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

Abstract

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.

Sliding mode corrector for jet UAV control

“…The proof of Theorem 5.1 is presented in Appendix. (17)): The sensing accuracy inequality 0 < ε 1 and r ∈ 0, 1 2 guarantee that the rejection ratio ρ(ω 1 ) in frequency band [ω 0 , ∞) is small enough, and [ω 0 , ∞) covers the low/mid/high frequency bands. Therefore, the disturbance d 1 (t) in [ω 0 , ∞) can be rejected sufficiently by the corrector.…”

“…Even velocity can be use for estimation, disturbance in position cannot still be corrected. For a jet UAV flight control system, an integral-uncertainty observer was designed to estimate the attitude angles and attitude dynamic uncertainty, and an augmented observer was used to estimate the flying velocity and position dynamic uncertainty [17]. However, drift may happen for long-time flight due to effect of disturbance and actuator vibrations on the IMU.…”

“…Theorem 5.2 (optimal sliding mode corrector): The corrector (17) and the measurement signals in Theorem 5.1 are considered, where, the unknown bounded disturbances…”

“…An RC-model-based F/A-18 Hornet prototype is used [17], which is shown in Fig. 2, and the forces and torques of UAV are described in Fig.…”

Adaptive Fading Bayesian Unscented Kalman Filter and Smoother for State Estimation of Unmanned Aircraft Systems

“…The control of UASs requires the prior knowledge of the corresponding state parameters which may include the positions, velocity, orientation and orientation rate of the aircraft. Though some of the parameters can be measured using specialized sensors such as Global Positioning System (GPS) receivers and Inertial Measurement Units (IMUs) [12], the signals obtained from such sensors may be contaminated by noise/interference from ambient environments and the bias inherited in the sensors themselves. Therefore, it is important to develop appropriate onboard algorithms for estimating the states of UASs from noisy measurements.…”