Output Tracking with Constrained Inputs via Adaptive Recurrent Neural Control
| Authors: | Sanchez Edgar, CINVESTAV, Unidad Guadalajara, Mexico
Ricalde Luis J., CINVESTAV, Unidad Guadalajara, Mexico |
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| Topic: | 1.2 Adaptive and Learning Systems |
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| Session: | Adaptive and Learning Approaches to Controller Design |
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| Keywords: | Recurrent neural networks, output trajectory tracking, adaptive control, constrained control, Lyapunov functions. |
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Abstract
This paper extends previous results to the output tracking problem of nonlinear systems with unmodelled dynamics and constrained inputs. A recurrent high order neural network is used to identify the unknown system dynamics and a learning law is obtained using the Lyapunov methodology. A stabilizing control law for the output tracking error dynamics is developed using the Lyapunov methodology and the Sontag control law for nonlinear systems with constrained inputs.
