• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.393-396
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• 2002

This note investigates the necessity of properness constraint on iterative learning controllers from the viewpoint of the initial condition problem. It is shown that unless the iterative learning controller is proper, the teaming control input may grow unboundedly and thus not be feasible in practice, though the convergence of tracking error is theoretically guaranteed. In addition, this note analyzes the effects of initial condition misalignment in the iterative learning control system on the control input and convergence property.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.307-310
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• 2003

In this paper, an iterative learning control method is applied to suppress the vibration of a 2-mass system which has a flexible coupling between a load an a motor. More specifically, conditions for the load speed without vibration are derived based on the steady-state condition. And the desired motor position trajectory is synthesized based on the relation between the load and motor speed. Finally, a PD-type learning iterative control law is applied for the desired motor position trajectory. Since the learning law applied for the desired trajectory guarantees the perfect tracking performance, the resulting load speed shows no vibration. In order to handle the initial position error, the PD-type learning law is changed to PID-type and a weight function is added to suppress the residual vibration caused by the initial error. The simulation results show the effectiveness of the proposed learning method.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.230-235
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• 1992

We propose a method of generating data to train a neural network controller. The data can be prepared directly by an iterative learning technique which repeatedly adjusts the control input to improve the tracking quality of the desired trajectory. Instead of storing control input data in memory as in iterative learning control, the neural network stores the mapping between the control input and the desired output. We apply this concept to the trajectory control of a two link robot manipulator with a feedforward neural network controller and a feedback linear controller. Simulation results show good generalization of the neural network controller.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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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.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.534-540
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• 1993

In this paper, we present an iterative learning method of compensating for position sensor error. The previously known compensation algrithms need a special perfect position sensor or a priori information about error sources, while ours does not. To our best knowledge, any iterative learning approach has not been taken for sensor error compensation. Furthermore, our iterative learning algorithm does not have the drawbacks of the existing iterative learning control theories. To be more specific, our algorithm learns a uncertain function inself rather than its special time-trajectory and does not request the derivatives of measurement signals. Moreover, it does not require the learning system to start with the same initial condition for all iterations. To illuminate the generality and practical use of our algorithm, we give the rigorous proof for its convergence and some experimental results.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1483-1486
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• 2002

In this paper, a new scheme of iterative loaming control of a robot manipulator is presented. The proposed method uses a fuzzy sliding mode controller(FSMC), which is designed based on the similarity between the fuzzy logic control(FLC) and the sliding mode control(SMC), for the feedback. With this, the proposed method makes possible fDr fast iteration and has advantages that no linear approximation is used for the derivation of the learning law or in the stability proof Full proof of the convergence of the fuzzy sliding base learning scheme Is given.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.9-15
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• 1998

This paper presents an adaptive learning control method for unknown nonlinear systems by combining neuro control and iterative learning control techniques. In the present control system, an iterative learning controller (ILC) is used for a process of short term memory involved in a temporary adaptive and learning manipulation and a short term storage of a specific temporary action. The learning gain of the iterative learning law is estimated by using a neural network for an unknown system except relative degrees. The control informations obtained by ILC are transferred to a long term memory-based feedforward neuro controller (FNC) and accumulated in it in addition to the previously stored infonnations. This scheme is applied to a two link robot manipulator through simulations.

• Proceedings of the KIPE Conference
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• 2003.07b
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• pp.584-587
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• 2003

Iterative learning control is an approach to improve the transient response of systems that operate repetitively over a fixed time interval. It is useful for the system where the system output follows the different type input, in case of design or modeling uncertainty In this paper, we introduce the concept of iterative learning control and then apply the learning control algorithm for multi-motor system for performance Improvement.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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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.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.335-338
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• 1996

In this paper, we are going to design an iterative learning controller with the robust properties for initial error. For this purpose, the PID-type learning law will be considered and the design guide-line will be presented for the selection of the learning gain. Also, we are going to suggest a condition for the convergence of control input for a plant with input delay. Several simulation results are presented, which shows the effectiveness of the proposed algorithms.