Electromechanical actuators in flight control of new aircrafts

In the past decade, the aerospace industry has been striving towards the "moreelectrical" aircraft concept. The objectives of this initiative have been to increasesubsystem modularity while achieving weight reduction, better energy efficiency, andincreased system availability. Although Electro Mechanical Actuators (EMAs) are stillrelative newcomers to the aerospace field the concept of all-electric aircraft andspacecraft designs promises an even wider use of EMAs in the future at the expense ofhydraulic actuators.Parameter estimation based approach can be used for monitoring and controlling ofEMAs. Three type of system identification methods are used in this chapter for themodeling of the electro mechanical actuator: Extended Kalman filter (EKF); AdaptiveRobust EKF and Fuzzy model identification using Genetic Algorithms (GA).An Extended Kalman filter is designed to estimate the parameters of the electromechanicalactuator. If the measurements are not reliable because of any kind ofmalfunction in the estimation system, the filter gives inaccurate results and diverges bytime. An Adaptive Robust EKF with the filter gain correction is proposed in the case ofpresence of measurement faults.If the electromechanical actuator mathematical model is not available (there are notany information about model structure and parameter values), then the fuzzy modelidentification can be used. Generally, design of fuzzy models of such complex processesis performed via fuzzy IF-THEN rules. In this chapter the fuzzy model of EMA based onGenetic Algorithms is designed and examined.