Abstract
This paper presents a new model reference neuroadaptive control architecture for multivariable nonlinear uncertain dynamical systems with input actuator constraints. In particular, we consider both linear and nonlinear in the parameters neural network approximations to design neuroadaptive controllers for stabilization and command following in the presence of actuator dynamics that can effectively account for actuator amplitude and rate saturation constraints. An illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.
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