Multilayer feedforward networks are universal approximators
Neural Networks
Brief paper: A neural network solution for fixed-final time optimal control of nonlinear systems
Automatica (Journal of IFAC)
Brief paper: Finite-horizon dynamic optimization of nonlinear systems in real time
Automatica (Journal of IFAC)
Brief paper: Adaptive optimal control for continuous-time linear systems based on policy iteration
Automatica (Journal of IFAC)
Asymptotically stable adaptive critic design for uncertain nonlinear systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
Direct heuristic dynamic programming for nonlinear tracking control with filtered tracking error
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem
Automatica (Journal of IFAC)
Discrete-Time Nonlinear HJB Solution Using Approximate Dynamic Programming: Convergence Proof
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear optimal tracking control with application to super-tankers for autopilot design
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
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.