Kalman filter based modification on helicopter adaptive control

In this paper, a Kalman Filter based e-modification term is used in the update law of an online learning adaptive neural network controller for a helicopter application. The controller is applied to a high fidelity helicopter simulation model. The applications include both an example on a single-input single-output controller structure using a linear in the parameter neural network, but also a multi-input multi-output neural network structure to compensate uncertainties in the attitude control of a helicopter. It is observed that using a Kalman Filter based e-modification provides improved command following and requires less control effort compared to traditional e-modification.