• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.347-350
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• 1996

With only the classical PID controller applied to control of a DC motor, a good (target) performance characteristic of the controller can be obtained, if all the model parameters of DC motor and operating conditions such as external load torque, disturbance, etc. are exactly known. However, in case when some of system parameters or operating conditions are uncertain or unknown, the fixed PID controller does not guarantee the good performance which is assumed with precisely known system parameters and operating conditions. In view of this and robustness enhancement of DC motor control system, we propose a PID learning controller which consists of a set of learning rules for PID gain tuning and learning of an auxiliary input. The proposed PID learning controller is shown to drive the state of uncertain DC motor system with unknown system parameters and external load torque to the desired one globally asymptotically. Computer simulation results are given to demonstrate the effectiveness of the proposed PID learning controller, thereby showing whose superiority to the conventional fixed PID controller.

• 제어로봇시스템학회논문지
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• 제22권2호
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• pp.126-132
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• 2016

The root-locus method is often employed when a controller is designed to find controller gain. It is usually used to determine one parameter gain while most controllers for industrial applications have more than one controller gain. For example PID controller has three controller gains, i.e. P, I, and D gains. Thus the conventional root-locus technique cannot complete the design of a controller with more than one controller gain. One way to overcome this drawback has been to apply the root-locus technique for one parameter while other parameters are assumed to be proportional to the parameter or to be constant. However this approach could lead to limited performance of the controller and if we try to adjust the proportional ratio or constants then it could be a long and tedious process of trial and error. Thus it is required to find an effective method for the root-locus technique to design controllers with more than one parameter. To this end this paper proposes an extended root-locus method for controllers with two parameters. In this paper Matlab is used as a computation tool to show the effectiveness of our method by solving examples numerically. As a result we obtained an extended root-locus illustrated in two-dimensional space for a control system with two parameters. The paper then presents how to find two controller gains based on this result of the extended root-locus. The main idea is that we can find the parameters by approaching the desired poles. It is expected that the proposed idea will help control engineers to easily design control systems using the root-locus technique, resulting in more accurate and faster control systems. Note that the extended root-locus idea can be applied to controller design problems with multiple parameters.

• Smart Structures and Systems
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• 제9권4호
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• pp.373-392
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• 2012

Tracking control of systems with variable stiffness hysteresis using a gain-scheduled (GS) controller is developed in this paper. Variable stiffness hysteretic system is represented as quasi linear parameter dependent system with known bounds on parameters. Assuming that the parameters can be measured or estimated in real-time, a GS controller that ensures the performance and the stability of the closed-loop system over the entire range of parameter variation is designed. The proposed method is implemented on a spring-mass system which consists of a semi-active independently variable stiffness (SAIVS) device that exhibits hysteresis and precisely controllable stiffness change in real-time. The SAIVS system with variable stiffness hysteresis is represented as quasi linear parameter varying (LPV) system with two parameters: linear time-varying stiffness (parameter with slow variation rate) and stiffness of the friction-hysteresis (parameter with high variation rate). The proposed LPV-GS controller can accommodate both slow and fast varying parameter, which was not possible with the controllers proposed in the prior studies. Effectiveness of the proposed controller is demonstrated by comparing the results with a fixed robust $\mathcal{H}_{\infty}$ controller that assumes the parameter variation as an uncertainty. Superior performance of the LPV-GS over the robust $\mathcal{H}_{\infty}$ controller is demonstrated for varying stiffness hysteresis of SAIVS device and for different ranges of tracking displacements. The LPV-GS controller is capable of adapting to any parameter changes whereas the $\mathcal{H}_{\infty}$ controller is effective only when the system parameters are in the vicinity of the nominal plant parameters for which the controller is designed. The robust $\mathcal{H}_{\infty}$ controller becomes unstable under large parameter variations but the LPV-GS will ensure stability and guarantee the desired closed-loop performance.

• 로봇학회논문지
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• 제9권1호
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• pp.67-77
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• 2014

An electric motor is the one of the most important parts in robot systems, which mainly drives the wheel of mobile robots or the joint of manipulators. According to the requirement of motor performance, the controller type and parameters vary. For the wheel driving motors, a speed tracking controller is used, while a position tracking controller is required for the joint driving motors. Moreover, if the mechanical parameters are changed or a different motor is used, we might have to tune again the controller parameters. However, for the beginners who are not familiar about the controller design, it is hard to design pertinently. In this paper, we develop a nominal robust controller model for the velocity tracking of wheel driving motors and the position tracking of joint driving motors based on the disturbance observer (DOB) which can reject disturbances, modeling errors, and dynamic parameter variations, and propose the methodology for the determining the least control parameters. The proposed control system enables the beginners to easily construct a controller for the newly designed robot system. The purpose of this paper is not to develop a new controller theory, but to increase the user-friendliness. Finally, simulation and experimental verification have performed through the actual wheel and joint driving motors.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2120-2122
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• 2002

There are several types of control systems that use fuzzy logic controller as a essential system component. The majority of research work on fuzzy PID controller focuses on the conventional two-input PI or PD type controller. However, fuzzy PID controller design is a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. In this paper we combined conventional PI type and PD type fuzzy controller and set the initial parameters of this controller from the conventional PID controller gains obtained by Ziegler-Nichols tuning or other coarse tuning methods. After that, by replacing some of these parameters with sing1e neurons and making them to be adjusted by back-propagation learning algorithm we designed a neuro-fuzzy controller which showed good performance characteristics in both computer simulation and actual application.

• 대한전기학회논문지
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• 제41권3호
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• pp.311-319
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• 1992

In this paper, a direct adaptive pole placement controller for an unknown linear time-invariant single-input single-output nonminimum phase plant is proposed. To design this direct adaptive pole placement controller, the auxiliary signals are introduced. Consequently, a linear equation error model is formulated for estimating both the controller parameters and the additional auxiliary parameters. To estimate the controller parameters and the additional auxiliary parameters, the exponentially weighted least-squares algorithm is implemented, and a method of selecting the characteristic polynomials of the sensitivity function filters is proposed. In this method, all the past measurement data are weighted exponentially. A series of simulations for a nonminimum phase plant is presented to illustrate some features of both the parameter estimation and the output response of this adaptive pole placement controller.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권5호
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• pp.259-267
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• 2003

In this paper, we propose a nonlinear variable PID controller using a cell-mediated immune response. An immune feedback response is based on the functioning of biological T-cells. An immune feedback response and P-controller of conventional PID controllers resemble each other in role and mechanism. Therefore, we extend immune feedback mechanism to nonlinear PE controller. And in order to choose the optimal nonlinear PID controller games, we also propose the on-line tuning algorithm of nonlinear functions parameters in immune feedback mechanism. The trained parameters of nonlinear functions are adapted to the variations of the system parameters and any command velocity. And the adapted parameters obtained outputs of nonlinear functions with an optimal control performance. To verify performances of the proposed control systems, the speed control of nonlinear BC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system variations.

• 소음진동
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• 제11권1호
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• pp.147-155
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• 2001

This paper is concerned with the real-time automatic tuning of the multi-input multi-output positive position feedback controllers for smart structures by the genetic algorithms. The genetic algorithms have proven its effectiveness in searching optimal design parameters without falling into local minimums thus rendering globally optimal solutions. The previous real-time algorithm that tunes a single control parameter is extended to tune more parameters of the MIMO PPF controller. We employ the MIMO PPF controller since it can enhance the damping value of a target mode without affecting other modes if tuned properly. Hence, the traditional positive position feedback controller can be used in adaptive fashion in real time. The final form of the MIMO PPF controller results in the centralized control, thus it involves many parameters. The bounds of the control Parameters are estimated from the theoretical model to guarantee the stability. As in the previous research, the digital MIMO PPF control law is downloaded to the DSP chip and a main program, which runs genetic algorithms in real time, updates the parameters of the controller in real time. The experimental frequency response results show that the MIMO PPF controller tuned by GA gives better performance than the theoretically designed PPF. The time response also shows that the GA tuned MIMO PPF controller can suppress vibrations very well.

• 한국지능시스템학회논문지
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• 제14권7호
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• pp.807-815
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• 2004

본 논문은 고정 파라미터를 갖는 퍼지 PID제어기의 설계방법을 기술하고 컴퓨터 연산 시간을 줄일 수 있는 단순화된 퍼지 PID제어기를 제안하며 제안한 제어기의 안정도를 분석한다. 안정도 분석을 위해 사용한 소이득 정리로부터 전체 피드백 시스템의 BIBO 안정도의 실제적인 충분조건을 유도하고, 유도한 안정 조건으로부터 안정한 선형 PID제어기의 파라미터로부터 퍼지 PID제어기의 파라미터를 결정하는 방법을 고찰한다. 마지막으로 선형 시스템과 비선형 시스템에 대한 컴퓨터 시뮬레이션을 실시하고 비선형 퍼지 PID제어기의 시뮬레이션 결과를 선형 PID제어기의 시뮬레이션 결과와 비교하여 성능을 확인한다.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 추계종합학술대회논문집
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• pp.221-224
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• 2000

We consider one of robust controller, fuzzy-sliding mode controller dealing with model uncertainty, simplified representation of nonlinear system, changed parameters of plant. We propose fuzzy-sliding mode algorithm which provides control input that has system states approaching the choosed sliding surface. This fuzzy controller has a rule base to get initial states converged on sliding surface. This algorithm Is applied to a transfer function of DC motor to be modeled simply and do position control of DC motor due to system parameters. We compare fuzzy-sliding mode controller to both sliding mode controller and fuzzy controller to identify roust control.