Model Predictive Control Architecture for Rotorcraft Inverse Simulation

Abstract

A novel inverse simulation scheme is proposed for applications to rotorcraft dynamic models. The algorithm adopts an architecture which closely resembles that of a model predictive control scheme, where the controlled plant is represented by a high-order helicopter model. A fast solution of the inverse simulation step is obtained on the basis of a lower-order, simplified model. The resulting control action is then propagated forward in time using the more complex one. The algorithm compensates for discrepancies between the models by updating initial conditions for the inverse simulation step and introducing a simple guidance scheme in the definition of the tracked output variables.The proposed approach allows for the assessment of handling quality potential on the basis of the most sophisticated model while keeping model complexity to a minimum for the computationally more demanding inverse simulation algorithm. The reported results, for an articulated blade, single main rotor helicopter model, demonstrate the validity of the approach.

Engine Control Design Using Compressor Guided Vanes During Autorotation Power Recovery

“…An eight-DOF model for the PZL Mi-2 Plus helicopter (three translations and three rotations of a rigid fuselage, main rotor speed, and engine output power) was developed to optimize autorotation profiles with different performance indices, finally comparing the results with flight test data [16]. The above-mentioned autorotation control models can be regarded as being specific for investigation of optimized profiles after engine failures; however, they are not sufficient for designing flight or engine control law during autorotation [17][18][19][20][21]. This motivates the need for more It is meaningful to emphasize the significance and necessity of autorotation power recovery simulation and control.…”

Inverse simulation system for evaluating handling qualities during rendezvous and docking

“…Later, the two-timescale method [25] and global optimization method [26] were proposed, both of which were based on the integration method. To decrease the computational cost, Avanzini, Thomson, and Torasso introduced a model-predictive-control architecture for the inverse simulation that improved the efficiency of the previous inverse simulation systems [27]. This architecture is the basis for the RVD inverse simulation system proposed in this paper.…”

Numerical characterization of helicopter noise hemispheres

“…From this study, it will be possible to identify those flight parameters that need to be matched in order to assure, at a satisfactory level of accuracy, the (instantaneous) noiseemitted equivalence between a maneuver and a steady-state, rectilinear flight. In order to perform the proposed analysis, an unsteady approach maneuver is defined first, and an inverse flight dynamics simulation tool is then applied for determining the time-histories of corresponding pilot commands, hub loads and helicopter attitude variables [21,22,23]. Next, three different criteria are applied to correlate steady-state, rectilinear flights with the local specific unsteady flight conditions at the selected trajectory points.…”