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Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Jun2018, Vol. 29 Issue 6, p2614-2624. 11p. - Publication Year :
- 2018
-
Abstract
- This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi–Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals. [ABSTRACT FROM AUTHOR]
- Subjects :
- *NONLINEAR systems
*ARTIFICIAL neural networks
*FEEDBACK control systems
Subjects
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 29
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 129655423
- Full Text :
- https://doi.org/10.1109/TNNLS.2017.2761718