This paper presents a flight control strategy based on nonlinear dynamic inversion. The approach presented, called incremental nonlinear dynamic inversion, uses properties of general mechanical systems and nonlinear dynamic inversion by feeding back angular accelerations. Theoretically, feedback of angular accelerations eliminates sensitivity to model mismatch, greatly increasing the robust performance of the system compared with conventional nonlinear dynamic inversion. However, angular accelerations are not readily available. Furthermore, it is shown that angular acceleration feedback is sensitive to sensor measurement time delays. Therefore, a linear predictive filter is proposed that predicts the angular accelerations, solving the time delay and angular acceleration availability problem. The predictive filter uses only references and measurements of angular rates. Hence, the proposed control method makes incremental nonlinear dynamic inversion practically available using conventional inertial measurement units.

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This paper describes the design, implementation and flight testing of flight control laws based on Incremental Nonlinear Dynamic Inversion (INDI). The method compares commanded and measured accelerations to compute increments on the current control deflections. This results in highly robust control solutions with respect to model uncertainties as well as changes in aircraft dynamic characteristics of failure cases during flight. At the same time, the complexity of the algorithms is similar to classical ones. The key for practical implementation is in ensuring synchronization between angular acceleration and control deflection measurements or estimates. The underlying theory and practical design methods of INDI are very well understood, but implementation and testing has remained limited to sub-scale UAVs. The main contribution of this paper is to present the design and validation of manual attitude control functions for a Cessna Citation II experimental aircraft, covering control structure design, application of INDI, design optimization, robustness analyses, software implementation, ground and flight testing. For comparison, also control laws based on classical Nonlinear Dynamic Inversion were implemented and flown. The flight tests were highly successful and marked the first successful demonstration of INDI on a CS-25 certified aircraft. The flight test results proved that INDI clearly outperforms NDI and provided valuable lessons-learnt for future applications. Nomenclature A x , A y , A z Specific forces along body X/Y/Z axis, g C Dimensionless coefficient F Force, N J Inertia matrix, kg•m 2 J Moment of Inertia, kg•m 2 K Gain M Moment, Nm M Mach number N 1 Fan speed, s −1 S Wing surface area, m 2 V Velocity vector, m/s V Airspeed, m/s a x , a y , a z Linear accelerations along body X/Y/Z axis, m/s 2 b Wing span, m 2 c Mean aerodynamic chord, m

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This paper presents a new method for estimating the parameters of multi-channel pilot models that is based on maximum likelihood estimation. To cope with the inherent nonlinearity of this optimization problem, the gradient-based Gauss-Newton algorithm commonly used to optimize the likelihood function in terms of output error is complemented with a genetic algorithm. This significantly increases the probability of finding the global optimum of the optimization problem. The genetic maximum likelihood method is successfully applied to data from a recent human-inthe-loop experiment. Accurate estimates of the pilot model parameters and the remnant characteristics were obtained. Multiple simulations with increasing levels of pilot remnant were performed, using the set of parameters found from the experimental data, to investigate how the accuracy of the parameter estimate is affected by increasing remnant. It is shown that only for very high levels of pilot remnant the bias in the parameter estimates