• 제어로봇시스템학회논문지
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• 제7권1호
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• pp.1147-1154
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• 2001

In this paper, a new iterative learning control scheme for discrete time nonlinear systems is proposed based on an objective function consisting of the output error and input energy. The relationships between the proposed ILC and the optimal control are described. A new input update law is proposed and its convergence is proved under certain conditions. In this proposed update law, the inputs in the whole control horizon are updated at once considered as one large vector. Some illustrative examples are given to show the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.91-94
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• 1996

This paper presents an approach to estimation of learning gain in iterative learning control for discrete-time affine nonlinear systems. In iterative learning control, to determine learning gain satisfying the convergence condition, we have to know the system model. In the proposed method, the input-output equation of a system is identified by neural network refered to as Piecewise Linearly Trained Network (PLTN). Then from the input-output equation, the learning gain in iterative learning law is estimated. The validity of our method is demonstrated by simulations.

• 제어로봇시스템학회논문지
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• 제2권3호
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• pp.165-173
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• 1996

It is not desirable to have too large control input in control systems, because there are usually a limitation for the input magnitude and cost for the input energy. Previous papers in the iterative learning control did not considered on these points. In this paper, an iterative learning control method is proposed for a class of nonlinear systems with consideration on input magnitude by adopting a concept of cost function consisting of the output error and the input magnitude in quadratic form. We proposed a new input update law with an input penalty function. If we choose a reasonable input penalty function, the two control objectives, good command following and small input energy, can be achieved. The characteristics of the proposed method are shown in the simulation examples.

• 전자공학회논문지SC
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• 제37권2호
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• pp.1-7
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• 2000

본 논문에서는 입-출력 모델로 표현되는 SISO(Single-Input-Single-Output) 비선형 시스템의 적응제어 기법을 제시한다. 시스템에 포함된 미지의 파라미터는 알고 있는 콤펙트(compact) 볼록(convex) 집합 내에 있다고 가정한다. 기존의 결과와는 달리 이 집합은 원점을 포함하는 원이나 초입방체(hypercube)를 포함한 임의의 콤펙트 볼록 집합이 될 수 있다. 제시하는 갱신(update) 법칙으로 얻어지는 파라미터 추정치는 항상 알고 있는 집합 내에 존재하며 이를 이용한 제어 입력을 적용하면 시스템의 출력과 원하는 신호와의 위치, 속도, 그리고 가속도 오차가 시간에 따라 영으로 수렴함을 이론적으로 그리고 시뮬레이션을 통하여 입증한다.

• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.194-203
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• 2008

Iterative learning control (ILC) is a simple and effective method for the control of systems that perform the same task repetitively. ILC algorithm uses the repetitiveness of the task to track the desired trajectory. In this paper, we propose a PID (proportional plus integral and derivative) type ILC update law for control discrete-time single input single-output (SISO) linear time-invariant (LTI) systems, performing repetitive tasks. In this approach, the input of controlled system in current cycle is modified by applying the PID strategy on the error achieved between the system output and the desired trajectory in a last previous iteration. The convergence of the presented scheme is analyzed and its convergence condition is obtained in terms of the PID coefficients. An optimal design method is proposed to determine the PID coefficients. It is also shown that under some given conditions, this optimal iterative learning controller can guarantee the monotonic convergence. An illustrative example is given to demonstrate the effectiveness of the proposed technique.

• 전기학회논문지
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• 제57권1호
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• pp.117-120
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• 2008

This paper investigates the convergence condition of ADILC(iterative learning control with advanced output data) for nonminimum phase systems. ADILC has simple learning structure including both minimum phase and nonminimum phase systems. However, for nonminimum phase systems, the overall time horizon must be considered in input update law. This makes the dimension of convergence condition matrix large. In this paper, a new sufficient condition is proposed to satisfy the convergence condition. Also, it has been shown that this sufficient condition can be satisfied although it is not full impulse response.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.470-473
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• 1996

This paper presents an iterative learning control scheme for industrial manipulators. Based upon the frequency-domain analysis, the input update law of the learning controller is given together with a sufficient condition for the convergence of the iterative process in the frequency domain. The proposed learning control scheme is structurally simple and computationally efficient since it is independent joint control depending only on locally measured variables and it does not involve the computation of complicated nonlinear manipulator dynamics. Moreover, it is capable of canceling the unmodeled dynamics of the manipulator without even the parametric model. Several important aspects of the learning scheme inherent in the frequency-domain design are discussed and the control performance is demonstrated through computer simulations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.225-227
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• 2006

This note investigates the convergence condition of ADILC (iterative learning control with advanced output data) for nonminimum phase systems. ADILC has simple learning structure including both minimum phase and nonminimum phase systems. However, for nonminimum phase systems, the overall time horizon must be considered in input update law. This makes the dimension of convergence condition matrix large. In this paper, a new sufficient condition is proposed to satisfy the convergence condition. Also, it has been shown that this sufficient condition can be satisfied although it is not full impulse response.

• 한국항공우주학회지
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• 제38권3호
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• pp.228-235
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• 2010

본 논문에서는 틸트로터 항공기의 회전익 모드에 대한 자율비행 유도제어 알고리즘을 적응제어기법을 이용하여 설계하는 것이다. 이를 위해 우선 출력기반 근사적 궤환선형화 기법을 통하여 알고리즘의 내부루프를 구성하고 그로부터 발생하는 모델오차를 단일 은닉층-신경망을 적용하여 상쇄하였다. 그리고 리아푸노프 안정성 이론에 따른 적응제어 갱신법칙은 선형 관측기를 기반으로 설계하였다. 나아가 외부루프는 경로점 유도법칙으로부터 생성되는 궤적을 추종하도록 하였으며 특히 엄밀한 자동착륙 궤적추종 성능 향상을 위하여 방향각 및 비행경로각 시선유도법칙을 설계하였다. 틸트로터 비선형 모델 시뮬레이션 결과는 콜렉티브 입력에서 보이는 순간적인 작동기 포화현상 이외에는 만족할 만한 안정성과 추종성능을 보여 주고 있다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.999-1004
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• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.