Multivariable adaptive algorithms for reconfigurable flight control

• Published in: IEEE transactions on control systems technology Vol. 5; no. 2; pp. 217 - 229
• Main Authors: Bodson, M., Groszkiewicz, J.E.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.1997; Institute of Electrical and Electronics Engineers
• Subjects: Actuators; Adaptative systems; Adaptive algorithm; Adaptive control; Aerodynamics; Aerospace control; Aircraft propulsion; Applied sciences; Automatic control; Computer science; control theory; systems; Control system synthesis; Control theory. Systems; Exact sciences and technology; Fault tolerant systems; Nonlinear dynamical systems; Nonlinear equations; Least squares method; Non linear model; Control system; Flight; Adaptive control; Multivariable system; Fault tolerant system; Reconfiguration
• ISSN: 1063-6536
• DOI: 10.1109/87.556026

The application of multivariable adaptive control techniques to flight control reconfiguration is considered. The objective is to redesign automatically flight control laws to compensate for actuator failures or surface damage. Three adaptive algorithms for multivariable model reference control are compared. The availability of state measurements in this application leads to relatively simple algorithms. The respective advantages and disadvantages of the adaptive algorithms are discussed, considering their complexity and the assumptions that they require. An equation-error based algorithm is found to be preferable. Simulations obtained using a full nonlinear model of a twin-engine jet aircraft are presented. The results demonstrate the ability of the adaptive algorithms to maintain trim after a failure, to restore tracking of the pilot commands despite the loss of actuator effectiveness, and to coordinate the use of the remaining active control surfaces in order to guarantee the decoupling of the rotational axes. A new adaptive algorithm with a variable forgetting feature is also used and is found to yield a useful alternative to covariance resetting as a solution to covariance wind-up in least-squares algorithms.