Nonlinear Multiple Integrator and Application to Aircraft Navigation

Wang, Xinhua; Shirinzadeh, Bijan

doi:10.1109/taes.2013.120135

Cited by 23 publications

(8 citation statements)

References 29 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MEMS accelerometers and gyroscopes can determine the AAV position and velocity. These integration of the sensors is known as Inertial Navigation System (INS) [26], [27]. However, a MEMS INS suffers from bias errors.…”

Section: Data Acquisition Methodologiesmentioning

confidence: 99%

System Identification-Based Sliding Mode Control for Small-Scaled Autonomous Aerial Vehicles With Unknown Aerodynamics Derivatives

Choi

Shirinzadeh

Porter

2016

IEEE/ASME Trans. Mechatron.

Self Cite

View full text Add to dashboard Cite

This paper proposes an autonomous system identification methodology for fixed wing Autonomous Aerial Vehicles (AAVs) and implementation with a robust control techinque. This system identification methodology does not require the user to provide any knowledge of the aerodynamic derivatives of the AAV. Hence, this control methodology is applicable to a wide range of fixed-wing AAVs. Since the system identification process generates errors due to the measurement noise and external disturbances, a robust control methodology, sliding mode control, is proposed to control the AAV based on the system identification results. To characterise the capability of the control methodology, experiments have been conducted on commerically available fixed-wing aircraft platforms using low-cost sensing equipment. Experimental results demonstrate that the autonomous system identification-based sliding mode control enables the AAV to follow the desired trajectory without prior knowledge of the aerial vehicle or manual tuning of control system parameters.

show abstract

Section: Data Acquisition Methodologiesmentioning

confidence: 99%

System Identification-Based Sliding Mode Control for Small-Scaled Autonomous Aerial Vehicles With Unknown Aerodynamics Derivatives

Choi

Shirinzadeh

Porter

2016

IEEE/ASME Trans. Mechatron.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [24], a nonlinear double-integral observer was presented to estimate synchronously the onefold and double integrals of a signal, and a generalized multiple integrator was designed to estimate the multiple integrals [25]. In [26], a nonlinear integral-derivative observer was proposed to estimate synchronously the integral and derivative of a signal.…”

Section: Introductionmentioning

confidence: 99%

Aircraft navigation based on differentiation–integration observer

Wang

Cai

2017

Aerospace Science and Technology

Self Cite

View full text Add to dashboard Cite

In this paper, a generalized differentiation-integration observer is presented based on sensors selection. The proposed differentiation-integration observer can estimate the multiple integrals and high-order derivatives of a signal, synchronously. The parameters selection rules are presented for the differentiation-integration observer. The theoretical results are confirmed by the frequency-domain analysis. The effectiveness of the proposed observer are verified through the numerical simulations on a quadrotor aircraft: i) through the differentiation-integration observer, the attitude angle and the uncertainties in attitude dynamics are estimated synchronously from the measurements of angular velocity; ii) a control law is designed based on the observers to drive the aircraft to track a reference trajectory.

show abstract

“…In [15], a nonlinear double-integral observer with the abilities of noise rejection and drift correction was presented to estimate synchronously the onefold and double integrals of a signal. In [16], a generalized multiple integrator was designed to estimate the multiple integrals for a signal. In [17], a nonlinear integral-derivative observer was proposed to estimate synchronously the integral and derivative of a signal.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the theory of finite-time stability [37,38], singular perturbation technique [39,40] and our previous works [15,16], a nonlinear integral-uncertainty observer is developed. Based on the theories in [15,16], an extended system is implemented after the uncertainty in system is taken as a new state.…”

Section: Introductionmentioning

confidence: 99%

Navigation and Control Based on Integral-Uncertainty Observer for Unmanned Jet Aircraft

Wang

Cai

2017

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

View full text Add to dashboard Cite

A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract: A nonlinear integral-uncertainty observer is presented, which can estimate the integral of measurement output signal and the uncertainty in system, synchronously. In order to be satisfied with the existing hardware computational environments and to select the parameters more easily, a simplified linear version of the nonlinear integral-uncertainty observer is also developed. The effectiveness of the proposed observers are verified through the numerical simulations and experiments: i) through the integral-uncertainty observers, the attitude angle and the uncertainties in attitude dynamics are estimated synchronously from the measurements of angular velocity, and the estimate results by the two observers are compared; ii) a control law is designed based on the observers to drive the jet aircraft to track a reference trajectory.

show abstract

Nonlinear Multiple Integrator and Application to Aircraft Navigation

Cited by 23 publications

References 29 publications

System Identification-Based Sliding Mode Control for Small-Scaled Autonomous Aerial Vehicles With Unknown Aerodynamics Derivatives

System Identification-Based Sliding Mode Control for Small-Scaled Autonomous Aerial Vehicles With Unknown Aerodynamics Derivatives

Aircraft navigation based on differentiation–integration observer

Navigation and Control Based on Integral-Uncertainty Observer for Unmanned Jet Aircraft

Contact Info

Product

Resources

About