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TC 1.2. Adaptive and Learning Systems

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This Technical Committee addresses all control problems involving system models that are subject to large size uncertainties and seeks solutions where model uncertainty is compensated for using adaptation and learning rules. It facilitates migrating intelligence into adaptive and learning systems.


Technical Committee 1.2 develops methods for designing and analyzing adaptive and learning systems, including:

• Adaptive Estimators and Optimizers

• Adaptive Predictors and Filters

• Adaptive Observers for Linear and Nonlinear Systems

• Adaptive Observers for Finite- and Infinite-Dimensional Systems

• Sampled-data and Networked Adaptive Observers

• Adaptive Controllers for Linear and Nonlinear Systems

• Adaptive Controllers for Finite- and Infinite-Dimensional Systems

• Robust and Nonlinear Adaptive Controllers

• Output-Feedback Adaptive Controllers

• Self-Tuning and Stochastic Adaptive Controllers

• Gain-scheduling Adaptive Systems

• Multiple-Model and Switched Adaptive Systems

• Fault Detection and Fault-Tolerant Control Systems

• Intelligent and Knowledge-based Adaptive Systems

• Iterative learning and repetitive control Systems

• Reinforcement Learning Systems

• Agent-based Control Systems


Technical Committee 1.2 applies the developed methods in a wide range of engineering areas, including:

• Aeronautics and Aerospace

• Transportation and Automotive Systems

• Power and Energy systems

• Networked and Communication systems

• Speech and Audio processing

• Sonar and Radar

• Medical and Biomedical Engineering

• Robotics and Mechatronic Systems

• Process Engineering and Industrial Manufacturing

• Mining and Minerals Applications

• Components and instruments

TC 1.2. Adaptive and Learning Systems

On Adaptive and Learning Systems

Welcome Message from the Chair

The complexity of Control Problems lies, at least partly, in the fact that the models used in control design are subject to uncertainty. This uncertainty reflects the impossibility, for most real-life systems, to have their dynamics perfectly described with tractable mathematical models. In effect, physical systems may involve highly nonlinear, time-varying, or infinite dimension dynamics. They also are usually affected, in a more or less complex way, by random exogenous disturbances. On the other hand, in control design, relatively simple linear or nonlinear models (reduced-order, deterministic ...) are deliberately preferred, due to theoretical considerations and implementation constraints. Therefore, one of the fundamental problems in control theory is: how to achieve and maintain a high level of control performance despite large model uncertainty? Adaptive and Learning Control offers solutions to this problem. This approach formulates model uncertainty in terms of parameter uncertainty and provides a set of techniques to deal with. Making use of these techniques, controllers can be designed featuring the capability of real time self-adjustment. To this end, Adaptive and Learning controllers contain specific components such as online parameter estimators, adaptive observers, adaptive predictors, adaptive filters, ....

Adaptive and Learning Control attracts a long standing interest of most active researchers in systems and control. Keeping yourself at the cutting edge of this aspect of modern control science would be an important advantage this Technical Committee would provide you with, no matter are you a young or an experienced researcher.

If you are a practitioner working with process control, power and energy system control, manufacturing systems, transportation systems, networked systems, robotics, and mechatronics, then adaptive and learning systems may help you to solve challenging problems of estimation and control under conditions of uncertainty and lack of information.

Fouad Giri

University of Caen, France

Members - TC Roster

Chair: Fouad Giri



Area Adaptive Systems: Anuradha Annaswamy

Area Learning Systems: Eric Rogers

Education-Liaison: Alexander Fradkov


Industry-Liaison: Eugene Lavretsky


IFAC Social Media-Liaision: Travis Gibson


TC Media-Liaison: Christopher Freeman



Ahmed-Ali Tarek
Alessandri Angelo
Andrievsky Boris
Annaswamy Anuradha
Aranovskiy Stanislav
Ariyur Kartik B.
Astolfi Alessandro
Back Juhoon
Balakrishnan S.N.
Baldi Simone
Banyasz Csilla
Barkana Itzhak
Besançon Gildas
Bittanti Sergio
Bresch-Pietri Delphine
Cimen Tayfun
Ding Zhengtao
Duncan Tyrone
Efe Mehmet Onder
Efimov Denis
Fidan Baris
Fradkov Alexander
Freeman Christopher
Furtat Igor
Gibson Travis
Giri Fouad
Guo Bao-zhu
Hayakawa Tomohisa
Hou Zhongsheng
Ikhouane Fayçal
Ioannou Petros
Jafari Saeid
Kaynak Okyay
Keviczky Laszlo
Krstic Miroslav
Laila Dina Shona
Lavretsky Eugene
Mboup Mamadou
Miyasato Yoshihiko
M'Saad Mohammed
Nijmeijer Henk
Oliveira Tiago Roux
Ortega Romeo
Pasik-Duncan Bozenna
Pogromsky Alexander
Polycarpou Marios M.
Rodellar José
Rogers Eric
Tomei Patrizio
Tsakalis Kostas S.
Tyukin Ivan
Ulrich Steve
Veres Sandor M.
Wang Hong
Ye Xudong
Yildiz Yildiray
Yucelen Tansel
Zhang Huaguang


Activities: Flagship events primarily sponsored by TC1.2

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Forthcoming events...
Past events...
2017 IFAC World Congress from Jul 09, 2017 04:55 PM to Jul 14, 2017 04:55 PM Toulouse, France,
12th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing (ALCOSP'2016) from Jun 29, 2016 08:30 AM to Jul 01, 2016 05:30 PM Eindhoven, The Netherlands,
6th IFAC International Workshop on Periodic Control Systems (PSYCO'2016) from Jun 29, 2016 06:35 AM to Jul 01, 2016 06:35 PM Eindhoven, The Netherlands,
11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing (ALCOSP'2013) from Jul 03, 2013 05:30 AM to Oct 05, 2013 05:30 PM Caen, France,
5th IFAC International Workshop on Periodic Control Systems (PSYCO'2013) from Jul 03, 2013 08:35 AM to Jul 05, 2013 05:35 PM Caen, France,
ALCOSP'2010 Oct 27, 2010 03:35 PM
PSYCO'2010 Oct 27, 2010 03:40 PM
ALCOSP'2007 Oct 27, 2007 03:40 PM Saint -Petersburg, Russia,
PSYCO'2007 Oct 27, 2015 03:40 PM Saint -Petersburg, Russia,
ALCOSP'2004 Oct 27, 2004 03:40 PM Yokohama, Japan,
PSYCO'2004 Oct 27, 2004 03:45 PM Yokohama, Japan,
ALCOSP'2001 Oct 27, 2001 03:45 PM Cernobbio-Come, Italy,
PSYCO'2001 Oct 27, 2015 03:45 PM Cernobbio-Come, Italy,
ALCOSP'1998 Oct 27, 2015 03:45 PM Glasgow, Scotland,
ALCOSP'1995 Oct 27, 2015 03:45 PM Budapest, Hungary,
ACASP'1992 Oct 27, 2015 03:45 PM Grenoble, France,
ACASP'1989 Oct 27, 2015 03:45 PM Grenoble, France,
ACASP'1986 Oct 27, 2015 03:50 PM Lund, Sweden,

Success stories

Reports of successful stories relating to adaptive and learning control:

1. Rehabilitation Engineering

Over the last decade researchers have been applying learning control to help stroke patients recover movement. Learning algorithms are used to adjust the electrical stimulation applied to the muscles of stroke patients. The stimulation is precisely controlled in order to assist patients’ completion of functional reaching tasks like pushing a light switch or picking up a cup. The controllers used to do this learn from experience how much help to give each patient to make the therapy as effective as possible.

 The team, led by Dr Chris Freeman at Southampton University in the UK, has shown that these technologies have significant clinical effectiveness and the aim now is to create systems that patients can use in their own homes. By exploiting the effectiveness of learning control, the aim is to soon combine these elements to provide a system that can provide treatment to the 15 million people worldwide who suffer from a stroke each year.

 Rehabilitation picture

 Further reading:

C. T. Freeman. Control System Design for Electrical Stimulation in Upper Limb Rehabilitation. Springer International Publishing, December 2015

C. T. Freeman et al. Iterative learning control in healthcare electrical stimulation and robotic-assisted upper limb stroke rehabilitation. IEEE Control Systems Magazine, 32, (1), 18-43, 2012.

C. T. Freeman et al. Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation (SpringerBriefs in Electrical and Computer Engineering), Springer, 2015.



Relevant literature

The following references will help newcomers to the field to get introduced:


Books on Adaptive Control of Linear Systems

Kumpati S. Narendra, Anuradha M. Annaswamy. Stable Adaptive Systems. Prentice Hall 1989 & Dover Books on Electrical Engineering, 2012.

P.A. Ioannou and B. Fidan, Adaptive Control Tutorial, Prentice-Hall 1996 & SIAM 2006.

H. Kaufman, I. Barkana, and K. Sobel, Direct Adaptive Control Algorithms - Theory and Applications, 2nd ed., New York: Springer, 1998.

Karl J. Åström, Dr. Björn Wittenmark. Adaptive Control. Dover Publications, 2008.

Graham C Goodwin  and Kwai Sang Sin Adaptive Filtering Prediction and Control. Dover Books on Electrical Engineering, 2009.

Naira Hovakimyan, Chengyu Cao. L1 Adaptive Control Theory: Guaranteed Robustness with Fast Adaptation. SIAM, 2010.

Margareta Stefanovic and Michael G. Safonov. Safe Adaptive Control: Data-driven Stability Analysis and Robust Synthesis. Springer, 2011.

Iven Mareels and Jan Willem Polderman. Adaptive Systems: An Introduction. Springer, 2012.

P. A. Ioannou and J. Sun, Robust Adaptive Control. Dover Books on Electrical Engineering, 2012.

Shankar Sastry and Marc Bodson. Adaptive Control: Stability, Convergence and Robustness. Dover Books on Electrical Engineering, 2012.

Books on Adaptive Control for Non-Linear and Distributed Parameter Systems

Miroslav Krstic, Ioannis Kanellakopoulos, Petar Kokotovic. Nonlinear and Adaptive Control Design, Wiley, 1995.

Riccardo Marino and Patrizio Tomei. Nonlinear control design: geometric, adaptive and robust. Prentice Hall, 1996.

Kartik B. Ariyur and Miroslav Krstic. Real-Time Optimization by Extremum Seeking Feedback. Wiley, 2003.

Alessandro Astolfi, Dimitrios Karagiannis, Romeo Ortega, Nonlinear and Adaptive Control with Applications. Springer, 2008.

Andrey Smyshlyaev and Miroslav Krstic. Adaptive Control of Parabolic PDEs, Princeton University Press, 2010

Shu-Jun Liu and Miroslav Krstic. Stochastic Averaging and Stochastic Extremum Seeking, Springer, 2012.

Sample of Survey Articles on Adaptive Control

K.J. Åström. Theory and applications of adaptive control—A survey. Automatica, Volume 19, Issue 5, September 1983, Pages 471-486

Romeo Ortega, Yu Tang. Robustness of adaptive controllers—A survey. Automatica, Volume 25, Issue 5, September 1989, Pages 651-677

D.W. Clarke. Adaptive predictive control. Annual Reviews in Control, Volume 20, 1996, Pages 83-94

Riccardo Marino. Adaptive control of nonlinear systems: Basic results and applications. Annual Reviews in Control, Volume 21, 1997, Pages 55-66

Brian D.O. Anderson, Arvin Dehghani. Challenges of adaptive control–past, permanent and future. Annual Reviews in Control, Volume 32, Issue 2, December 2008, Pages 123-135

Miroslav Krstic, Andrey Smyshlyaev. Adaptive control of PDEs. Annual Reviews in Control, Volume 32, Issue 2, December 2008, Pages 149-160

Kumpati S. Narendra, Zhuo Han. The changing face of adaptive control: The use of multiple models. Annual Reviews in Control, Volume 35, Issue 1, April 2011, Pages 1-12

Gang Tao. Multivariable adaptive control: A survey. Automatica, Volume 50, Issue 11, November 2014, Pages 2737-2764.

P. A. Ioannou, A. M. Annaswamy, K. S. Narendra, S. Jafari, L. Rudd, Roméo Ortega, J. Boskovic. IEEE Transactions on Automatic Control, 59 (11), pp.3075-3080, 2014.


Books on Iterative Learning Control

Z. Bien (Editor), J.-X. Xu, "Iterative Learning Control: Analysis, Design, Integration and Applications Paperback," Springer, 2012.

K. L. Moore. Iterative Learning Control for Deterministic Systems (Advances in Industrial Control), Springer, 1993.

H.-S. Ahn and K. L. Moore. Iterative Learning Control: Robustness and Monotonic Convergence for Interval Systems (Communications and ControlEngineering), Springer, 2007.

J.-X. Xu and Y. Tan, Linear and Nonlinear Iterative Learning Control: v. 291 (Lecture Notes in Control and Information Sciences), Springer, 2003.

D. Wang and Y. Ye. Practical Iterative Learning Control with Frequency Domain Design and Sampled Data Implementation (Advances in Industrial Control), Springer, 2014.

C. T. Freeman et al. Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation (SpringerBriefs in Electrical and Computer Engineering), Springer, 2015

Articles: Iterative Learning Control

S . Arimoto, S. Kawamura, and F. Miyazaki, “Bettering operations of robots by learning,” J. Robot. Syst., vol. 1, no. 2, pp. 123–140, 1984.

D . A. Bristow, M. Tharayil, and A. G. Alleyne, “A survey of iterative learning control,” IEEE Contr. Syst. Mag., vol. 26, no. 3, pp. 96–114, 2006.

H .-S. Ahn, Y. Chen, and K. L. Moore, “Iterative learning control: Brief survey and categorization,” IEEE Trans. Syst., Man, Cybern. C, vol. 37, no. 6, pp. 1109–1121, 2007.

Books on Repetitive Control

G. A. Ramos, R. Costa-Castelló and J. M. Olm. Digital Repetitive Control under Varying Frequency Conditions (Lecture Notes in Control and Information Sciences), Springer, 2013.

H. Youde. Optimality and Computation Issues in Repetitive Control, AP Lambert Academic Publishing, 2013

Articles on Repetitive Control

B. A. Francis and W. M. Wonham. The internal model principle for linear multivariable regulators. Applied Mathematics and Optimization, 2(2):170194, 1975

R. W. Longman, On the theory and design of linear repetitve control systems. European Journal of Control, 5:447496, 2010

R. W. Longman, Iterative learning control and repetitive control for engineering practice. International Journal of Control, 73(10):930954, 2000


We invite you to discuss here comments and suggestions on the activities of this TC. Please note that non-relevant comments or comments violating IFAC ethics will be removed.
Forum of experts

How TC members can help

- Promoting publications, especially journal special sections and issues as well as research books and textbooks on subjects interesting this TC.

- Participating to the TC sponsored events:

    • Submitting high quality papers to the TC flagship event ALCOSP/PSYCO.
    • Submitting high quality papers to the IFAC World Congress within the TC scope.
    • Participating to the review process for ALCOSP/PSYCO and IFAC WC.
- Reporting on new trends in the field and on successful application stories, sending very succinct reports on these achievements to the TC Chair.


This folder includes all past ALCOSP and PSYCO events, together with all TC1.2 meetings dates and minutes.

History - Read More…