Lateral Control Law Design for Helicopter Using Radial Basis Function Neural Network

Abstract

As fixed-parameter control can not satisfy control requirements when helicopter is aviating in large scale flight envelop, this paper proposes a new control law design to adjust parameters on-line. Firstly a parameter-mapping approach is developed to design flight control parameters at certain flight conditions according to the desired system performance. Then parameters obtained at given conditions are used to train Radial Basis Function Neural Network (RBFNN). Thus RBFNN can generalize the given flight conditions information and output appropriate control parameters which will meet control requirements for any current flight condition within the given flight envelop. Simulation results indicate this control law design is feasible and effective.

A direct adaptive control scheme for a faulty helicopter using the outer loop compensation technique

“…HE helicopter flight control technology has attracted more and more attentions owing to the increasing flight quality, task performance and the expanding flight envelopes of modern helicopter [1][2]. There are many actuator faults and external disturbance existing in the flight control systems of helicopter, so it's very important to deal with them timely.…”