Fixed-Final-Time-Constrained Optimal Control of Nonlinear Systems Using Neural Network HJB Approach
| Autor: | Murad Abu-Khalaf, Frank L. Lewis, Tao Cheng |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Nonholonomic system
Mathematical optimization Adaptive control Artificial neural network Computer Networks and Communications MathematicsofComputing_NUMERICALANALYSIS Constrained optimization Hamilton–Jacobi–Bellman equation General Medicine Nonlinear control Optimal control Computer Science Applications Nonlinear system Artificial Intelligence Control theory Software Mathematics |
| Zdroj: | IEEE Transactions on Neural Networks. 18:1725-1737 |
| ISSN: | 1941-0093 1045-9227 |
| DOI: | 10.1109/tnn.2007.905848 |
| Popis: | In this paper, fixed-final time-constrained optimal control laws using neural networks (NNS) to solve Hamilton-Jacobi-Bellman (HJB) equations for general affine in the constrained nonlinear systems are proposed. An NN is used to approximate the time-varying cost function using the method of least squares on a predefined region. The result is an NN nearly -constrained feedback controller that has time-varying coefficients found by a priori offline tuning. Convergence results are shown. The results of this paper are demonstrated in two examples, including a nonholonomic system. |
| Databáze: | OpenAIRE |
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