The Experts below are selected from a list of 13875 Experts worldwide ranked by ideXlab platform
Jing Li - One of the best experts on this subject based on the ideXlab platform.
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PID Controller Parameter Tuning Based on Iterative Learning Control
Advanced Materials Research, 2010Co-Authors: Jing Tang, Yun'an Hu, Jing LiAbstract:On the basis of sufficient analysis to the characteristic of Iterative Learning Control and PID Controller parameter tuning, an idea of applying Iterative Learning Control to PID Controller parameter tuning was aroused in this paper, we choose the linear model around the character point of Roll Attitude Controller as the research plant, transforming the problem of PID Controller parameter tuning into the problem of open-close loop Iterative Learning Control problem. The stability of PID Controller parameter Iterative Learning Control system and the astringency of Controller parameters were verified for the first time through the construct of a compress mapping arithmetic operator, and a novel stop condition design scheme of integral type is advanced at the same time, the Iterative Learning of PID Controller parameter was then successfully solved. The ultimate simulation study validates the correctness and the effectiveness of the theory.
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Missile PID Controller parameter tuning based on Iterative Learning Control
2010 2nd International Conference on Signal Processing Systems, 2010Co-Authors: Jing Tang, Yun'an Hu, Zhicai Xiao, Jing LiAbstract:On the basis of sufficient analysis to the characteristic of Iterative Learning Control and PID Controller parameter tuning, an idea of applying Iterative Learning Control to PID Controller parameter tuning was aroused in this paper, we choose the linear model around the character point of Missile Roll Attitude Controller as the research plant, transforming the problem of PID Controller parameter tuning into the problem of open-close loop Iterative Learning Control problem. The stability of PID Controller parameter Iterative Learning Control system and the astringency of Controller parameters were verified for the first time through the construct of a compress mapping arithmetic operator, and a novel stop condition design scheme of integral type is advanced at the same time, the Iterative Learning of PID Controller parameter was then successfully solved. The ultimate simulation study validates the correctness and the effectiveness of the theory.
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Iterative Learning Control based PID Controller parameter tuning scheme design and stability analysis
2010 Chinese Control and Decision Conference, 2010Co-Authors: Yun'an Hu, Jing LiAbstract:On the basis of sufficient analysis on the characteristic of Iterative Learning Control and PID Controller parameter tuning work, an idea of applying Iterative Learning Control to PID Controller parameter tuning was aroused. We choose the linear model in the neighborhood of character points as the research plant, transforming the problem of PID Controller parameter tuning into an open-close loop Iterative Learning Control problem. The stability of PID Controller parameter Iterative Learning Control system and the astringency of Controller parameters was verified for the first time through the construct of a compress mapping arithmetic operator. The stop condition design scheme of integral type is advanced at the same time, finally, the Iterative Learning of PID Controller parameters was solved successfully. The ultimate simulation study validates the correctness and effectiveness of the theory.
Senping Tian - One of the best experts on this subject based on the ideXlab platform.
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Iterative Learning Control for a class of singular impulsive systems
International Journal of Systems Science, 2018Co-Authors: Senping Tian, Panpan GuAbstract:In this paper, the Iterative Learning Control problem for a class of nonlinear singular impulsive systems is discussed. Then, a D-type (derivative-type) Iterative Learning Control algorithm is presented such that the output tracks the desired output trajectory as accurate as possible. Furthermore, the sufficient condition for the convergence of the proposed algorithm is established in detail. Finally, a numerical example is included to corroborate the theoretical analyses.
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Iterative Learning Control for switched singular systems
2017 6th Data Driven Control and Learning Systems (DDCLS), 2017Co-Authors: Panpan Gu, Senping TianAbstract:In this paper, the problem of Iterative Learning Control is considered for a class of switched singular systems. And the considered switched singular systems with arbitrary switching rules are operated in a fixed time interval repetitively. Based on the singular value decomposition method, the switched singular systems are transformed into the switched differential-algebraic systems. Then an Iterative Learning Control algorithm, which is composed of D-type and P-type Learning algorithms, is proposed. Using the contraction mapping principle, it is shown that the algorithm can guarantee the state tracking error to converge uniformly to zero as the iteration increases. Finally, a numerical example is constructed to illustrate the effectiveness of the presented algorithm.
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Iterative Learning Control for distribute parameter systems with time-delay
2011 Chinese Control and Decision Conference (CCDC), 2011Co-Authors: Senping TianAbstract:In this paper, An Iterative Learning Control problem for distributed parameter systems with time-delay is discussed. Based on P-type Iterative Learning Control method, sufficient conditions for the convergence of systems are given by employing special (L2, λ) norm.
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A new Iterative Learning Control algorithm for distributed parameter systems
2009 Chinese Control and Decision Conference, 2009Co-Authors: Li Zheng, Senping Tian, Huiping TianAbstract:An Iterative Learning Control problem for distributed parameter systems is discussed. And a new Iterative Learning Control algorithm is proposed, which is different from the present algorithms and has the form of nonlinear. Furthermore, geometry explanation is given for the new algorithm in the paper.
Jianxin Xu - One of the best experts on this subject based on the ideXlab platform.
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On initial conditions in Iterative Learning Control
2006 American Control Conference, 2006Co-Authors: Jianxin Xu, Yangquan ChenAbstract:Initial conditions, or initial resetting conditions, play a fundamental role in all kinds of Iterative Learning Control methods. In this work we study five different initial conditions, disclose the inherent relationship between each initial condition and corresponding Learning convergence (or boundedness) property. The Iterative Learning Control method under consideration is based on Lyapunov theory, which is suitable for plants with time varying parametric uncertainties and local Lipschitz nonlinearities.
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on initial conditions in Iterative Learning Control
IEEE Transactions on Automatic Control, 2005Co-Authors: Jianxin XuAbstract:Initial conditions, or initial resetting conditions, play a fundamental role in all kinds of Iterative Learning Control methods. In this note, we study five different initial conditions, disclose the inherent relationship between each initial condition and corresponding Learning convergence (or boundedness) property. The Iterative Learning Control method under consideration is based on Lyapunov theory, which is suitable for plants with time-varying parametric uncertainties and local Lipschitz nonlinearities.
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linear and nonlinear Iterative Learning Control
2003Co-Authors: Jianxin XuAbstract:Introduction.- Robust Optimal Design for the First Order Linear-type ILC Scheme.- Analysis of Higher Order Linear-type ILC Schemes.- Linear ILC Design for MIMO Dynamics Systems.- Nonlinear-type ILC Schemes.- Nonlinear ILC Design for MIMO Dynamic Schemes.- Composite Energy Function Based Learning Control.- Quasi-Optimal Iterative Learning Control.- Learning Wavelet Control Using Constructive Wavelet Networks.- Conclusions and Recommendations.
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Iterative Learning Control analysis design integration and applications
1998Co-Authors: Zeungnam Bien, Jianxin XuAbstract: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.
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analysis of Iterative Learning Control for a class of nonlinear discrete time systems
Automatica, 1997Co-Authors: Jianxin XuAbstract:Abstract An output feedback Iterative Learning Control scheme is proposed for a class of nonlinear discrete-time systems in the presence of direct transmission from system inputs to outputs. The convergence condition of the Iterative Learning Control scheme is analyzed. The effectiveness of suggested discrete-time Iterative Learning Control algorithm is demonstrated with a single-link robotic manipulator as the illustrative example. © 1997 Elsevier Science Ltd.
Yun'an Hu - One of the best experts on this subject based on the ideXlab platform.
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PID Controller Parameter Tuning Based on Iterative Learning Control
Advanced Materials Research, 2010Co-Authors: Jing Tang, Yun'an Hu, Jing LiAbstract:On the basis of sufficient analysis to the characteristic of Iterative Learning Control and PID Controller parameter tuning, an idea of applying Iterative Learning Control to PID Controller parameter tuning was aroused in this paper, we choose the linear model around the character point of Roll Attitude Controller as the research plant, transforming the problem of PID Controller parameter tuning into the problem of open-close loop Iterative Learning Control problem. The stability of PID Controller parameter Iterative Learning Control system and the astringency of Controller parameters were verified for the first time through the construct of a compress mapping arithmetic operator, and a novel stop condition design scheme of integral type is advanced at the same time, the Iterative Learning of PID Controller parameter was then successfully solved. The ultimate simulation study validates the correctness and the effectiveness of the theory.
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Missile PID Controller parameter tuning based on Iterative Learning Control
2010 2nd International Conference on Signal Processing Systems, 2010Co-Authors: Jing Tang, Yun'an Hu, Zhicai Xiao, Jing LiAbstract:On the basis of sufficient analysis to the characteristic of Iterative Learning Control and PID Controller parameter tuning, an idea of applying Iterative Learning Control to PID Controller parameter tuning was aroused in this paper, we choose the linear model around the character point of Missile Roll Attitude Controller as the research plant, transforming the problem of PID Controller parameter tuning into the problem of open-close loop Iterative Learning Control problem. The stability of PID Controller parameter Iterative Learning Control system and the astringency of Controller parameters were verified for the first time through the construct of a compress mapping arithmetic operator, and a novel stop condition design scheme of integral type is advanced at the same time, the Iterative Learning of PID Controller parameter was then successfully solved. The ultimate simulation study validates the correctness and the effectiveness of the theory.
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Iterative Learning Control based PID Controller parameter tuning scheme design and stability analysis
2010 Chinese Control and Decision Conference, 2010Co-Authors: Yun'an Hu, Jing LiAbstract:On the basis of sufficient analysis on the characteristic of Iterative Learning Control and PID Controller parameter tuning work, an idea of applying Iterative Learning Control to PID Controller parameter tuning was aroused. We choose the linear model in the neighborhood of character points as the research plant, transforming the problem of PID Controller parameter tuning into an open-close loop Iterative Learning Control problem. The stability of PID Controller parameter Iterative Learning Control system and the astringency of Controller parameters was verified for the first time through the construct of a compress mapping arithmetic operator. The stop condition design scheme of integral type is advanced at the same time, finally, the Iterative Learning of PID Controller parameters was solved successfully. The ultimate simulation study validates the correctness and effectiveness of the theory.
Eric Rogers - One of the best experts on this subject based on the ideXlab platform.
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Iterative Learning Control—An Overview
Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation, 2015Co-Authors: Chris Freeman, Eric Rogers, Jane Burridge, Ann-marie Hughes, Katie MeadmoreAbstract:This chapter gives the required background on Iterative Learning Control. After introducing the defining characteristic of this form of Control, attention is restricted to the laws used in the stroke rehabilitation research.
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ISIC - Iterative Learning Control under parameter uncertainty and failures
2012 IEEE International Symposium on Intelligent Control, 2012Co-Authors: Pavel Pakshin, Julia Emelianova, Krzysztof Galkowski, Eric RogersAbstract:This paper develops new results on the design of Iterative Learning Control schemes using a repetitive process setting for analysis. Iterative Learning Control has been developed as a technique for Controlling systems which are required to repeat the same operation over a finite duration known as the trial duration, or length, and information from previous executions is used to update the Control input for the next one and thereby sequentially improve performance. This paper considers the design of Iterative Learning Control laws for plants modeled by linear discrete systems with uncertain parameters and possible failures. Using a Lyapunov function approach both state and output feedback based schemes are developed.
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Measuring the Performance of Iterative Learning Control Systems
Proceedings of the 2005 IEEE International Symposium on Mediterrean Conference on Control and Automation Intelligent Control 2005., 2005Co-Authors: J D Ratcliffe, Paul Lewin, Eric Rogers, Jari Hätönen, T J Harte, David H. OwensAbstract:Iterative Learning Control has the potential to significantly improve the tracking performance of repeating trajectory Control systems. However, until now little attempt has been made to measure this performance quantitatively. A new Iterative Learning Control performance index PIN is introduced which allows direct, quantitative, performance comparison of different algorithms, or alternatively a single algorithm which has variable tuning parameters. In particular, PI N can be used as a tool for selecting and adjusting algorithm tuning parameters. Application of the new performance index is demonstrated with both simulation studies and practical implementation on a gantry robot
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Iterative Learning Control — 2D Control systems from theory to application
International Journal of Control, 2004Co-Authors: T. Al-towaim, Paul Lewin, Eric Rogers, A D Barton, David H. OwensAbstract:It has long been recognised that Iterative Learning Control is a 2D system, i.e. information propagation occurs in two independent directions. In this paper, the application of so-called norm optimal Iterative Learning Control, which has its origins in the theory of the class of 2D systems known as linear repetitive processes to an experimental testbed in the form of a chain conveyor system is reported. This includes the motivation for applying Iterative Learning Control to such systems, the design and construction of the testbed, and its use to demonstrate that norm optimal Iterative Learning Control gives superior performance over alternatives. As such, it provides an application for 2D systems theory where distinct advantages arise from using such a setting for modelling and Control.
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Systems Structure in Iterative Learning Control
IFAC Proceedings Volumes, 1995Co-Authors: David H. Owens, N. Amann, Eric RogersAbstract:Abstract The paper reviews recent results in the area of Iterative Learning Control with particular emphasis on its 2D structure and the effect of systems structural properties on convergence. Particular attention is focussed on high-gain concepts and H ∞ and LQ optimisation approaches.
