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Natnael S. Zewge,Hyochoong Bang 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
We address the entry phase navigation of Entry, Descent, and Landing (EDL) operation for future Mars spacecraft missions. The entry phase is characterized by a highly non-linear motion model and is influenced by various aerodynamic forces. Current entry phase navigation systems exclusively use Inertial Measurement Units (IMUs) from atmospheric entry to parachute deployment phase. This results in large error accumulation. Such systems cannot meet the precision landing requirements of future missions. In addition, entry phase filters designed using inertial measurement systems alone cannot observe some states which leads to diverging state estimates. We present an integrated navigation scheme whereby a ground based radio ranging device telemeters spacecraft range and range rate information to a computer on board the spacecraft. This information is fused with inertial measurements to yield a more accurate navigation system than a standalone inertial system. The paper implements the navigation scheme using the continuous-discrete Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Performance comparisons are made between the two filters in terms of RMS error, runtime, and ease of implementation. Our results show comparable results between the two filters. The UKF provides better estimates of lift to drag ratio and downrange distance while the EKF outperforms the UKF with the rest of the state and parameter estimates. The fact that no Jacobian calculation is required in the case of the UKF renders it a more attractive option than the EKF in terms of ease of implementation for highly non linear systems.
Reentry-phase Tracking of a Ballistic Missile in the Presence of Radar Glint Noise
Natnael S. Zewge,Hyochoong Bang 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
A framework to overcome the effect of radar glint noise for reentry-phase tracking of a ballistic missile is presented. Glint noise is characterized by probability density functions that are heavy-tailed. For such cases, the conventional Gaussian modeling of measurement noise does not apply. A filter designed with a Gaussian noise assumption will incur a performance penalty when outliers due to glint noise are encountered. In this work, the classic Extended Kalman Filter (EKF) is modified using a robust M-estimation procedure that accounts for measurement noise distributions with heavy tails. The proposed method provides a substantial improvement (in terms of root-mean-square error) over the standard versions of EKF and UKF. All the benefits that make EKF the method of choice for state estimation (e.g. simplicity and computational efficiency) are retained in this work while robustness is added. The suggested technique is validated using simulation experiments on a three-dimensional ballistic missile reentry model.
이동,김남수,Natnael S. Zewge,방효충 한국항공우주학회 2024 International Journal of Aeronautical and Space Sc Vol.25 No.3
This paper presents the design of data-driven fault-tolerant control using sparse online Gaussian process regression (SOGPR) to stabilize an aircraft with left-wing damage. The structural damage causes changes in mass, moment of inertia, center of gravity, and aerodynamic coefficients. These parameter variations deteriorate the performance of model-based nonlinear control methods. Hence, Gaussian process-based nonlinear dynamic inversion (GP-NDI) is proposed to compensate for uncertainties in situations of structural damage. Unlike parametric adaptive control approaches, Gaussian process regression is a non-parametric method that does not need prior information about uncertainties. And the proposed method implements SOGPR to reduce computational time and memory by incrementally updating the mean and variance. To compensate for the error in the estimated uncertainty, a robust control input is designed. In addition, a weighted delete score is used to improve the transient response. Numerical simulation results are compared with model reference adaptive control (MRAC) and nonlinear disturbance observer (NDO) to analyze a stabilizing and tracking performance in a structural damage situation.