IEEE Organizations related to Iterative Learning Control

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Conferences related to Iterative Learning Control

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2021 IEEE International Conference on Mechatronics (ICM)

CM focuses on recent developments and future prospects related to the synergetic integration of mechanics, electronics, and information processing.


2020 American Control Conference (ACC)

The ACC is the annual conference of the American Automatic Control Council (AACC, the U.S. national member organization of the International Federation for Automatic Control (IFAC)). The ACC is internationally recognized as a premier scientific and engineering conference dedicated to the advancement of control theory and practice. The ACC brings together an international community of researchers and practitioners to discuss the latest findings in automatic control. The 2020 ACC technical program will

  • 1996 13th Triennial World Congress of the International Federation of Automatic Control (IFAC)

  • 1997 American Control Conference - ACC '97

  • 1998 American Control Conference - ACC '98

  • 1999 American Control Conference - ACC '99

  • 2000 American Control Conference - ACC 2000

  • 2001 American Control Conference - ACC 2001

  • 2002 American Control Conference - ACC 2002

  • 2003 American Control Conference - ACC 2003

  • 2004 American Control Conference - ACC 2004

  • 2005 American Control Conference - ACC 2005

  • 2006 American Control Conference - ACC 2006 (Silver Anniversary)

  • 2007 American Control Conference - ACC 2007

  • 2008 American Control Conference - ACC 2008

  • 2009 American Control Conference - ACC 2009

    The 2009 ACC technical program will cover new developments related to theory, application, and education in control science and engineering. In addition to regular technical sessions the program will also feature interactive and tutorial sessions and preconference workshops.

  • 2010 American Control Conference - ACC 2010

    Theory and practice of automatic control

  • 2011 American Control Conference - ACC 2011

    ACC provides a forum for bringing industry and academia together to discuss the latest developments in the area of Automatic Control Systems, from new control theories, to the advances in sensors and actuator technologies, and to new applications areas for automation.

  • 2012 American Control Conference - ACC 2012

    All areas of control engineering and science.

  • 2013 American Control Conference (ACC)

    Control systems theory and practice. Conference themes on sustainability, societal challenges for control, smart healthcare systems. Conference topics include biological systems, vehicle dynamics and control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2014 American Control Conference - ACC 2014

    All areas of the theory and practice of automatic control, including but not limited to network control systems, model predictive control, systems analysis in biology and medicine, hybrid and switched systems, aerospace systems, power and energy systems and control of nano- and micro-systems.

  • 2015 American Control Conference (ACC)

    control theory, technology, and practice

  • 2016 American Control Conference (ACC)

    Control systems theory and practice. Conference topics include biological systems, vehicle dynamics and control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2017 American Control Conference (ACC)

    Technical topics include biological systems, vehicle dynamics and control, adaptive control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2018 Annual American Control Conference (ACC)

    Technical topics include biological systems, vehicle dynamics and control, adaptive control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2019 American Control Conference (ACC)

    Technical topics include biological systems, vehicle dynamics and control, adaptive control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.


2020 59th IEEE Conference on Decision and Control (CDC)

The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2019 Chinese Control And Decision Conference (CCDC)

Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2008 Chinese Control and Decision Conference (CCDC)

  • 2009 Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2010 Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies

  • 2011 23rd Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2012 24th Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2013 25th Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2014 26th Chinese Control And Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create aforum for scientists, engineers and practitioners throughout the world to present the latestadvancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2015 27th Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2016 Chinese Control and Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create aforum for scientists, engineers and practitioners throughout the world to present the latestadvancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2017 29th Chinese Control And Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.

  • 2018 Chinese Control And Decision Conference (CCDC)

    Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.


2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)

Electrical Engineering, Academic and Industrial



Periodicals related to Iterative Learning Control

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Most published Xplore authors for Iterative Learning Control

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Xplore Articles related to Iterative Learning Control

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Adaptive iterative learning control of nonlinear systems with hysteresis

2016 35th Chinese Control Conference (CCC), 2016

An adaptive iterative learning controller (ILC) is proposed for a class of nonlinear systems with hysteresis described by Bouc-Wen model. First, the property of Bouc-Wen mode is discussed and the upper bound is derived .Then an iterative learning controller is proposed through the Lyapunov-like function. This controller can eliminate the oscillation and overshoot caused by hysteresis effectively. Finally, simulation results ...


Direct learning control of trajectories subject to high-order internal model for a class of continuous-time linear systems

2017 11th Asian Control Conference (ASCC), 2017

Direct learning control (DLC) is a control method using pre-stored system information to determine the current system input. It has been applied in the case that the system plant is in somewhere similar to former systems, for example, in output reference trajectories. Therefore, new control input can be deduced directly from system information stored a priori according to the known ...


Structured digital predistorter model derivation based on iterative learning control

2016 46th European Microwave Conference (EuMC), 2016

A novel approach to derive model structures for digital predistorters based on iterative learning control (ILC) is presented. ILC is a technique used to identify the optimal PA input signal/predistorted signal that drives a PA to a desired output response. The ILC concept is used to derive an analytical expression of the predistorted signal and to identify basis functions for ...


Data-driven adaptive iterative learning predictive control

2017 6th Data Driven Control and Learning Systems (DDCLS), 2017

A new data-driven predictive iterative learning control(ILC) is proposed for same category discrete nonlinear systems in this work. The controller design only depends on the input/output data of the system and does not need explicit mathematical model. More prediction information along the iteration axis is utilized in the learning control law to improve the control performance. The applicability of the ...


Iterative learning control of a minimal half-center oscillator

2016 12th World Congress on Intelligent Control and Automation (WCICA), 2016

Half-center oscillator is one form of central pattern generators, which could generate rhythmic motor patterns. In this paper, we create a minimal half- center oscillator composed of two Izhikevich models connected via chemical inhibitory synapses, and use closed-loop iterative learning control algorithm to regularize the bursting firing activity of the half-center oscillator through adapting one of maximal synaptic conductance. Simulation ...



Educational Resources on Iterative Learning Control

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IEEE-USA E-Books

  • Adaptive iterative learning control of nonlinear systems with hysteresis

    An adaptive iterative learning controller (ILC) is proposed for a class of nonlinear systems with hysteresis described by Bouc-Wen model. First, the property of Bouc-Wen mode is discussed and the upper bound is derived .Then an iterative learning controller is proposed through the Lyapunov-like function. This controller can eliminate the oscillation and overshoot caused by hysteresis effectively. Finally, simulation results are provided to illustrate the effectiveness of the proposed controller.

  • Direct learning control of trajectories subject to high-order internal model for a class of continuous-time linear systems

    Direct learning control (DLC) is a control method using pre-stored system information to determine the current system input. It has been applied in the case that the system plant is in somewhere similar to former systems, for example, in output reference trajectories. Therefore, new control input can be deduced directly from system information stored a priori according to the known relationship. In this paper, considering multiple time-varying unknown parameters, a DLC law is proposed for a class of continuous-time linear systems with non-repeatable problems. The previously control profiles which are generated for non-repeatable trajectories were stored in the system already. The newly given reference trajectory is correlated with the pre- stored reference trajectories in terms of high-order internal model (HOIM). It is shown by theoretic proof and illustrative example of single-link robot manipulator that DLC algorithm can be effectively applied to the tracking trajectory by making full use of the pre-stored control information directly.

  • Structured digital predistorter model derivation based on iterative learning control

    A novel approach to derive model structures for digital predistorters based on iterative learning control (ILC) is presented. ILC is a technique used to identify the optimal PA input signal/predistorted signal that drives a PA to a desired output response. The ILC concept is used to derive an analytical expression of the predistorted signal and to identify basis functions for predistorter models. The proposed approach is used to derive a predistorter model structure from the memory polynomial model. Experimental results show that the predistorter model derived with the proposed approach can obtain better linearity performance than conventional models used in digital predistortion.

  • Data-driven adaptive iterative learning predictive control

    A new data-driven predictive iterative learning control(ILC) is proposed for same category discrete nonlinear systems in this work. The controller design only depends on the input/output data of the system and does not need explicit mathematical model. More prediction information along the iteration axis is utilized in the learning control law to improve the control performance. The applicability of the proposed methods is proved by simulation experiments.

  • Iterative learning control of a minimal half-center oscillator

    Half-center oscillator is one form of central pattern generators, which could generate rhythmic motor patterns. In this paper, we create a minimal half- center oscillator composed of two Izhikevich models connected via chemical inhibitory synapses, and use closed-loop iterative learning control algorithm to regularize the bursting firing activity of the half-center oscillator through adapting one of maximal synaptic conductance. Simulation results show that closed-loop iterative learning control not only can rapidly find the best coupling strength that leads to the regularization goal but also change the coupling strength automatically according to different control precisions.

  • Chaos synchronization between Josephson junction and classical chaotic system via iterative learning control

    An application of Iterative Learning Control Theory (ILC) for system synchronization exhibited is in this study. The drive system is the Rossler system in this study is a macroscopic system. Another system is a mesoscopic system containing the resistive-capacitive-inductance shunted Josephson junctions (RCL-shunted JJ) to be the response system in this study. The ILC method is important to research for the robotics development in 1984. Many studies, such as image identification, adaptive control, sliding mode control and secure communication are gradually employed. The resistive-capacitive- inductance shunted Josephson junctions (RCL-shunted JJ) and resistive- capacitive shunted Josephson junctions (RCS JJ) are essentially nonlinear dynamical systems in a superconductor device could be used to synchronize drive-response system of chaos and chaos-based secure communication. The iterative error between the drive and response system influences the synchronization of systems. The Lyapunov criteria are still satisfied the mesoscopic, and the tracking error dynamics could be bounded and converged. There are also to exhibit system synchronized will be Lyapunov stable for which based on to designate appropriated learning the law. The simulation results demonstrate the consistent with the concept of proposing the controlled learning law and scheme.

  • Multi‐agent Consensus Tracking with Input Sharing by Iterative Learning Control

    A novel input sharing ILC is developed. It allows agents to share learned information among neighbors, which improves convergence speed and also smooths transient performance.

  • Iterative learning control algorithm based on Chebyshev orthonormal basis for nonlinear systems

    A new iterative learning control algorithm with global convergence for nonlinear systems is presented. Constructed Chebyshev orthornormal polynomial basis in the control space, the iterative learning control problem is transformed as the optimization problem. The iterative projection method is utilized to solve this problem so that the new iterative learning control law is derived. Based on the extension method, a new algorithm with global convergence for nonlinear iterative learning systems is developed. Sufficient conditions of convergence of this approach are given and the global convergence is proved. Such an algorithm has the advantage of the simple computation and arbitrarily chosen initial control.

  • Iterative learning control for switched linear parabolic systems in space W1, 2

    This paper deals with the problem of iterative learning control for a class of switched linear parabolic systems in space W1,2. Here, the considered switched systems with arbitrary switching rules are operated during a finite time interval repetitively. According to the characteristics of the systems, iterative learning control laws are proposed for such switched parabolic systems based on the P-type learning algorithm. Using the contraction mapping method, it is shown that the algorithm can guarantee the output tracking errors on W1,2 space converge along the iteration axis. A simulation example illustrates the effectiveness of the proposed algorithm.

  • A novel design of iterative learning control with feedback structure

    The purpose of this work is to attain a unified design framework of PID-like ILC with pure feedback structure for a class of nonlinear repetitive NARMA systems. To serving the controller design and analysis, the nonlinear NARMA system is transferred into a linear data model. Then, we suppose that there exists a desired nonlinear controller such that the system output tracks the desired output exactly. By using the mean-values theorem, two time-dynamical linearization methods are proposed for the ideal nonlinear controller and a CFDL-ILC and a PFDL-ILC are presented, respectively. Comparatively, the PFDL- ILC can use more tracking errors of previous time instants of the same iteration and thus may achieve a better control performance. However, the trade off is that the computation may become more complex. The proposed approaches are data-driven and no process model is required for the controller design and analysis. The availability of the proposed approaches is further confirmed by simulation results.



Standards related to Iterative Learning Control

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No standards are currently tagged "Iterative Learning Control"