Online Optimal Adaptive Control of a Class of Uncertain Nonlinear Discrete-time Systems
In this paper, a multi-layer neural network (MNN) based online optimal adaptive regulation of a class of nonlinear discrete-time systems in affine form with uncertain internal dynamics is introduced. The multi-layer neural networks (MNN)-based actor-critic framework is utilized to estimate the optimal control input and cost function. The temporal difference (TD) error is derived from the difference between actual and estimated cost function. The MNN weights of both critic and actor are tuned at every sampling instant as a function of the instantaneous temporal difference and control policy errors. The proposed approach does not require the selection of any basis function and its derivatives. The boundedness of the system state vector and actor and critic NN weights are shown through Lyapunov theory. Extension of the proposed approach to MNNs with more hidden layers is discussed. Simulation results are provided to illustrate the effectiveness of the proposed approach. © 2020 IEEE.
Proceedings of the International Joint Conference on Neural Networks
Moghadam, R., Natarajan, P., Raghavan, K., & Jagannathan, S. (2020). Online optimal adaptive control of a class of uncertain nonlinear discrete-time systems. 2020 International Joint Conference on Neural Networks (IJCNN) Proceedings: 1-6. doi: 10.1109/IJCNN48605.2020.9206724.