Neural network adaptive control of systems with input saturation

In the application of adaptive flight control, significant issues arise due to limitations on the plant inputs, such as actuator displacement limits. The concept of utilizing a modified reference model to prevent an adaptation law from "seeing" this system-input characteristic is described. The method allows correct adaptation while the plant input is saturated. To apply the method, estimates of actuator positions must be found. However, the adaptation law can correct for errors in these estimates. A theorem of boundedness for all system signals is included for a single hidden layer neural network adaptive law. The domain of attraction is also discussed.