Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Microsystem design
Applied Optimal Control and Estimation
Applied Optimal Control and Estimation
Nonlinear systems control using neural networks
Nonlinear systems control using neural networks
Optimal control of distributed parameter systems using adaptive critic neural networks
Optimal control of distributed parameter systems using adaptive critic neural networks
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Design and Analysis of Experiments
Design and Analysis of Experiments
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Direct adaptive neural control for affine nonlinear systems
Applied Soft Computing
IEEE Transactions on Neural Networks
Reinforcement learning and adaptive dynamic programming for feedback control
IEEE Circuits and Systems Magazine
Power system stability enhancement by single network adaptive critic stabilizers
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Adaptive dynamic programming: an introduction
IEEE Computational Intelligence Magazine
Adaptive critic based redundancy resolution scheme for robot manipulators
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Neural network solution of optimal control problem with control and state constraints
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Neural networks solving free final time optimal control problem
TPNC'12 Proceedings of the First international conference on Theory and Practice of Natural Computing
Automatica (Journal of IFAC)
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Even though dynamic programming offers an optimal control solution in a state feedback form, the method is overwhelmed by computational and storage requirements. Approximate dynamic programming implemented with an Adaptive Critic (AC) neural network structure has evolved as a powerful alternative technique that obviates the need for excessive computations and storage requirements in solving optimal control problems. In this paper, an improvement to the AC architecture, called the ''Single Network Adaptive Critic (SNAC)'' is presented. This approach is applicable to a wide class of nonlinear systems where the optimal control (stationary) equation can be explicitly expressed in terms of the state and costate variables. The selection of this terminology is guided by the fact that it eliminates the use of one neural network (namely the action network) that is part of a typical dual network AC setup. As a consequence, the SNAC architecture offers three potential advantages: a simpler architecture, lesser computational load and elimination of the approximation error associated with the eliminated network. In order to demonstrate these benefits and the control synthesis technique using SNAC, two problems have been solved with the AC and SNAC approaches and their computational performances are compared. One of these problems is a real-life Micro-Electro-Mechanical-system (MEMS) problem, which demonstrates that the SNAC technique is applicable to complex engineering systems.