Comparison of L1 Adaptive Augmentation Strategies for a Differential PI Baseline Controller on a Longitudinal F16 Aircraft Model

Abstract

Abstract. In this paper two different approaches are presented to design an adaptive augmentation using L1 Adaptive Control. In terms of reference dynamics, the first one takes the closed-loop aircraft with baseline controller into account. The second approach tries to maintain nominal open-loop aircraft dynamics, with the baseline controller wrapped around the adaptive augmentation. They are compared by application to a model of the longitudinal dynamics of a F16 aircraft. The aircraft model is equipped with a Differential PI (DPI) baseline controller. The design of the combination of baseline controller and augmentation takes saturation as well as rate limitation of the control signal directly into account. Simulation results show the nominal closed-loop behavior is not harmed by the adaptive augmentation and that an increase of performance in case of uncertainty can be achieved. As an exemplary uncertainty case a rapid shift of the center of gravity (CG) is examined. The robustness of the controller structure is assessed by the use of both linear and nonlinear methods to obtain the Time Delay Margin (TDM) and the Gain Margin (GM) of the closed loop system. It can be observed, the adaptive augmentation leads to a significant decrease of robustness w.r.t. the plant input channel. This drop in TDM and GM can be fully restored by the application of a hedging strategy.

Temperature trajectory control of cryogenic wind tunnel with robust L1 adaptive control

“…This modification is realized by back-calculation, similar to the anti-winding-up mechanism in a conventional controller and will change the filter characteristics in (20), normally reducing the bandwidth of the filter. This modification can also be understood, as there is a hedging term (Hellmundt et al, 2015) in the control law to constrain the output signal within the limits of actuator.…”

L1 Adaptive Control Based on Dynamic Inversion for Morphing Aircraft

“…In addition, model uncertainties and measurement errors may also seriously affect the control accuracy [19]. Compared to other control methods, adaptive control is relatively model-free and its addition can compensate for the effects of possible faults or unexpected uncertainties and achieve a substantial improvement in performance; however, it is difficult for conventional adaptive control methods to achieve the trade-off between control performance and robustness [20][21][22]. L 1 adaptive control, an improvement of model reference adaptive control (MRAC) presented by Cao and Hovakimyan, appears to be beneficial both for robustness and performance [22][23][24], resolving the trade-off between the two by selecting a low-level filtering structure.…”

L 1 adaptive control of a generic hypersonic vehicle model with a blended pneumatic and thrust vectoring control strategy

“…Despite its wide applications, conventional adaptive controllers are also identified with another characteristic that an increase in performance usually leads to a decrease in robustness properties, hence posing a challenge in finding a suitable trade-off [27,28]. First proposed by Hovakimyan and Cao, L 1 adaptive control architecture seeked to tackle the problem by decoupling adaptation from robustness.…”