A Neural Network Solution for Fixed-Final Time Optimal Control of Nonlinear Systems
TEXAS UNIV AT ARLINGTON AUTOMATION AND ROBOTICS INST
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We consider the use of neural networks and Hamilton-Jacobi-Bellman equations towards obtaining fixed-final time optimal control laws in the input nonlinear systems. The method is based on Kronecker matrix methods along with neural network approximation over a compact set to solve a time-varying Hamilton-Jacobi-Bellman equation. The result is a neural network feedback controller that has time-varying coefficients found by a priori offline tuning. Convergence results are shown. The results of this paper are demonstrated on two examples.
- Operations Research
- Theoretical Mathematics