Stability Analysis for Incremental Nonlinear Dynamic Inversion Control

Wang, Xuerui; Kampen, E. van; Chu, Q.P.; Lu, Peng

doi:10.2514/1.g003791

Cited by 117 publications

(97 citation statements)

References 29 publications

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“…This significantly simplifies its identification and implementation processes. Even though its model dependency is reduced, by exploiting the sensor measurements, INDI actually has better robustness against uncertainties and disturbances than feedback linearization [19].…”

Section: Incremental Control Theorymentioning

confidence: 99%

“…By contrast, nonlinear control methods such as feedback linearization and backstepping can directly consider the operational condition variations. Different from the mainstream model-based nonlinear control methods, the novel incremental nonlinear dynamic inversion (INDI) control is sensor-based [19]. By exploiting sensor measurements, INDI simultaneously reduces its model dependency and enhances its robustness against model uncertainties and external disturbances [19].…”

Section: Introductionmentioning

confidence: 99%

“…Different from the mainstream model-based nonlinear control methods, the novel incremental nonlinear dynamic inversion (INDI) control is sensor-based [19]. By exploiting sensor measurements, INDI simultaneously reduces its model dependency and enhances its robustness against model uncertainties and external disturbances [19]. These features make it promising for aircraft load alleviation problems [20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sensing, Actuation, and Control of the SmartX Prototype Morphing Wing in the Wind Tunnel

2021

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This paper presents a study on trailing edge deflection estimation for the SmartX camber morphing wing demonstrator. This demonstrator integrates the technologies of smart sensing, smart actuation and smart controls using a six module distributed morphing concept. The morphing sequence is brought about by two actuators present at both ends of each of the morphing modules. The deflection estimation is carried out by interrogating optical fibers that are bonded on to the wing’s inner surface. A novel application is demonstrated using this method that utilizes the least amount of sensors for load monitoring purposes. The fiber optic sensor data is used to measure the deflections of the modules in the wind tunnel using a multi-modal fiber optic sensing approach and is compared to the deflections estimated by the actuators. Each module is probed by single-mode optical fibers that contain just four grating sensors and consider both bending and torsional deformations. The fiber optic method in this work combines the principles of hybrid interferometry and FBG spectral sensing. The analysis involves an initial calibration procedure outside the wind tunnel followed by experimental testing in the wind tunnel. This method is shown to experimentally achieve an accuracy of 2.8 mm deflection with an error of 9%. The error sources, including actuator dynamics, random errors, and nonlinear mechanical backlash, are identified and discussed.

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Section: Incremental Control Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensing, Actuation, and Control of the SmartX Prototype Morphing Wing in the Wind Tunnel

2021

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“…Recently, Ref. [11] presented more generalized derivations, as well as Lyapunov-based stability and robustness analyses for the INDI control. Theoretical analyses also show INDI is more robust than the nonlinear dynamic inversion used in [12].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Incremental Control for Flexible Aircraft Trajectory Tracking and Load Alleviation

Wang

Mkhoyan

Breuker

2022

Journal of Guidance, Control, and Dynamics

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This paper proposes a nonlinear control architecture for flexible aircraft simultaneous trajectory tracking and load alleviation. By exploiting the control redundancy, the gust and maneuver loads are alleviated without degrading the rigid-body command tracking performance. The proposed control architecture contains four cascaded control loops: position control, flight path control, attitude control, and optimal multi-objective wing control. Since the position kinematics are not influenced by model uncertainties, the nonlinear dynamic inversion control is applied. On the contrary, the flight path dynamics are perturbed by both model uncertainties and atmospheric disturbances; thus the incremental sliding mode control is adopted. Lyapunov-based analyses show that this method can simultaneously reduce the model dependency and the minimum possible gains of conventional sliding mode control methods. Moreover, the attitude dynamics are in the strict-feedback form; thus the incremental backstepping sliding mode control is applied. Furthermore, a novel load reference generator is designed to distinguish the necessary loads for performing maneuvers from the excessive loads. The load references are realized by the inner-loop optimal wing controller, while the excessive loads are naturalized by flaps without influencing the outer-loop tracking performance. The merits of the proposed control architecture are verified by trajectory tracking tasks and gust load alleviation tasks in spatial von Kármán turbulence fields.

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“…In principle, a perfect knowledge of the system dynamics across the entire flight envelope is required to achieve an exact dynamic cancellation. However, such a requirement is almost impossible to meet in reality due to modeling simplifications, computational errors, and external disturbances [22,23]. Particularly, for the tailless aircraft, uncertainties and disturbances come from the following possible sources: aerodynamic and propulsive approximations, neglected control effector interactions, neglected vehicle elasticity, unmodeled actuator and sensor dynamics, time delay in the feedback path, and wind gust [9].…”

Section: Introductionmentioning

confidence: 99%

Design and Validation of Disturbance Rejection Dynamic Inverse Control for a Tailless Aircraft in Wind Tunnel

et al. 2021

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This paper focuses on the design of a disturbance rejection controller for a tailless aircraft based on the technique of nonlinear dynamic inversion (NDI). The tailless aircraft model mounted on a three degree-of-freedom (3-DOF) dynamic rig in the wind tunnel is modeled as a nonlinear affine system subject to mismatched disturbances. First of all, a baseline NDI attitude controller is designed for sufficient stability and good reference tracking performance of the nominal system. Then, a nonlinear disturbance observer (NDO) is supplemented to the baseline NDI controller to estimate the lumped disturbances for compensation, including unmodeled dynamics, parameter uncertainties, and external disturbances. Mathematical analysis demonstrates the convergence of the employed NDO and the resulting closed-loop system. Furthermore, an anti-windup modification is applied to the NDO for control performance preserving in the presence of actuator saturation. Subsequently, the designed control schemes are preliminarily validated and compared via simulations. The baseline NDI controller demonstrates satisfactory attitude tracking performance in the case of nominal simulation; the NDO augmented NDI controller presents significantly improved ability of disturbance rejection when compared with the baseline NDI controller in the case of robust simulation; the anti-windup modified scheme, rather than the baseline NDI controller nor the NDO augmented NDI controller, can preserve the closed-loop performance in the case of actuator saturation. Finally, the baseline NDI scheme and the NDO augmented NDI scheme are implemented and further validated in the wind tunnel flight tests, which demonstrate that the experimental results are in good agreement with that of the simulations.

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Stability Analysis for Incremental Nonlinear Dynamic Inversion Control

Cited by 117 publications

References 29 publications

Sensing, Actuation, and Control of the SmartX Prototype Morphing Wing in the Wind Tunnel

Sensing, Actuation, and Control of the SmartX Prototype Morphing Wing in the Wind Tunnel

Nonlinear Incremental Control for Flexible Aircraft Trajectory Tracking and Load Alleviation

Design and Validation of Disturbance Rejection Dynamic Inverse Control for a Tailless Aircraft in Wind Tunnel

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