Incremental Nonlinear Model Predictive Control for Aircraft Attitude Tracking

Abstract

An incremental nonlinear model predictive control (INMPC) strategy is developed in this paper for aircraft attitude tracking control under external disturbances. To reduce the dependency on accurate system dynamics, an incremental model-based technique with partial system models is employed for aircraft attitude dynamics. It is built with first-order Taylor series expansion that only involves system input matrix information instead of the whole aircraft attitude dynamics, which improves the robustness against the strong nonlinearities and couplings of aircraft real dynamics via exploiting the real dynamics in an incremental domain. Then, the incremental aircraft attitude model is embedded into the nonlinear model predictive control (NMPC) method that can guarantee the optimality of the tracking control problem. Updating only the partial incremental model during the MPC prediction and optimization process reduces the computational burden compared to predicting the whole system. Simulation results under normal conditions and external disturbances indicate that the proposed INMPC method improves the robustness of the conventional NMPC method.