Practical Observer Design for Real-Time Helicopter Weight Estimation

In-flight estimates of helicopter weight and mass center can be used to improve flight control system performance and inform condition-based maintenance. This paper introduces a two-stage observer design that uses an extended state observer and Kalman filter to produce real-time weight estimates. The proposed algorithm is practical in the sense that it is designed to provide accurate estimates of helicopter weight in the presence of parametric and non-parametric model errors. By decomposing lumped disturbance estimates into a set of perturbation terms and injecting non-parametric corrections into the model, the sensitivity to model error is reduced. Following a description of the proposed algorithm, performance is demonstrated in simulation using the AH-1G helicopter. Under dynamic excitation, the algorithm is shown to converge reliably to the helicopter’s weight even in the presence of significant parametric model discrepancies. This added robustness is reliant on an accurate mass center estimate. Larger model error sensitivity is exhibited in the presence of non-parametric model discrepancies. Several example cases characterize filter performance as a function of the type and degree of model error.