Control Equivalent Turbulence Input for Inner Loop Controller Performance Evaluation

Atmospheric turbulence is a major source of uncertainty for unmanned rotorcraft operating in confined or disturbed environments, where robust trajectory planning requires reliable bounds on vehicle response. High-fidelity turbulence models are typically too computationally demanding for onboard use and difficult to integrate into planning frameworks. This paper presents a Control Equivalent Turbulence Input (CETI)–based approach to characterize turbulence effects on the inner-loop dynamics of a small unmanned helicopter and to derive disturbance-induced state deviation bounds suitable for robust planning. CETI models are identified from manually piloted hover flight tests of the unmanned research helicopter midiARTIS using a linear bare-airframe model and a Kalman filter for disturbance estimation. CETI transfer functions are fitted to averaged power spectral densities of the extracted disturbance inputs. The resulting model is validated by reproducing the identified transfer functions and by comparing open-loop simulation results to flight-test data in both time and frequency domain. Based on simulations with CETI inputs, probabilistic bounds on state deviations are derived and related to measured flight-test responses. The results demonstrate that the proposed CETI workflow provides a compact and computationally efficient turbulence surrogate that captures the dominant effects of atmospheric gusts on rotorcraft dynamics and is well suited for inner-loop performance assessment and as an input to robust model predictive control algorithms.