Fixed-Final Time Constrained Optimal Control of Nonlinear Systems Using Neural Network HJB Approach

Abstract

Fixed-final time constrained input optimal control laws using neural networks to solve Hamilton-Jacobi-Bellman (HJB) equations for general affine in the input nonlinear systems are proposed. A neural network is used to approximate the time-varying cost function using the method of least-squares on a pre-defined region and hence solve the HJB. The result is a neural network nearly optimal constrained feedback controller that has time-varying coefficients found by a priori offline tuning. The results of this paper are demonstrated on an example.