• Journal of Institute of Control, Robotics and Systems
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• v.21 no.8
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• pp.718-722
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• 2015

This paper proposes a fractional-order PID (Proportional-Integral-Derivative) control used electromagnetic strip stabilization controller in a continuous galvanizing line. Compared to a conventional PID controller, a fractional-order PID controller has integration-fractional-order and derivation-fractional-order as additional control parameters. Thanks to increased control parameters, more precise controller adjustment is available. In addition, accurate transfer function of a real system generally has a fractional-order form. Therefore, it is more adequate to use a fractional-order PID controller than a conventional PID controller for a real world system. Finite element models of a $1200{\times}2000{\times}0.8mm$ strip, which were extracted using a commercial software ANSYS were used as simulation plants, and Gaussian mixture models were used to find optimized control parameters that can reduce the strip vibrations to the lowest amplitude. Simulation results show that a fractional-order PID controller significantly reduces strip vibration and transient response time than a conventional PID controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.228-231
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• 2000

A hybrid fuzzy controller by means of the genetic algorithms is presented. The control input for the system in the HFC is a convex combination of the FLC's output in transient state and PlD's output in steady state by a fuzzy variable. The HFC combined a PID controller with a fuzzy controller concurrently produces the better output performance than any other controller. A auto-tuning algorithms is presented to automatically improve the performance of hybrid fuzzy controller using genetic algorithms. The algorithms estimates automatical Iy the optimal values of scaling factors, PID parameters and membership function parameters of fuzzy control rules. Especially, in order to auto-tune scaling factors and PID parameters of HFC using GA three kinds of estimation modes are effectively utilized. The HFCs are applied to the second process with time-delay. Computer simulations are conducted at step input and the performances of systems are evaluated and also discussed in ITAE(Integral of the Time multiplied by the Absolute value of Error ) and other ways.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.17-20
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• 2001

In this paper, we design a nonlinear adaptive controller using wavelet neural network. The method proposed in this paper performs for a nonlinear system with unknown parameters, identification with using a wavelet neural network, and then a nonlinear adaptive controller is designed with those identified informations. The advantage of the proposed control method is simple to design a controller for unknown nonlinear systems, because we use the identified informations and design parameters are positioned within a negative real part of s-plane. The simulation results showed the effectiveness of proposed controller design method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.355-361
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• 1992

In this paper, some rules for auto- tuning of feedback-feedforward controller in variable load and disturbance are presented. The parameters of feedback PID controller are determined by heuristic rules based on input regulation experiment, and the parameters of feedforward controller are determined by result rules based on spectral factorization, minimum variance, and polynomial equation. These heuristic and result rules are used as an element of the feedback loop in an auto-tuning feedback-feedforward controller. The robust and accurate control performance is demonstrated by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.128.3-128
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• 2001

In this paper we proposed that the speed of induction motor is controlled by a PI controller, which could control unknown motor using Neural Network for auto-tuning of the PI parameter. The parameters of the PI controller were adjusted to reduce the speed error of the controlled motor. The input parameters of the Neural Network controller are the speed, q-axis current, and speed reference of the induction motor respectively. The usefulness of proposed controller will be confirmed by simulation which we compare with conventional PI controller.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.321-325
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• 1988

The objective of this study is to develop a microcomputer-based adaptive controller for an electro hydraulic velocity control system subjected to the variation of system parameters. The step response performance of the system with the adaptive controller is investigated for the variation of the external load torque, the moment of inertia and the reference inputs, and compared with that obtained by PID controller whose gains are constant. The experimental results show that this proposed model reference adaptive controller is robust to the variation of system parameters and yield much better control performance compared with the conventionel PID controller.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.7
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• pp.852-861
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• 2003

In this paper, in order to improve the disadvantages of the fixed design-parameter fuzzy PID controller. a new fuzzy PID controller named a variable design-parameter fuzzy PID controller is suggested. The main characteristic of the suggested controller is to adjust design-parameters of the controller by comparing magnitudes between fuzzy controller inputs at each sampling time when controller inputs are measured. As a result. all fuzzy input partitioned spaces converge within a time-varying normalization scale. and the resultant PID control action can always be applied precisely regardless of operating input magnitudes. In order to verify the effectiveness of the suggested controller. several a computer simulations for a nonlinear system are executed and the control parameters of the variable design-parameter fuzzy PID controller are throughly analyzed.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.272-276
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• 2004

The conventional PID (Proportional-Integral-Derivative) control technique is widely used for the process control in many industries since it is simple in structure and provides the good response. Nowadays, this control technique has been developed on the Programmable Logic Controller (PLC) to use for the process control loop. However, using this technique is difficult when tuning the PID parameters ($K_p$, $T_i$ and $T_d$) to achieve the best response. Moreover, trial-and-error procedure along with the operator experiences are required to obtain the best results when tuning the PID controller parameters. This paper proposes the self-tuning PID controller based on PLC for the process control in the industries. The proposed self-tuning PID controller uses the PLC-based PID structures to control the process production. The proposed PID tuning utilizes the PLC to synthesize and analyze controller parameter as well as to tune for appropriate parameters using Dahlin method and extrapolation. Experimental results using a self-tuning PID controller to control temperature of the oven show that the controller developed is capable of controlling the process very effectively and provides a good response.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.456-458
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• 1986

A method for on-line tuning of the PID-controller parameters for a discrete-time multivariable process system is presented. And it is based on a step change in the controller set point. The system is presumed to be a linear, open loop stable and known one. The controller parameters are determined by the performance criterion and Fletcher-Powell methods.

• Journal of Power Electronics
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• v.19 no.6
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• pp.1505-1514
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• 2019

The permanent magnet synchronous motor (PMSM) is widely used in various fields and the proportional-integral (PI) controller is popular in PMSM control systems. However, the motor parameters are usually unknown, which can lead to a complicated PI controller design and poor performance. In order to design a PI controller with good performance when the motor parameters are unknown, a control algorithm based on stability margin is proposed in this paper. First of all, based on the mathematical model of the PMSM and the least squares (LS) method, motor parameters are estimated offline. Then based on the estimation values of the motor parameters, natural angular frequency and phase margin, a PI controller is designed. Performance indices including the natural angular frequency and the phase margin are used directly to design the PI controller in this paper. Scalar functions of the d-loop and the q-loop are selected. It can be seen that the designed controller parameters satisfy Lyapunov large scale asymptotic stability theory if the natural angular frequencies of the d-loop and the q-loop are large than 0. Experimental results show that the parameter estimation method has good accuracy and the designed PI controller proposed in this paper has good static and dynamic performances.