Neural Processing Letters
Performance Evaluation of Direct Heuristic Dynamic Programming using Control-Theoretic Measures
Journal of Intelligent and Robotic Systems
Brief paper: Design and implementation of an autonomous flight control law for a UAV helicopter
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
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
Adaptive dynamic programming: an introduction
IEEE Computational Intelligence Magazine
Fractal boundaries of basin of attraction of Newton-Raphson method in helicopter trim
Computers & Mathematics with Applications
An adaptive dynamic programming approach for closely-coupled MIMO system control
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Adaptive neural network control of helicopters
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
A distributed Web-based framework for helicopter rotor blade design
Advances in Engineering Software
Robust H∞ fuzzy control of dithered chaotic systems
Neurocomputing
Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
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This paper advances a neural-network-based approximate dynamic programming control mechanism that can be applied to complex control problems such as helicopter flight control design. Based on direct neural dynamic programming (DNDP), an approximate dynamic programming methodology, the control system is tailored to learn to maneuver a helicopter. The paper consists of a comprehensive treatise of this DNDP-based tracking control framework and extensive simulation studies for an Apache helicopter. A trim network is developed and seamlessly integrated into the neural dynamic programming (NDP) controller as part of a baseline structure for controlling complex nonlinear systems such as a helicopter. Design robustness is addressed by performing simulations under various disturbance conditions. All designs are tested using FLYRT, a sophisticated industrial scale nonlinear validated model of the Apache helicopter. This is probably the first time that an approximate dynamic programming methodology has been systematically applied to, and evaluated on, a complex, continuous state, multiple-input multiple-output nonlinear system with uncertainty. Though illustrated for helicopters, the DNDP control system framework should be applicable to general purpose tracking control.