This paper studies iterative learning control (ILC) for under-determined and over-determined systems, i.e., systems for which the control action to produce the desired output is not unique, or for which exact tracking of the desired trajectory is not feasible. For both cases we recommend the use of the pseudoinverse or its approximation as a learning operator. The Tikhonov regularization technique is discussed for computing the pseudoinverse to handle numerical instability. It is shown that for over-determined systems, the minimum error is never reached by a repetition invariant learning controller unless one knows the system exactly. For discrete time uniquely determined systems it is indicated that the inverse is usually ill-conditioned, and hence an approximate inverse based on a pseudoinverse is appropriate, treating the system as over-determined. Using the structure of the system matrix, an enhanced Tikhonov regularization technique is developed which converges to zero tracking error. It is shown that the Tikhonov regularization is a form of linear quadratic ILC, and that the regularization approach solves the important practical problem of how to intelligently pick the weighting matrices in the quadratic cost. It is also shown how to use a modification of the Tikhonov-based quadratic cost in order to produce a frequency cutoff. This robustifies good learning transients, by reformulating the problem as an over-determined system.
@article{bwmeta1.element.bwnjournal-article-amcv13i1p113bwm, author = {Avrachenkov, Konstantin and Longman, Richard}, title = {Iterative learning control for over-determined under-determined, and ill-conditioned systems}, journal = {International Journal of Applied Mathematics and Computer Science}, volume = {13}, year = {2003}, pages = {113-122}, zbl = {1046.93019}, language = {en}, url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-amcv13i1p113bwm} }
Avrachenkov, Konstantin; Longman, Richard. Iterative learning control for over-determined under-determined, and ill-conditioned systems. International Journal of Applied Mathematics and Computer Science, Tome 13 (2003) pp. 113-122. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-amcv13i1p113bwm/
[000] Arimoto S., Kawamura S. and Miyazaki F. (1984): Bettering operation of robots by learning. - J.Robot. Syst., Vol. 1, No. 2, pp. 123-140.
[001] Astrom K., Hagander P. and Strenby J. (1980): Zeros of sampled systems. - Proc. IEEE CDC'80, Albuquerque, pp. 1077-1081.
[002] Avrachenkov K.E. (1998): Iterative learning control based on quasi-Newton methods. - Proc. IEEE CDC'98, Tampa, (on CD-ROM).
[003] Avrachenkov K.E. and Pervozvansky A.A. (1998a): Regularization and robustness of learning-based control algorithms. - J.Comput. Syst. Sci., Vol. 37, No. 2, pp. 338-340. | Zbl 1092.93612
[004] Avrachenkov K.E. and Pervozvansky A.A. (1998b): Iterative learning control for singularly perturbed systems. - Proc. ILC Workshop, IEEE CDC'98, Tampa, pp. 71-73.
[005] Avrachenkov K.E., Beigi H.S.M. and Longman R.W. (1999): Updating procedures for iterative learning control in Hilbert space. - Proc. IEEE CDC'99, Phoenix, pp. 276-280.
[006] Beigi H.S.M. (1997): New adaptive and learning-adaptive control techniques based on an extension of the generalized secant method. - J. Intell. Automat. Soft Comp., Vol. 3, No. 2, pp. 171-184.
[007] Beklemishev D.V. (1983): Additional Chapters of Linear Algebra. -Moscow: Nauka, (in Russian). | Zbl 0532.15002
[008] Campbell S.L. and Meyer C.D. (1979): Generalized Inverses of Linear Transformation. - London: Pitman. | Zbl 0417.15002
[009] Dennis J.E. Jr. and Schnabel R.B. (1983): Numerical Methods for Unconstrained Optimization and Nonlinear Equations. -Englewood Cliffs: Prentice-Hall. | Zbl 0579.65058
[010] Ding J. and Huang L.J. (1996): Perturbation of generalized inverses of linear operators in Hilbert spaces. - J. Math. Anal. Appl., Vol. 198, No. 2, pp. 506-515. | Zbl 0867.47003
[011] Frueh J.A. and Phan M.Q. (2003): Linear quadratic optimal learning control (LQL). - Int. J. Contr., Special Issue on Iterative Learning Control, (in print). | Zbl 0995.49018
[012] Jang H.S. and Longman R.W. (1994): A new learning control law with monotonic decay of the tracking error norm. - Proc. 32-nd Ann. Allerton Conf. Communication, Control, and Computing, Monticello, Illinois, pp. 314-323.
[013] Jang H.S. and Longman R.W. (1996): Design of digital learning controllers using a partial isometry. - Adv. Astronaut. Sci., Vol. 93, pp. 137-152.
[014] Juang J.-N., Phan M., Horta L.G. and Longman R.W. (1993): Identification of observer Kalman filter Markov parameters: Theory and experiments. - J. Guid. Contr. Dynam., Vol. 16, No. 2, pp. 320-329. | Zbl 0775.93259
[015] Longman R.W. (1998): Designing Iterative Learning and Repetitive Controllers, In: Iterative Learning Control: Analysis, Design, Integration and Applications (Z. Bien and J.-X. Xu, Eds.). - Boston: Kluwer Academic Publishers, pp. 107-146.
[016] Longman R.W. (2000): Iterative learning control and repetitive control for engineering practice. - Int. J. Contr., Special Issue on Iterative Learning Control, Vol. 73, No. 10, pp. 930-954. | Zbl 1006.93598
[017] Longman R.W. and Chang C.-K. (1990): Learning control for minimizing a quadratic cost during repetitions of a task. - Proc. AIAA/AAS Astrodynamics Conf., A Collection of Technical Papers, Part 2, Portland, Oregon, pp. 530-536.
[018] Longman R.W. and Huang Y.-C. (2003): The phenomenon of apparent convergence followed by divergence in learning and repetitive control. - Intell. Automat. Soft Comput., Special Issue on Learning and Repetitive Control, Vol. 8, No. 2, (to appear).
[019] Longman R.W., Beigi H.S.M. and Li C.J. (1989): Learning control by numerical optimization methods. - Proc. Conf. Modeling and Simulation, Instrument Soc. of America, Vol. 20, Part 5, pp. 1877-1882.
[020] Moore K.L. (1993): Iterative learning control for deterministic systems. - London, U.K.: Springer-Verlag. | Zbl 0773.93002
[021] Moore K.L. (1997): Iterative learning control- An expository overview.- Tech. Rep., No. 9798 002, (to appear in Appl. Comput. Contr. Signal Process. Circ.). | Zbl 0955.93500
[022] Oh S.J., Longman R.W. and Phan M.Q. (1997): Use of decoupling basis functions in learning control for local learning and improved transients. - Adv. Astronaut. Sci., Vol. 95, pp. 651-670.
[023] Owens D.H., Amann N. and Rogers E. (1995): Iterative learning control-An overview of recent algorithms. - Appl. Math. Comput. Sci., Vol. 5, No. 3, pp. 425-438. | Zbl 0850.93003
[024] Pervozvansky A.A. (1995a): Learning control and its applications. Part 1. Elements of general theory. - Avtomatika i Telemekhanika, No. 11, Engl. Transl. in Automation and Remote Control.
[025] Pervozvansky A.A. (1995b): Learning control and its applications. Part 2. Frobenious systems and learning controlfor robot-manipulators. - Avtomatika i Telemekhanika, No. 12, Engl. Transl. in Automation and Remote Control.
[026] Pervozvansky A.A. and Avrachenkov K.E. (1997): Learning control algorithms: convergence and robustness. - Proc. Australian Control Conf., Sydney, pp. 366-371.
[027] Plotnik A.M. and Longman R.W. (1999): Subtleties in the use of zero-phase low-pass filtering and cliff filtering in learning control. - Adv. Astronaut. Sci., Vol. 103, pp. 673-692.
[028] Rogers E. and Owens D.H. (1992): Stability Analysis for Linear Repetitive Processes. - Berlin: Springer-Verlag. | Zbl 0772.93072
[029] Tikhonov A.N. and Arsenin V.Ya. (1974): Solution Methods for Ill-Posed Problems. - Moscow: Nauka, (in Russian).