Aircraft fault-tolerant trajectory control using Incremental Nonlinear Dynamic Inversion. (December 2016)

• Record Type: Journal Article
• Authors: Lu, Peng; van Kampen, Erik-Jan; de Visser, Cornelis; Chu, Qiping
• Is Part Of: Control engineering practice. Volume 57(2016)
• Page Start: 126
• Page End: 141
• Publication Date: 2016-12
• Subjects: Trajectory control -- Fault-tolerant control -- Nonlinear flight control -- Incremental Nonlinear Dynamic Inversion -- Model identification; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2016.09.010
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: This paper deals with aircraft trajectory control in the presence of model uncertainties and actuator faults. Existing approaches, such as adaptive backstepping and nonlinear dynamic inversion with online model identification, can be applied. However, since there are a number of unknown aerodynamic derivatives, the tuning of parameter update law gains is time-consuming. Methods with online model identification require excitation and the selection of a threshold. Furthermore, to deal with highly nonlinear aircraft dynamics, the aerodynamic model structure needs to be designed. In this paper, a novel aircraft trajectory controller, which uses the Incremental Nonlinear Dynamic Inversion, is proposed to achieve fault-tolerant trajectory control. The detailed control law design of four loops is presented. The idea is to design the loops with uncertainties using the incremental approach. The tuning of the approach is straightforward and there is no requirement for excitation and selection of a threshold. The performance of the proposed controller is compared with existing approaches using three scenarios. The results show that the proposed trajectory controller can follow the reference even when there are model uncertainties and actuator faults. Abstract : Highlights: A novel aircraft trajectory controller with four control loops is proposed. The approach does not require designing parameter update laws or online model identification. The approach does not require additional effort for designing the aerodynamic model structure. The approach is robust to model uncertainties as well as structural and actuator faults. The performance of the proposed approach is compared to existing approaches.