• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 1987.10a
• /
• pp.249-254
• /
• 1987

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.50 no.1
• /
• pp.22-30
• /
• 2001

This paper describes a neural network based fuzzy PID control scheme. The PID controller is being widely used in industrial applications. However, it is difficult to determine the appropriated PID gains in nonlinear systems and systems with long time delay and so on. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based self tuning fuzzy PID controller of which output gains were adjusted automatically. The tuning parameters of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods. Then they were adjusted by using proposed neural network learning algorithm. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The experiment on the magnetic levitation system, which is known to be heavily nonlinear, showed the proposed controller's excellent performance.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.7 no.5
• /
• pp.830-836
• /
• 2006

In this paper described an auto tuning neuro-fuzzy PID controller based neural network. The PID type controller has been widely used in industrial application due to its simply control structure, easy of design, and inexpensive cost. However, control performance of the PID type controller suffers greatly from high uncertainty and nonlinearity of the system, large disturbances and so on. In this paper will design to take advantage of neural network fuzzy theory and pid controller auto toning technique. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. This controller simple structure and computational complexity are less, and also application is easy and performance is excellent in system that is strong and has nonlinearity to system dynamic behaviour change or disturbance. Finally, the proposed auto tuning neuro-fuzzy controller is applied to magnetic levitation. Simulation results demonstrated that the control performance of the proposed controller is better than that of the conventional controller.

• Proceedings of the KIEE Conference
• /
• 2001.07b
• /
• pp.1182-1184
• /
• 2001

This paper presents a neural network based self-tuning fuzzy PID control scheme for induction motor speed control. 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 PID controller whose scaling factors are adjusted automatically. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink is performed to verify the effectiveness of the proposed scheme.

• 제어로봇시스템학회:학술대회논문집
• /
• 1988.10a
• /
• pp.41-44
• /
• 1988

This paper represents the study of an effective self-tuning PID control for a robot manipulator to track a reference trajectory in spite of the presence of nonlinearities and parameters uncertainties in robot dynamic models. In this control scheme, an error model of the manipulator is established, for the first time, by difference between joint reference trajectory and tracked trajectory. It's model Parameters are estimated by the recursive least-square identification algorithm, and classical controller parameters are determined by pole placement method. A computer simulation study was conducted to demonstrate performance of the proposed self-tuning PID control in joint-based coordinates for a robot with payload.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.51 no.12
• /
• pp.691-696
• /
• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for induction motor speed control. When induction motor is continuously used long time, its electrical and mechanical Parameters will change, which degrade the Performance of PID controller considerably. This Paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using dSPACE(DS1102) board are performed to verify the effectiveness of the proposed scheme.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.704-707
• /
• 1997

In this paper, we present a fuzzy logic based tuner for continuous on-line tuning of PID controllers. The essential idea of the scheme is to parameterize a Ziegler-Nichols-like tuning formula by a singler parameter .alpha., then to use an on line fuzzy logic to self-tune the parameter. The adaptive scaling makes the controller robust against large variations in parametric and dynamics uncertainties in the plant model. New self-tuning controller has the ability to decide when to use PI or PID control by extracting process dynamics from relay experiments. These scheme lead to improved performance of the transient and steady state behavior of the closed loop system, including processes with nonminimum phase processes.

• Proceedings of the KIEE Conference
• /
• 2002.07b
• /
• pp.1165-1168
• /
• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.5 no.4
• /
• pp.101-110
• /
• 1995

In this paper, a DC servo motor position control system based on acceleration control is proposed. The proposed control system consists of an acceleration controller and an auto-tuqing fuzzy PID controller. The auto-tuning fuzzy PID controller provides corrections for an acceleration reference to remove the effect of parametric uncertainties. And it comprises of the expert system which performs the automatic tuning of the PID controller parameters and the conventional PID controller. Expermental results demonstrate strate thi~tth e proposed overall control system has robust properties and good control performances with regard to unmeasurable disturbances and parameter variations. Therefore, the proposed control scheme enhances the applicability of an acceleration control approach and especially performs accurate position control under such an operating environment that model uncertainties exist and/or load, etc. change significantly.

• Journal of Institute of Control, Robotics and Systems
• /
• v.2 no.1
• /
• pp.14-20
• /
• 1996

This paper presents a self tuning method of a velocity type PID controller for minimum or non-minimum phase systems with time delays. The velocity type PID control structure is determined in the process of minimizing the variance of the auxilliary output, and self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a design parameter. This method is simple and effective compared with other existing methods[1,2]. Numerical examples are included to illustrate the procedure and to show the performance of the control system.