Generalized Linear Quadratic Control for a Full Tracking Problem in Aviation

Dul, Franciszek; Lichota, Piotr; Rusowicz, А.

doi:10.3390/s20102955

Cited by 17 publications

(17 citation statements)

References 36 publications

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“…Nevertheless, the proposed algorithm is not limited to underwater; it can also be used in other applications [ 66 , 67 ] such as improving aircraft navigation and tracking by using aerial sensors. Moreover, autonomous ground vehicles are another area where this method can be employed.…”

Section: Results and Discussionmentioning

confidence: 99%

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

Shaukat

Ahmed

Iqbal

et al. 2021

Sensors

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The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.

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Section: Results and Discussionmentioning

confidence: 99%

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

Shaukat

Ahmed

Iqbal

et al. 2021

Sensors

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“…Despite "wing-rock" generally not being dangerous, it is advisable to examine it more thoroughly. Studying the dynamics of this motion was attempted in numerous research work (for example: [10,[55][56][57][58][59][60]. All of the quoted work adopted quasi-static aerodynamic aircraft characteristics or presented different identification methods.…”

Section: 1"wing Rock" Oscillation Simulationmentioning

confidence: 99%

Bifurcation Flight Dynamic Analysis of a Strake-Wing Micro Aerial Vehicle

et al. 2021

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Non-linear phenomena are particularly important in -flight dynamics of micro-class unmanned aerial vehicles. Susceptibility to atmospheric turbulence and high manoeuvrability of such aircraft under critical flight conditions cover non-linear aerodynamics and inertia coupling. The theory of dynamical systems provides methodology for studying systems of non-linear ordinary differential equations. The bifurcation theory forms part of this theory and deals with stability changes leading to qualitatively different system responses. These changes are called bifurcations. There is a number of papers, the authors of which applied the bifurcation theory for analysing aircraft flight dynamics. This article analyses the dynamics of critical micro aerial vehicle flight regimes. The flight dynamics under such conditions is highly non-linear, therefore the bifurcation theory can be applied in the course of the analysis. The application of the theory of dynamical systems enabled predicting the nature of micro aerial vehicle motion instability caused by bifurcations and analysing the post-bifurcation microdrone motion. This article presents the application of bifurcation analysis, complemented with time-domain simulations, to understand the open-loop dynamics of strake-wing micro aerial vehicle model by identifying the attractors of the dynamic system that manages upset behaviour. A number of factors have been identified to cause potential critical states, including non-oscillating spirals and oscillatory spins. The analysis shows that these spirals and spins are connected in a one-parameter space and that due to improper operation of the autopilot on the spiral, it is possible to enter the oscillatory spin.

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“…The trajectory of the helicopter in this phase is prescribed by way points, and may describe any approach procedure required by regulations. A helicopter, controlled by LQR, passes through the prescribed waypoints with assumed error thresholds (this can be done with time points restrictions as well [27]). The start of the approach phase is also the beginning of collecting data of the vessel motion for a prediction algorithm (based on the autoregressive method) of the vessel motion.…”

Section: Helicopter Approach and Landing Startegymentioning

confidence: 99%

Helicopter Control During Landing on a Moving Confined Platform

2020

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The paper presents a control algorithm for a helicopter automatic approach and landing on a moving confined platform. It discusses landing on a sea vessel deck as a representative case for a mobile confined area. The dynamic model of a single rotor helicopter with a control system, developed in the FLIGHTLAB environment, and validated against flight tests data, is used to investigate control efficiency. The developed control method is based on the Linear Quadratic Regulator combined with prediction of motion of the landing area. An important part of the research was analysis of availability of the data needed for controlling the rotorcraft. The simulations of approach and landing on a moving vessel in various environmental conditions confirmed the efficiency of the developed control methodology. INDEX TERMS Helicopter automatic control, Confined area landing, Helicopter motion modeling, Vessel motion prediction.

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Generalized Linear Quadratic Control for a Full Tracking Problem in Aviation

Cited by 17 publications

References 36 publications

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

Bifurcation Flight Dynamic Analysis of a Strake-Wing Micro Aerial Vehicle

Helicopter Control During Landing on a Moving Confined Platform

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