Stability and Robustness Analysis and Improvements for Incremental Nonlinear Dynamic Inversion Control

Abstract

Incremental nonlinear dynamic inversion (INDI) is a variation on nonlinear dynamic inversion (NDI), retaining the high-performance characteristics, while reducing model dependency and increasing robustness. After a successful flight test with a multirotor micro aerial vehicle, the question arises whether this technique can be used to successfully design a flight control system for aircraft in general. This requires additional research on aircraft characteristics that could cause issues related to the stability and performance of the INDI controller. Typical characteristics are additional time delays due to data buses and measurement systems, slower actuator and sensor dynamics, and a lower control frequency. The main contributions of this article are 1) an analytical stability analysis showing that implementing discrete-time INDI with a sampling time smaller than 0.02s results in large stability margins regarding system characteristics and controller gains; 2) a simulation study showing significant performance degradation requiring controller adaptation due to actuator measurement bias, angular rate measurement noise, angular rate measurement delay and actuator measurement delay; and 3) the use of a real-time time delay identification algorithm based on latency to successfully synchronize the angular rate and actuator measurement delay together with pseudo control hedging (PCH) to prevent oscillatory behavior.

Actuator modelling for attitude control using incremental nonlinear dynamic inversion

“…Finally, Figure 4 shows a similar analysis for the effect of uncertainty in the time delay trueτ^. As was already discussed in the literature, 8 a lack of assumed time delay trueτ^ can lead to oscillations in the closed loop. In case of underestimating the delay trueτ^, the significant poles first tend towards faster dynamics while still being fully damped.…”

“…Since the problem of oscillations frequently arises when applying INDI, we try to gain some insight into this issue by observing the closed-loop system poles. A similar analysis was already done in previous work 8 but concentrates on the stability properties of the closed loop.…”

Stability Margin and Structural Coupling Analysis of a Hybrid INDI Control for the Fighter Aircraft

“…Therefore, the designer should use the low-pass filter considering sampling rate of FLCC. And, as already known, the time delay of the measured angular acceleration feedback significantly affects the performance degradation of the aircraft and the stability margin reduction of the control system [58]. These characteristics can seriously impair flight safety of the aircraft with relaxed static stability (RSS) configuration [21].…”

“…Recently, The Netherlands Aerospace Centre (NLR) and german aerospace center (DLR) with the Technical University of Delft have applied it to Cessna 550 demonstrator [18] and have proved the performance of the developed control law. The stability and robustness of the sensor-based INDI control has already been proven [19,20].…”

Online reinforcement learning for fixed-wing aircraft longitudinal control

“…To analyze controller design under the presence of measurement noise, the noise terms have been implemented in the form of zero mean Gaussian noise. Cessna Citation aircraft specific noise terms for states , and have been set following [30]. Because is a relatively difficult state to measure as the measurement process involves a mechanical wind vane when compared to body rate and attitude angle states, a larger noise standard deviation value of 0.5 • has been given.…”