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James Hwang

Bio: James Hwang is an academic researcher from Dayeh University. The author has contributed to research in topics: Iterative learning control & Tracking error. The author has an hindex of 4, co-authored 5 publications receiving 76 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, a proportional-valve-controlled pneumatic X-Y table system for position tracking control experiments is presented, where the tracking error from previous stages is used as the correction factor for the next control action.

33 citations

Proceedings ArticleDOI
06 Jul 2004
TL;DR: Experimental results show that under the disturbances the PD-typed ILC controller is superior to the P-typing one and can effectively control the system to track the given circular trajectory.
Abstract: In this paper, a proportional-valve controlled pneumatic X-Y table system is built to perform position tracking control experiments. The pneumatic system is subjected to external loads and to the parameter changes during the control. ILC (Iterative Learning Control) controllers are implemented in the experiments to show their ability to reject disturbances. The P and PD-typed updating laws with delay parameters are used respectively for the repetitive trajectory tracking control of X-Y table. Pre-saved control signals for different types of disturbances are also used compare control performances. Experimental results show that under the disturbances the PD-typed ILC controller is superior to the P-typed one and can effectively control the system to track the given circular trajectory.

15 citations

Proceedings ArticleDOI
02 Sep 2004
TL;DR: In this article, a proportional-valve controlled pneumatic X-Y table system is built to perform the position tracking control experiments and the ILC controllers are implemented in the experiments.
Abstract: The iterative learning control (ILC) learns the unknown information from repeated control operations. The tracking error from previous stages is used as the correction factor for the next control action. Therefore, the ILC controller can make the system tracking error converge to a small region within the limited numbers of iterations. A proportional-valve controlled pneumatic X-Y table system is built to perform the position tracking control experiments. The ILC controllers are implemented in the experiments and the results are compared. The P-type updating law with delay parameters are used for both the x and y axes in the repetitive trajectory tracking control. Experimental results show that the ILC controller can effectively control the system to track the given circular trajectory at different speeds. The controller parameters are varied for studying their effects. Inertial disturbance is also added to show the learning performance of the ILC controller.

14 citations

01 Feb 2013
TL;DR: In this article, a PD-type iterative learning control (ILC) algorithm with time delay parameters is studied to control the upper plate of the Stewart platform to track a desired trajectory.
Abstract: The real-time path-tracking control of Stewart platform (SP) is very difficult, because the six links must be actuated and controlled simultaneously to track the desired trajectory. The desired length trajectory of the six actuators is obtained using inverse kinematic mechanism. To achieve the precise spatial motion of SP, there is a limitation on the permissible tracking error for each actuator. This paper presents a real-time implementation of iterative learning control (ILC) for a Stewart platform manipulator. A PD-type ILC algorithm with time delay parameters are studied to control the upper plate of the Stewart platform to track a desired trajectory. Two real-time experiments validate the proposed method with the permissible tracking errors.

1 citations


Cited by
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Journal ArticleDOI
01 Nov 2007
TL;DR: The iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors.
Abstract: In this paper, the iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors. The papers includes a general introduction to ILC and a technical description of the methodology. The selected results are reviewed, and the ILC literature is categorized into subcategories within the broader division of application-focused and theory-focused results.

1,417 citations

Journal ArticleDOI
TL;DR: In this paper, a non-linear ILC algorithm that utilizes the capability of the Newton method was proposed, which allows one to decompose a nonlinear iterative learning control problem into a sequence of linear time-varying ILC problems.
Abstract: Significant progress has been achieved in terms of both theory and industrial applications of iterative learning control (ILC) in the past decade However, the techniques of solving non-linear ILC problems are still under development The main result of this paper is a novel non-linear ILC algorithm that utilizes the capability of the Newton method By setting up links between non-linear ILC problems and non-linear multivariable equations, the Newton method is introduced into the ILC framework The implementation of the new algorithm allows one to decompose a nonlinear ILC problem into a sequence of linear time-varying ILC problems Simulations on a discrete non-linear system and a manipulator model display its advantages Conditions for its semi-local convergence are analysed Links of ILC with existing non-linear topics are pointed out as ways to construct new non-linear ILC schemes Potential improvements are discussed for future work

74 citations

Journal Article
TL;DR: In this paper, a PID (proportional plus integral and derivative) type ILC update law was proposed for control discrete-time single input single output (SISO) linear time-invariant (LTI) systems, performing repetitive tasks.
Abstract: 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.

71 citations

Journal ArticleDOI
TL;DR: In this paper, a rotary-cylinder position pneumatic servo mechanism was used to actuate the ball-plate system, which has high control precision, large speed variation range and good low-speed characteristics.
Abstract: For the multivariable and complicated ball-plate control system, in this study a touch screen and a rotary pneumatic cylinder are adopted instead of a camera and a step motor, respectively. A touch screen is utilised to collect the ball coordinates, and it is of high precision, rapid response and strong anti-interference ability. Using the rotary-cylinder position pneumatic servo mechanism to actuate the ball-plate system, it has high control precision, large speed variation range and good low-speed characteristics. Models of the rotary-cylinder servo mechanism and the ball-plate system are, respectively, built. For the non-linearity of the system, it is hard to attain good performance by using the traditional control method, so the controller is designed with a state observer and fuzzy control algorithm. Simulation results show that it has good dynamic and static characteristics with the proposed control method. And a hardware-in-the-loop (HIL) model is further built to realise the steady run of the system.

46 citations

Journal ArticleDOI
TL;DR: A finite impulse response mapping is derived to generalize the learned forces at a specific set-point toward arbitrary set- point profiles, thus relaxing the need for further learning.

40 citations