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Iterative learning of impedance control
Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289), 2003This paper proposes an iterative learning control scheme for impedance control of robotic tasks when the tool endpoint covered by soft and deformable material presses a rigid object or environment at a prescribed periodic force pattern. To this end, an iterative learning control scheme for a class of linear dynamical systems with a negative feedback ...
Pham Thuc Anh Nguyen +3 more
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Iterative Learning in Ballistic Control
2007 American Control Conference, 2007In this paper, we formulate and explore the characteristics of iterative learning in ballistic control problems. The ILC theory provides a suitable framework for derivations and analysis of ballistic control under learning process. To overcome the obstacles caused by uncertain gradient and redundant control input, we incorporate extra trials into ...
Jian-Xin Xu 0001 +2 more
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Predictive Iterative Learning Control
Intelligent Automation & Soft Computing, 2003There are applications in which the control system is commanded to execute identical and repetitive tasks. These include pick-and-place robot control, moulding control, and many semiconductor processes. The iterative learning control (ILC) method first introduced by Arimoto et al. (1984) is based on the previous control history and a learning mechanism.
Sunan Huang 0001, Ser Yong Lim
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Intermittent iterative learning control
2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, 2006In this paper we present a mathematical formulation of the problem of robust iterative learning control (ILC) design when the system is subject to data dropout. It is assumed that an ILC scheme is implemented via a networked control system (NCS) and that during the data transfer from the remote plant to the ILC controller data dropout occurs, resulting
Hyo-sung Ahn, Yangquan Chen, Kevin Moore
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Iterative Learning Control — An Optimization Paradigm
Annual Reviews in Control, 2005J Hätönen, D H Owens
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Parameter optimisation in iterative learning control
2003 European Control Conference (ECC), 2003In this paper parameter optimization through a quadratic performance index is introduced as a method to establish a new iterative learning control law. With this new algorithm, monotonic convergence of the error to zero is guaranteed if the original system is a discrete-time LTI system and it satisfies a positivity condition.
David H. Owens 0001, K. Feng
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An iterative learning controller for nonholonomic robots
Proceedings of IEEE International Conference on Robotics and Automation, 2002We present an iterative learning controller for nonholonomic systems in chained form. The learning algorithm relies on the fact that chained-form systems are linear under piecewise-constant inputs. The proposed control scheme requires the execution of a small number of experiments in order to drive the system to the desired state in finite time, with ...
ORIOLO, Giuseppe +2 more
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An iterative learning controller with initial state learning
IEEE Transactions on Automatic Control, 1999In iterative learning control (ILC), a common assumption is that the initial states in each repetitive operation should be inside a given ball centred at the desired initial states which may be unknown. This assumption is critical to the stability analysis, and the size of the ball will directly affect the final output trajectory tracking errors.
YangQuan Chen +3 more
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An iteration-domain filter for controlling transient growth in Iterative Learning Control
Proceedings of the 2010 American Control Conference, 2010Transient growth is a problem in Iterative Learning Control (ILC) in which the tracking error temporarily grows very large during the learning process, before converging to a small value. While some ILC algorithms can guarantee monotonic convergence, there are limitations when the model is uncertain.
Qing Liu, Douglas A. Bristow
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On initial conditions in iterative learning control
2006 American Control Conference, 2006Initial 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.
Jian-Xin Xu 0001, Rui Yan, YangQuan Chen
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