• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1996.10a
• /
• pp.150-155
• /
• 1996

This paper presents a new approach to the design of self-tuning adaptive control system that is robust to the changing dynamic configuration as well as to the load variation factors using digital signal processors for robot manipulators. TMS3200C50 is used in implementing real-time adaptive control algorithms provide advanced performance for robot manipulator. In this paper an adaptive control scheme is proposed in order to design the pole-placement self-tuning controller which can reject the offset due to any load disturbance without a detailed description of robot dynamics. parameters of discrete-time difference model are estimated by the recursive least-square identification algorithm and controller parameters are detemined by the pole-placement method. Performance of self-tuning adaptive controller is illusrated by the simulation and experiment for a SCARA robot.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.11a
• /
• pp.63-66
• /
• 1997

The rule bases self organizing controller(SOC) has one of its main advantages in the fact that there is no need to have a mathematical description of the system to be controlled. In this controller, the rules are linguistics statements expressed mathematically through the concepts of fuzzy sets and correspond to the actions a human operator would take when controlling a given process. With this controller, we have performed to sight stabilization system, and we realize that it needs a scale factor tuning. The self tuning controller(STC) uses an instantaneous system fuzzy performance which can give an inspection to the scale factor. Therefore, the STC can compensate the scale factor when it is not adequately tuned. With this trial, we shows that STC can give a good transient characteristics in the nonlinearity which imposed basically in the conventional servo system.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.629-632
• /
• 1997

This paper presents the design method of controller which is combined Genetic Algorithms with the Generalized minimum variance self tuning controller. It is shown that the controllers adapts to changes in the system parameters with time delays and noises. The 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 polynomial parameters. The computer simulation results are presented to illustrate the procedure and to show the performance of the control system.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10b
• /
• pp.190-192
• /
• 1987

In the paper an multivariable decoupling self-tuning algorithm is proposed for controller design, by specifying the closed-loop behaviour of the system in the form of a reference model, so that the controller parameters can be estimated on-line as the process development. The effectiveness of this algorithm in controlling multivariable systems is demonstrated by simulation example in spite of the usual implementation problems of self-tuning controllers.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10b
• /
• pp.179-181
• /
• 1987

In this paper, a self-tuning controller design is proposed by using pole-zero placement method and considering a system time delay. To got better tracking for the generalized self-tuning controller, pole placement method for the closed loop system and zero placement method for the error transfer function are Introduced. The proposed method shows better efficiency than pole placement method for minimizing tracking error. Simulation gives good results in tie reference signal tracking.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.40 no.4
• /
• pp.264-274
• /
• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• Proceedings of the KIEE Conference
• /
• 1989.07a
• /
• pp.125-128
• /
• 1989

This paper presents a DC servo motor controller using self-tuning PID algorithm, which can support Multi-processor for the real time processing. Computer simulation as well as experiment using Multi-processor(8088) are implemented with self-tuning PID algorithm. Presented algorithm is used to compare the performance of the controller with that of the classical PID controller through computer simulation and experiment. The result which use the Self-Tuning algorithm show that motor output follows the reference input trajectory fairly well inspite of load disturbances and parameter variations.

• 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.

• 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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.23 no.11
• /
• pp.69-75
• /
• 2009

In this paper, a self-tuning regulator for a speed control of a permanent magnet type stepping motor is proposed. The self-tuning theory provides a nonlinear modeling of a stepping motor drive system and can provide the controller with information regarding the reference variation and parameter variation of the stepping motor through the on-line estimation. The proposed self-tuning regulator organize the positive feedback loop and IP(Integral-Proportional) type. Therefore, the proposed self-tuning regulator has a robust control capabilities during dynamic operation. The availability of the proposed controller is verified through experimental results.