Aerospace Science and Technology
Mohammad Yavari; Nemat Allah Ghahremani; Reza Zardashti; Jalal Karimi
Abstract
In this paper a new mid-course guidance algorithm for intercepting high altitude target is proposed. A part of target flight path is outside the atmosphere. The maximum acceleration command is designed as a variable constraint that varies with altitude. This physical limitation is happened for the aerodynamically ...
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In this paper a new mid-course guidance algorithm for intercepting high altitude target is proposed. A part of target flight path is outside the atmosphere. The maximum acceleration command is designed as a variable constraint that varies with altitude. This physical limitation is happened for the aerodynamically control interceptors at high altitudes because of decreasing air density. Based on generalized incremental predictive control approach, a new formulation for parallel navigation guidance law is proposed. Using the nonlinear kinematic equations of target-interceptor, the commands of the new guidance method are computed by optimization of a cost function involved the velocity perpendicular to the line of sight errors and guidance commands. An important feature of the proposed method is the minimization of the line- of - sight angular rate in a finite period of time. The various simulation results of the proposed guidance law shows the higher effectiveness of the designed guidance law in comparison with proportional navigation and sliding mode guidance.
Aerospace Science and Technology
Nemat Allah Ghahremani; Hassan Majed Alhassan
Abstract
This paper presents a new Modified Predictive Kalman Filter (MPKF). To solve the problem of a strap-down inertial navigation system (SINS) self-alignment process that the standard Kalman filters cannot give the optimal solution when the system model and stochastic information are unknown accurately. ...
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This paper presents a new Modified Predictive Kalman Filter (MPKF). To solve the problem of a strap-down inertial navigation system (SINS) self-alignment process that the standard Kalman filters cannot give the optimal solution when the system model and stochastic information are unknown accurately. The proposed algorithm is applied to SINS in the initial alignment process with a large misalignment heading angle. The filter is based on the idea of an accurate predictive filter applies n-steps ahead prediction of the SINS model errors to effectively enhance the corrections of the current information residual error on the system. Firstly, the formulations of a novel predictive filter and a fine alignment algorithm for SINS are presented. Secondly, the vehicle results demonstrate the superior performance of the proposed method, in which the MPKF algorithm is less sensitive to uncertainty. It performs faster and more accurate estimation of SINS' initial orientation angles compared with the conventional EKF method.
