The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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