Reinforcement Learning-Based Output Feedback Control of Nonlinear Systems with Input Constraints
A novel neural network (NN)-based output feedback controller with magnitude constraints is designed to deliver a desired tracking performance for a class of multi-input and multi-output (MIMO) strict feedback nonlinear discrete-time systems. Reinforcement learning is proposed for the output feedback controller, which uses three NNs: 1) an NN observer to estimate the system states with the input-output data, 2) a critic NN to approximate certain strategic utility function, and 3) an action NN to minimize both the strategic utility function and the unknown dynamics estimation errors. Using the Lyapunov approach, the uniformly ultimate boundedness (UUB) of the state estimation errors, the tracking errors and weight estimates is shown.
J. Sarangapani and P. He, "Reinforcement Learning-Based Output Feedback Control of Nonlinear Systems with Input Constraints," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Institute of Electrical and Electronics Engineers (IEEE), Feb 2005.
The definitive version is available at http://dx.doi.org/10.1109/TSMCB.2004.840124
Electrical and Computer Engineering
Keywords and Phrases
Discrete Time Systems; Feedback; Learning (Artificial Intelligence); Neural Nets; Nonlinear Systems
Article - Journal
© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
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