In this paper, an alternative solution for adaptive optimal tracking control of nonlinear completely unknown systems is proposed. Firstly, an adaptive identifier is used to estimate the unknown system dynamics. Then, a recently developed system augmentation approach is adopted to design the optimal control, where the reference signal is incorporated into the augmented system. Thus, both the feedforward control and feedback control can be obtained simultaneously. Then, a critic neural network (NN) is used to estimate the augmented performance index, and calculate the optimal control action. Thus, the widely used actor NN is not needed. Finally, a new adaptive law recently proposed by the authors is used to online update the NN weight. The closed-loop stability and the convergence of the optimal control are all proved. The feasibility of the suggested approach is demonstrated by a simulation example.
|Name||Proceedings of the International Joint Conference on Neural Networks (IJCNN)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Conference||2016 IEEE International Joint Conference on Neural Networks (IJCNN)|
|Period||24/07/16 → 29/07/16|