scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Iterative learning control for position tracking of a pneumatic actuated X–Y table

01 Dec 2005-Control Engineering Practice (Pergamon)-Vol. 13, Iss: 12, pp 1455-1461
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.
About: This article is published in Control Engineering Practice.The article was published on 2005-12-01. It has received 33 citations till now. The article focuses on the topics: Iterative learning control & Control theory.
Citations
More filters
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 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


Cites background from "Iterative learning control for posi..."

  • ...For controlled processes exhibiting repeated motion, iterative learning control provides a means to learn updated commands (or forces) from past servo information and apply these commands at future executions (or trials) of such motion, see also Chen and Hwang (2005), Mishra, Coaplen, and Tomizuka (2007), and Tayebi and Islam (2006). The latter with the aim to improve upon servo performance....

    [...]

  • ...…repeated motion, iterative learning control provides a means to learn updated commands (or forces) from past servo information and apply these commands at future executions (or trials) of such motion, see also Chen and Hwang (2005), Mishra, Coaplen, and Tomizuka (2007), and Tayebi and Islam (2006)....

    [...]

  • ...For controlled processes exhibiting repeated motion, iterative learning control provides a means to learn updated commands (or forces) from past servo information and apply these commands at future executions (or trials) of such motion, see also Chen and Hwang (2005), Mishra, Coaplen, and Tomizuka (2007), and Tayebi and Islam (2006)....

    [...]

Journal ArticleDOI
TL;DR: In this paper, a new kind of volume control hydraulic press that combines the advantages of both hydraulic and SRM (switched reluctance motor) driving technology is developed, and a fuzzy ILC algorithm that utilizes the fuzzy strategy to adaptively adjust the iterative learning gains is put forward.
Abstract: A new kind of volume control hydraulic press that combines the advantages of both hydraulic and SRM (switched reluctance motor) driving technology is developed. Considering that the serious dead zone and time-variant nonlinearity exist in the volume control electro-hydraulic servo system, the ILC (iterative learning control) method is applied to tracking the displacement curve of the hydraulic press slider. In order to improve the convergence speed and precision of ILC, a fuzzy ILC algorithm that utilizes the fuzzy strategy to adaptively adjust the iterative learning gains is put forward. The simulation and experimental researches are carried out to investigate the convergence speed and precision of the fuzzy ILC for hydraulic press slider position tracking. The results show that the fuzzy ILC can raise the iterative learning speed enormously, and realize the tracking control of slider displacement curve with rapid response speed and high control precision. In experiment, the maximum tracking error 0.02 V is achieved through 12 iterations only.

30 citations


Cites methods from "Iterative learning control for posi..."

  • ...Circular trajectory tracking of Pneumatic X-Y table was accomplished by using ILC [10]....

    [...]

Journal ArticleDOI
Seung Ho Cho1
TL;DR: In this paper, the use of Neural Network based PID control scheme in order to assure good tracking performance of a pneumatic X-Y table has been discussed, where a self-excited oscillation method is applied to derive the dynamic design parameters of a linear model.
Abstract: This paper deals with the use of Neural Network based PID control scheme in order to assure good tracking performance of a pneumatic X-Y table. Pneumatic servo systems have inherent nonlinearities such as compressibility of air and nonlinear frictions present in cylinder. The conventional PID controller is limited in some applications where the affection of nonlinear factor is dominant. In order to track the reference model output, the primary control function is provided by the PID control and then the auxiliary control function is given by neural network for learning and compensating the inherent nonlinearities, A self-excited oscillation method is applied to derive the dynamic design parameters of a linear model. The experiment using the proposed control scheme has been performed and a significant reduction in tracking error is achieved.

22 citations

References
More filters
Journal ArticleDOI
TL;DR: A betterment process for the operation of a mechanical robot in a sense that it betters the nextoperation of a robot by using the previous operation's data is proposed.
Abstract: This article proposes a betterment process for the operation of a mechanical robot in a sense that it betters the next operation of a robot by using the previous operation's data. The process has an iterative learning structure such that the (k + 1)th input to joint actuators consists of the kth input plus an error increment composed of the derivative difference between the kth motion trajectory and the given desired motion trajectory. The convergence of the process to the desired motion trajectory is assured under some reasonable conditions. Numerical results by computer simulation are presented to show the effectiveness of the proposed learning scheme.

3,222 citations

Book
08 Jul 1998
TL;DR: This chapter discusses Iterative Learning Control with Non-Standard Assumptions Applied to the Control of Gas-Metal Arc Welding and its Extension to a Class of Nonlinear Systems.
Abstract: List of Figures. List of Tables. Preface. Part I: General Introduction to Iterative Learning Control. 1. A Brief History of Iterative Learning Control S. Arimoto. 2. The Frontiers of Iterative Learning Control Jian-Xin Xu, Zenn Z. Bien. Part II: Property Analysis of Iterative Learning Control. 3. Robustness and Convergence of a PD-type Iterative Learning Controller Hak-Sung Lee, Zeungnam Bien. 4. Ability of Learning Comes from Passivity and Dissipativity of System Dynamics S. Arimoto. 5. On the Iterative Learning Control of Sampled-Data Systems Chiang-Ju Chien. 6. High-Order Iterative Learning Control of Discrete-Time Nonlinear Systems Using Current Iteration Tracking Error Yangquan Chen, et al. Part III: The Design Issues of Iterative Learning Control. 7. Designing Iterative Learning and Repetitive Controllers R.W. Longman. 8. Design of an ILC for Linear Systems with Time-Delay and Initial State Error Kwang-Hyun Park, et al. 9. Design of Quadratic Criterion-Based Iterative Learning Control Kwang Soon Lee, J.H. Lee. 10. Robust ILC with Current Feedback for Uncertain Linear Systems Tae-Yong Doh, Myung Jin Chung. Part IV: Integration of Iterative Learning Control with Other Intelligent Controls. 11. Model Reference Learning Control with a Wavelet Network M. Fukuda, S. Shin. 12. Neural-Based Iterative Learning Control Jin Young Choi, et al. 13. Adaptive Learning Control of Robotic Systems and Its Extension to a Class of Nonlinear Systems B.H. Park, et al. 14. Direct Learning Control of Non-Uniform Trajectories Jian-Xin Xu, Yanbin Song. 15. System Identification and Learning Control M.Q. Phan, J.A. Frueh. Part V: Implementations of Iterative Learning Control Method. 16. Model-Based Predictive Control Combined with Iterative Learning for Batch or Repetitive Processes Kwang Soon Lee, J.H. Lee. 17. Iterative Learning Control with Non-Standard Assumptions Applied to the Control of Gas-Metal Arc Welding K.L. Moore, A. Matheus. 18. Robust Control of Functional Neuromuscular Stimulation System by Discrete-time Iterative Learning Huifang Dou, et al. Index. About the Editors.

451 citations

Journal ArticleDOI
TL;DR: An algorithm is presented for iterative learning of the control input for a linear discrete-time multivariable system and its synthesis and analysis are based on two-dimensional system theory.
Abstract: An algorithm is presented for iterative learning of the control input for a linear discrete-time multivariable system. Necessary and sufficient conditions are stated for convergence of the proposed algorithm. The algorithm synthesis and analysis are based on two-dimensional (2-D) system theory. A numerical example is given. >

363 citations

Journal ArticleDOI
TL;DR: An algorithm for iterative learning control is developed on the basis of an optimization principle which has been used previously to derive gradient-type algorithms and has numerous benefits which include realization in terms of Riccati feedback and feedforward components.
Abstract: An algorithm for iterative learning control is developed on the basis of an optimization principle which has been used previously to derive gradient-type algorithms. The new algorithm has numerous benefits which include realization in terms of Riccati feedback and feedforward components. This realization also has the advantage of implicitly ensuring automatic step size selection and hence guaranteeing convergence without the need for empirical choice of parameters. The algorithm is expressed as a very general norm optimization problem in a Hilbert space setting and hence, in principle, can be used for both continuous and discrete time systems. A basic relationship with almost singular optimal control is outlined. The theoretical results are illustrated by simulation studies which highlight the dependence of the speed of convergence on parameters chosen to represent the norm of the signals appearing in the optimization problem.

308 citations

Journal ArticleDOI
01 Oct 1998
TL;DR: It is shown with theory and experiments that pneumatic actuators can rival the performance of more common electric actuators and be controlled without the need for an expensive force/torque sensor usually required by electric motors driven systems.
Abstract: Focuses on modeling and control of a light-weight and inexpensive pneumatic robot that can be used for position tracking and for end-effector force control. Unlike many previous controllers, our approach more fully accounts for the nonlinear dynamic properties of pneumatic systems such as servovalve flow characteristics and the thermodynamic properties of air compressed in a cylinder. We show with theory and experiments that pneumatic actuators can rival the performance of more common electric actuators. Our pneumatic robot is controlled by extending existing manipulator control algorithms to handle the nonlinear flow and compressibility of air. The control approach uses the triangular form of the coupled rigid body and air flow dynamics to establish path tracking. In addition to the trajectory tracking control law, a hybrid position/force control algorithm is developed. The experimental results indicate that the tip forces on the robot can be controlled without the need for an expensive force/torque sensor usually required by electric motors driven systems.

178 citations