Aircraft and Rotorcraft System Identification

Remple, Robert K.; Tischler, Mark B.

doi:10.2514/4.861352

Cited by 38 publications

(9 citation statements)

References 68 publications

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“…The rotor forces and moments are calculated in their reference axes based on the tippath-plane dynamics using the combined blade-element/momentum theory analysis [31]. The tilting of the rotor's tip-path-plane generates control moments and forward/backward control forces that are the components of the rotor's thrust.…”

Section: Decompositionmentioning

confidence: 99%

Unified Accurate Attitude Control for Dual-Tiltrotor UAV with Cyclic Pitch Using Actuator Dynamics Compensated LADRC

Wang

Cai

et al. 2022

Sensors

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This paper proposes a unified attitude controller based on the modified linear active disturbance rejection control (LADRC) for a dual-tiltrotor unmanned aerial vehicle (UAV) with cyclic pitch to achieve accurate attitude control despite its nonlinear and time-varying characteristics during flight mode transitions. The proposed control algorithm has higher robustness against model mismatch compared with the model-based control algorithms. The modified LADRC utilizes the state feedbacks from the onboard sensors like IMU and Pitot tube instead of the mathematical model of the plane. It has less dependency on the accurate dynamics model of the dual-tiltrotor UAV, which can hardly be built. In contrast to the original LADRC, an actuator model is integrated into the modified LADRC to compensate for the non-negligible slow rotor flapping dynamics and servo dynamics. This modification eliminates the oscillation of the original LADRC when applied on the plant with slow-response actuators, such as propeller and rotors of the helicopter. In this way, the stability and performance of the controller are improved. The controller replaces the gain-scheduling or the control logic switching by a unified controller structure, which simplifies the design approach of the controller for different flight modes. The effectiveness of the modified LADRC and the performance of the unified attitude controller are demonstrated in both simulation and flight tests using a dual-tiltrotor UAV. The attitude control error is less than ±4° during the conversion flight. The control rising time in different flight modes is all about 0.5 s, despite the variations in the airspeed and tilt angle. The flight results show that the controller guarantees high control accuracy and uniform control quality in different flight modes.

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Section: Decompositionmentioning

confidence: 99%

Unified Accurate Attitude Control for Dual-Tiltrotor UAV with Cyclic Pitch Using Actuator Dynamics Compensated LADRC

Wang

Cai

et al. 2022

Sensors

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“…Based on the work of Gary Fay in Mesicopter project [10,11], the aerodynamic forces and moments of rotating propeller are resolved using classic blade element theory. For convenience of the reader, we recall some symbols.…”

Section: Ducted-fan System Forces and Momentsmentioning

confidence: 99%

“…After the hardware construction of the ducted-fan prototype, we first obtain a comprehensive flight dynamics model based on an elaborated approach which integrates first-principle and system identification techniques. The frequency-domain identification tool CIFER [10], which was developed by army/NASA Rotorcraft Division and is one of today's standard tools for identifying models of different aircraft configurations, has been used here to capture the key dynamics. With the identified model in hand, we then carry out to design a flight control system with two-loop architecture, in which an inner loop is for stabilization and decoupling the UAV dynamics and an outer loop is for desired velocity tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Flight Control Development and Test for an Unconventional VTOL UAV

Wang¹,

Chen²,

Ma³

et al. 2016

Autonomous Vehicle

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This chapter deals with the control system development and flight test for an unconventional flight vehicle, namely, a tandem ducted-fan experimental flying platform. The first-principle modeling approach combined with the frequency system identification has been adopted to obtain a high-fidelity dynamics model. It is inherently less stable and difficult to control. To accomplish the required practical flight tasks, the flying vehicle needs to work well even in windy conditions. Moreover, for flight control engineers, simple prescribed multi-loop controller structures are preferred. To handle the multiple problems, a structured velocity controller consisting of two feedback loops is developed, where inner loop provides stability augmentation and decoupling, and the outer loop guarantees desired velocity tracking performance. The simultaneous design of the two-loop controllers under multiple performance requirements in the usual H ∞ metrics can be cast as a nonsmooth optimization program. To compensate for changes in plant dynamics across the flight envelope, a smooth and compact polynomial scheduling formula is implemented as a function of the forward flight speed. Both simulations and flight test results have been presented in this work to showcase the potential for the proposed robust nonlinear control system to optimize the performance of UAV, specifically unconventional vehicles.

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“…Researchers extended the neural-network-based optimisation algorithms in combination with fuzzy logic to identify system parameters of various fixed wing and rotorcraft flight vehicles (12)(13)(14)(15) . Researchers have also carried out substantial work in estimating parameters using methods in the frequency domain (16)(17)(18)(19)(20)(21)(22) . It is generally observed from the available literature that major research on parameter estimation, from flight data, has traditionally been focused towards model identification of manned aircraft, while the research on aerodynamic modelling of UAVs from flight test is the focus of much of the current research.…”

Section: Introductionmentioning

confidence: 99%

Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods

2018

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The current research paper describes the lateral-directional parameter estimation from flight data of a miniature Unmanned Aerial Vehicle (UAV) using Maximum Likelihood (ML), and Neural-Gauss-Newton (NGN) methods. An unmanned configuration with a cropped delta planform and thin rectangular cross-section has been designed, fabricated and instrumented. Exhaustive full-scale wind-tunnel tests were performed on the UAV to extract the form of aerodynamic model that has to be postulated a priori for parameter estimation. Rigorous flight tests have been performed to acquire the flight data for several prescribed manoeuvres. Four sets of compatible flight data have been used to carry out parameter estimation using classical ML and neural-network-based NGN methods. It is observed that the estimated parameters are consistent and the lower values of the Cramer-Rao bound for the corresponding estimates have shown significant confidence in the obtained parameters. Furthermore, to validate the aerodynamic model used and to enhance the confidence in the estimated parameters, a proof of match exercise has been carried out.

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Aircraft and Rotorcraft System Identification

Cited by 38 publications

References 68 publications

Unified Accurate Attitude Control for Dual-Tiltrotor UAV with Cyclic Pitch Using Actuator Dynamics Compensated LADRC

Unified Accurate Attitude Control for Dual-Tiltrotor UAV with Cyclic Pitch Using Actuator Dynamics Compensated LADRC

Flight Control Development and Test for an Unconventional VTOL UAV

Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods

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