What is Iterative Learning Control?
Learning control starts with a fundamental recognition that repeated practice is a common mode of human learning. How many of us know our moments of inertia, and yet we can learn how to walk or to swim through practice? Given a goal (regulation, tracking, or optimization), learning control, or more specifically, iterative learning control, to distinguish this from other modes of learning, refers to the mechanism by which the necessary control can be synthesized by repeated trials. Learning control is most suitable to operations where the same task is to be performed over and over again, e.g., robots in a manufacturing line. Indeed, robotics is where learning control had its origin. Learning control in its simplest form is pure feedforward. It assumes the system is repeatable, the initial condition can be resetted perfectly, and the objective is to track a feasible trajectory prescribed by the user. One or more of these assumptions can be relaxed, leading to various learning control problems. Available learning techniques range from those requiring no knowledge of the system dynamics (e.g., genetic algorithm) to more sophisticated methods involving system identification to make the learning process efficient and successful on difficult problems. A close relative of learning control is repetitive control. In learning control, the system is designed to return to the same initial condition before each new execution of the task, as in the case of a robot performing a task on each item that arrives one by one on an assembly line. Repetitive control, on the other hand, applies to the situation where the desired trajectory to be tracked is a periodic function of time, and there is no resetting between periods.
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Phan, M.Q. and Frueh, J.A., "System Identification and Learning Control," Iterative Learning Control: Analysis, Design, Integration, and Applications, Chapter 15, Bien Z. and Xu, J. (eds.), Kluwer Academic Publishing, Norwell, MA, 1998, pp. 285-306.
Phan, M.Q. and Rabitz, H., "A Self-Guided Algorithm for Learning Control of Quantum-Mechanical Systems," Journal of Chemical Physics, Vol. 110, No. 1, January 1999, pp. 34-41.
Frueh, J.A. and Phan, M.Q., "Linear Quadratic Optimal Learning Control (LQL)," Proceedings of the 37th IEEE Conference on Decision and Control, Tampa, Florida, December 1998.
Wen, H.P, Phan, M.Q., and Longman, R.W., "Bridging Learning and Repetitive Control Using Basis Functions," Advances in the Astronautical Sciences, Vol. 99, Part 1, Middour, J.W. et al. (eds.), 1998, pp. 335-354.
Frueh, J.A. and Phan, M.Q., "System Identification and Inverse Control Using Input-Output Data from Multiple Trials," Proceedings of the 2nd Asian Control Conference, Seoul, Korea, July 1997.
Phan, M.Q. and Rabitz, H., "Learning Control of Quantum-Mechanical Systems by Identification of Effective Input-Output Maps," Chemical Physics, Vol. 217, No. 2&3, May 1997, pp. 389-400.
Phan, M.Q. and Frueh, J.A., "Learning Control for Trajectory Tracking Using Basis Functions," Proceedings of the IEEE Conference on Decision and Control, Kobe, Japan, 1996, pp. 2490-2492.
Phan, M.Q. and Juang, J.-N., "Designs of Learning Controllers Based on an Auto-Regressive Representation of a Linear System," Journal of Guidance, Control, and Dynamics, Vol. 19, No. 2, March-April 1996, pp. 355-362.
Chew, M. and Phan, M.Q., "Application of Learning Control Theory to Mechanisms: Inverse Kinematics and Parametric Error Compensation (Part I), and Reduction of Residual Vibrations in Electromechanical Bonding Machines (Part II)," Proceedings of the 23rd ASME Mechanisms Conference, Minneapolis, MN, September 1994.
Elci H., Phan, M., Longman, R. W., Juang, J.-N., and Ugoletti, R., "Experiments in The Use of Learning Control for Maximum Precision Robot Trajectory Tracking," Proceedings of the 1994 Conference on Information Sciences and Systems, Princeton University, Princeton, NJ, March 1994, pp. 951-958.
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