Optimal Adaptive Control of Uncertain Nonlinear Continuous-Time Systems with Input and State Delays
In this article, an actor-critic neural network (NN)-based online optimal adaptive regulation of a class of nonlinear continuous-time systems with known state and input delays and uncertain system dynamics is introduced. The temporal difference error (TDE), which is dependent upon state and input delays, is derived using actual and estimated value function and via integral reinforcement learning. The NN weights of the critic are tuned at every sampling instant as a function of the instantaneous integral TDE. A novel identifier, which is introduced to estimate the control coefficient matrices, is utilized to obtain the estimated control policy. The boundedness of the state vector, critic NN weights, identification error, and NN identifier weights are shown through the Lyapunov analysis. Simulation results are provided to illustrate the effectiveness of the proposed approach.
R. Moghadam and J. Sarangapani, "Optimal Adaptive Control of Uncertain Nonlinear Continuous-Time Systems with Input and State Delays," IEEE Transactions on Neural Networks and Learning Systems, Institute of Electrical and Electronics Engineers (IEEE), Sep 2021.
The definitive version is available at https://doi.org/10.1109/TNNLS.2021.3112566
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Control; Artificial Neural Networks; Delays; Discrete-Time Systems; Mathematical Models; Multilayer Neural Network (NN); Nonlinear Dynamical Systems; Optimal Adaptive Control.; Optimal Control; System Dynamics
International Standard Serial Number (ISSN)
Article - Journal
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29 Sep 2021