Huang, J.; Xu, D.; Li, Y.; Ma, Y. Near-Optimal Tracking Control of Partially Unknown Discrete-Time Nonlinear Systems Based on Radial Basis Function Neural Network. Mathematics2024, 12, 1146.
Huang, J.; Xu, D.; Li, Y.; Ma, Y. Near-Optimal Tracking Control of Partially Unknown Discrete-Time Nonlinear Systems Based on Radial Basis Function Neural Network. Mathematics 2024, 12, 1146.
Huang, J.; Xu, D.; Li, Y.; Ma, Y. Near-Optimal Tracking Control of Partially Unknown Discrete-Time Nonlinear Systems Based on Radial Basis Function Neural Network. Mathematics2024, 12, 1146.
Huang, J.; Xu, D.; Li, Y.; Ma, Y. Near-Optimal Tracking Control of Partially Unknown Discrete-Time Nonlinear Systems Based on Radial Basis Function Neural Network. Mathematics 2024, 12, 1146.
Abstract
This paper proposed an optimal tracking control scheme through adaptive dynamic
programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial
basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we
use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly
approximate the steady-state control input, where a novel adaptive law is proposed to update
neural network weights. While the optimal feedback control and the cost function are derived
via feedforward neural networks approximating, it is proposed to regulate the tracking error, the
critic network and the actor network are then trained online to obtain the solution of the associated
Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify
the effectiveness of the optimal tracking control technique using the neural networks.
Copyright:
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