Cited by 677Adaptive Dynamic Programming ControlAdvanced Control Systems OptimizationReinforcement Learning in Robotics

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Adaptive dynamic programming

Murray, J.J., Cox, C.J., Lendaris, G.G., Saeks, R.·2002·IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)

DOI 10.1109/tsmcc.2002.801727

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Abstract

Unlike the many soft computing applications where it suffices to achieve a "good approximation most of the time," a control system must be stable all of the time. As such, if one desires to learn a control law in real-time, a fusion of soft computing techniques to learn the appropriate control law with hard computing techniques to maintain the stability constraint and guarantee convergence is required. The objective of the paper is to describe an adaptive dynamic programming algorithm (ADPA) which fuses soft computing techniques to learn the optimal cost (or return) functional for a stabilizable nonlinear system with unknown dynamics and hard computing techniques to verify the stability and convergence of the algorithm. Specifically, the algorithm is initialized with a (stabilizing) cost functional and the system is run with the corresponding control law (defined by the Hamilton-Jacobi-Bellman equation), with the resultant state trajectories used to update the cost functional in a soft computing mode. Hard computing techniques are then used to show that this process is globally convergent with stepwise stability to the optimal cost functional/control law pair for an (unknown) input affine system with an input quadratic performance measure (modulo the appropriate technical conditions). Three specific implementations of the ADPA are developed for 1) the linear case, 2) for the nonlinear case using a locally quadratic approximation to the cost functional, and 3) the nonlinear case using a radial basis function approximation of the cost functional; illustrated by applications to flight control.

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Dynamic Programming
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Linear Optimal Control Systems
Kwakernaak, H., Sivan, R., & Tyreus, B. N. D. (1974) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.3426828 · Cited by 5,010
Neuronlike adaptive elements that can solve difficult learning control problems
Barto, A. G., Sutton, R. S., & Anderson, C. W. (1983) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1983.6313077 · Cited by 3,325
Stable adaptive fuzzy control of nonlinear systems
Wang, L.-X. (1993) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.227383 · Cited by 1,622
Dynamic Programming: Deterministic and Stochastic Models
Bertsekas, D. P. (1987) · Medical Entomology and Zoology · Cited by 1,348
Introduction to dynamic systems: theory, models and applications
(1981) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(81)90069-0 · Cited by 1,136
Gaussian Networks for Direct Adaptive Control
Sanner, R. M., & Slotine, J.-J. E. (1991) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.1991.4791778 · Cited by 1,079
Approximate dynamic programming for real-time control and neural modeling
Werbos, P. J. (1992) · Medical Entomology and Zoology · Cited by 971
Introduction to dynamic systems
Williams, M. (2016) · Structural Dynamics · doi:10.1201/9781315375311-5 · Cited by 568
Adaptive neural network control of nonlinear systems by state and output feedback
Ge, S. S., Hang, C. C., & Zhang, T. (1999) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.809035 · Cited by 419

Flagged first, then by citations

Show all 32 references

Research works printed but not linked: 4

2001, Jour of Spacecraft and Rockets Special Section on Hyper-X, 38, 801
sitz, 1998, HYPER-X Hypersonic experimental research vehicle
wang, 1996, stable adaptive fuzzy control of nonlinear systems, IEEE Trans Syst Man Cybern B Cybern, 26, 677
barnett, 1971, The Matrices of Control Theory

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Authors: Murray, J. J., Cox, C. J., Lendaris, G. G., & Saeks, R.
Affiliations: Stony Brook University (US)
Accurate Automation (United States) (US)
Portland State University (US)
ORCID: Murray, J.J.
Venue: IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2002-05
Volume: 32
Issue: 2
Pages: 140-153
ISSN: 1094-6977 (ISSN-L)
DOI: 10.1109/tsmcc.2002.801727
OpenAlex: W2160561608
Cited by: 677
References: 32
Citations by year: 2026: 16 · 2025: 36 · 2024: 31 · 2023: 26 · 2022: 34 · 2021: 34 · 2020: 39 · 2019: 28 · 2018: 27 · 2017: 42 · 2016: 36 · 2015: 40 · 2014: 42 · 2013: 43 · 2012: 45
FWCI: 4.99
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) (publishedVersion)
PDXScholar (Portland State University) (submittedVersion)
Topics: Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Dynamic Programming Control
Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Keywords: adaptive dynamic programming, optimal control, Hamilton-Jacobi-Bellman equation, stability analysis, convergence analysis, unknown dynamics, nonlinear systems, input-affine systems, radial basis function approximation, flight control, soft computing

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