Fixed-final-time optimal tracking control of input-affine nonlinear systems

In this study, approximate dynamics programming framework is utilized for solving the Bellman equation related to the fixed-final-time optimal tracking problem of input-affine nonlinear systems. Convergence of the weights of the neurocontroller in the proposed successive approximation based algorithms is provided and the network is trained to provide the optimal solution to the problems with (a) unspecified initial conditions (b) different time horizons, and (c) different reference trajectories under certain general conditions. Numerical simulations illustrate the versatility of the proposed neurocontroller.