tuned by the Ziegler-Nickels method, through various simulation based on the various disturbance and step response. The simulation results of the fuzzy control represent a more satisfactory response than those of the conventional controllers.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.1
• /
• pp.1-6
• /
• 2013

For regulating generator speed above the rated wind, versatile methods have been published based on PID. However, these methods with the fixed PID gains could not guarantee that the controller works well in the whole area. In this paper, variable PID gain method has been suggested to overcome this problem. The sensitivity of power to blade pitch angle changes according to wind speed. The variable PID gain function has been derived from this sensitivity.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.8
• /
• pp.1993-1997
• /
• 2009

Robust control for DC servo motor is needed according to the highest precision of industrial automation. However, when a motor control system with PID controller has an effect of load disturbance, it is very difficult to guarantee the robustness of control system. As a compensation method solving this problem, in this paper, PID-neural network hybrid control method for motor control system is presented. The output of neural network controller is determined by error and rate of error change occurring in load disturbance. The robust control of DC servo motor using neural network controller is demonstrated by computer simulation.

• Journal of Biosystems Engineering
• /
• v.31 no.3 s.116
• /
• pp.203-208
• /
• 2006

This study was conducted to improve the control performance of a current variable-rate controller for granular fertilizers. Simulation model was developed. Optimized proportional, integral and derivative gains were determined by simulation model using 2nd order PID gain learning algorithm, and these control gains were evaluated through the field tests. Important results of this study are as follows; 1. Principles of pre-existing variable-rate application of granular fertilizers were investigated. 2. Simulation model of a PID controller that could simulate the control system was developed by using Matlab/Simulink program. The program was to determine PID control coefficients through the simulation model and 2nd order PID gain learning algorithm. 3. PID control coefficients obtained from the simulation were applied to the developed model. When the step input was given, Maximum overshoot were 1.96%, rise time were 0.05 sec, settling time were 0.06 sec and steady state error were 0.21 % respectively. 4. The simulation model was verified through field tests. The errors of maximum overshoot were 10%, rise time were 0.11 sec, settling time were 0.40 sec and steady state error were 8% because of loads and noises. Rise time was decreased to one third of that of the pre-existing system. 5. If the speed of a fertilizing machine is $0.3{\sim}0.6\;m/s$ and the maximum rotation speed of a discharging roller is 64 rpm, rise time would be 0.26 sec and fertilizing machine would cover the distance of $0.07{\sim}0.15\;m$ with settling time of 0.4 sec, fertilizing machine would cover the distance of $0.12{\sim}0.24\;m$.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.2
• /
• pp.276-282
• /
• 2006

PID controller has been widely used in industry. Because it has a simple structure and robustness to modeling error. But it is difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose an adaptive PID controller based on a gradient descent learning. This algorithm has a simple structure like conventional PID controller and a robustness to system parameters variation and different velocity command. To verify performances of the proposed adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1066-1070
• /
• 2003

Recently technologies have created new principle and theory but the PID control system remains its popularity as the PID controller contains simple structure, including maintenance and parameter adjustment being so simple. Thus, this paper proposes auto tune PID by fuzzy logic controller based on FPGA which to achieve real time and small size circuit board. The digital PID controller design to consist of analog to digital converter which use chip TDA8763AM/3 (10 bit high-speed low power ADC), digital to analog converter which use two chip DAC08 (8 bit digital to analog converters) and fuzzy logic tune digital PID processor embedded on chip FPGA XC2S50-5tq-144. The digital PID processor was designed by fundamental PID equation which architectures including multiplier, adder, subtracter and some other logic gate. The fuzzy logic tune digital PID was designed by look up table (LUT) method which data storage into ROM refer from trial and error process. The digital PID processor verified behavior by the application program ModelSimXE. The result of simulation when input is units step and vary controller gain ($K_p$, $K_i$ and $K_d$) are similarity with theory of PID and maximum execution time is 150 ns/action at frequency are 30 MHz. The fuzzy logic tune digital PID controller based on FPGA was verified by control model of level control system which can control level into model are correctly and rapidly. Finally, this design use small size circuit board and very faster than computer and microcontroller.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.67.1-67
• /
• 2001

This paper presents a neural network based self-tuning fuzzy PI-PD control scheme for robust speed control of induction motor. The PID controller is being widely used in industrial applications. When continuously used long time, the electric and mechanical parameters of induction motor change, degrading the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, and proposes a neural network based self-tuning fuzzy PI-PD controller whose scaling factors are adjusted automatically. Proposed scheme is simple in structure and computational burden is small ...

• Proceedings of the KIEE Conference
• /
• 1989.11a
• /
• pp.327-331
• /
• 1989

This paper concerned about a study on the direct pole placement PID self-tuning controller design for Robot manipulator control system. The method of a direct pole placement self-tuning PID control for a DC motor of robot manipulator tracks a reference velocity in spite of the parameters uncertainties in nonminimum phase system. In this scheme, the parameters of controller are estimated by the recursive least square(RLS) identification algorithm, the pole placement method and diophantine equation. A series of simulation in which minimum phase system and nonminimum phase system are subjected to a pattern of system parameter changes is presented to show some of the features of the proposed control algorithm. The proposed control algorithm which shown are effective for the practical application, and experiments of DC motor speed control for Robot manipulator by a microcomputer IRH-PC/AT are performed and the results are well suited.