Multiple-Model Parameter-Adaptive Control for In-Flight Simulation

Adaptive control of aircraft model-following systems has shown promising results for in-flight simulation, but the computational expense and slow convergence of conventional parameter estimation techniques for higher models inhibits their direct use. Computer simulations of adaptive systems usually assume some knowledge of model parameters to maintain tracking fidelity at a reasonable computational cost as parameters change. This paper incorporates a priori informatior into a multiple-model estimation algorithm which assigns a probability weighting to each estimtor within a "bank" of estimators. Final parameter estimates used in adaptive control are formed as a probabalistic weighted sum of individual estimates. Simulations of the system show excellent tracking performance throughout the flight envelope.