Composite adaptive control with locally weighted statistical learning

Neural Networks

Volumn 18, Issue 1, 2005, Pages 71-90

  Nakanishi, Jun ^a,b   Farrell, Jay A ^c   Schaal, Stefan ^a,d  

^a ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

^b Japan Science and Technology Agency   (Japan)

^c University of California Riverside   (United States)

^d UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

Adaptive control; Composite adaptation; Locally weighted learning; Receptive field weighted regression; Statistical learning of nonlinear functions

Indexed keywords

ADAPTIVE CONTROLLERS; DYNAMICAL SYSTEM; NONLINEAR FUNCTIONS; NONPARAMETRIC REGRESSION TECHNIQUES;

ALGORITHMS; APPROXIMATION THEORY; COMPUTER SIMULATION; LEARNING SYSTEMS; PROBLEM SOLVING; REGRESSION ANALYSIS; STABILITY; STATISTICAL METHODS;

ADAPTIVE CONTROL SYSTEMS;

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; CONTROL SYSTEM; ERROR; LEARNING; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; PRIORITY JOURNAL; STATISTICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; LINEAR MODELS; MODELS, STATISTICAL; NONLINEAR DYNAMICS; REGRESSION ANALYSIS; ROBOTICS;

EID: 11844255501     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2004.08.009     Document Type: Article

References (24)

1. C.G. Atkeson, A.W. Moore, and S. Schaal Locally weighted learning Artificial Intelligence Review 11 1-5 1997 11 73
2. C.M. Bishop Neural networks for pattern recognition 1995 Oxford University Press Oxford
3. F.-C. Chen, and H.K. Khalil Adaptive control of a class of nonlinear discrete-time systems using neural networks IEEE Transactions on Automatic Control 40 5 1995 791 801
4. J.Y. Choi, and J.A. Farrell Nonlinear adaptive control using networks of piecewise linear approximatiors IEEE Transactions on Neural Networks 11 2 2000 390 401
5. H. Gomi, and M. Kawato Neural network control for a closed-loop system using feedback-error-learning Neural Networks 6 1993 933 946
6. H. Kaufman, I. Bar-Kana, and K. Sobel Direct adaptive control algorithm 1993 Springer Berlin
7. H.K. Khalil Nonlinear systems 2nd ed 1996 Prentice Hall Englewood Cliffs, NJ
8. M. Krstić, I. Kanellakopoulos, and P. Kokotović Nonlinear and adaptive control design 1995 Wiley New York
9. A.U. Levin, and K.S. Narendra Control of nonlinear dynamical systems using neural networks: Controllability and stabilization IEEE Transactions on Neural Networks 4 2 1993 192 206
10. L. Ljung, and T. Söderström Theory and practice of recursive identification 1986 MIT Press Cambridge, MA
11. K.S. Narendra, and A.M. Annaswamy Stable adaptive systems 1989 Prentice Hall Englewood Cliffs, NJ
12. R. Sanner, and J.-J.E. Slotine Gaussian networks for direct adaptive control IEEE Transactions on Neural Networks 3 6 1992 837 863
13. S. Sastry, and M. Bodson Adaptive control: Stability convergence, and robustness 1989 Prentice Hall Englewood Cliffs, NJ
14. S. Sastry, and A. Isidori Adaptive control of linearizable systems IEEE Transactions on Automatic Control 34 11 1989 1123 1131
15. Schaal, S., & Atkeson, C.G. (1997). Receptive field weighted regression (Technical Report RE-H-209). ATR Human Information Processing Laboratories.
16. S. Schaal, and C.G. Atkeson Constructive incremental learning from only local information Neural Computation 10 8 1998 2047 2084
17. S. Seshagiri, and H.K. Khalil Output feedback control of nonlinear systems using RBF neural networks IEEE Transactions on Neural Networks 11 1 2000 69 79
18. J.-J.E. Slotine, and W. Li On the adaptive control of robot manipulators International Journal of Robotics Research 6 3 1987 49 59
19. J.-J.E. Slotine, and W. Li Composite adaptive control of robot manipulators Automatica 25 4 1989 509 519
20. J.-J.E. Slotine, and W. Li Applied nonlinear control 1991 Prentice Hall Englewood Cliffs, NJ
21. G. Tao, and P.A. Ioannou Necessary and sufficient conditions for strictly positive real matrices IEE Proceedings G, Circuits, Devices and Systems 137 5 1990 360 366
22. S. Vijayakumar, and H. Ogawa RKHS based functional analysis for exact incremental learning Neurocomputing 29 1-3 1999 85 113
23. S. Vijayakumar, and S. Schaal Locally weighted projection regression: An O(n) algorithm for incremental real time learning in high dimensional space In International conference in machine learning (ICML), Stanford, USA 2000
24. L.L. Whitcomb, A.A. Rizzi, and D.E. Koditschek Comparative experiments with a new adaptive controller for robot arms IEEE Transactions on Robotics and Automation 9 1 1993 59 70