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RISS 인기검색어
- 검색키워드
- 저자 : Khayyam Masood (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Implementation and Application of Guidance law using Aerodynamic Data for Simulation Purposes
Zhang Wei,Jin Jian-yun,Khayyam Masood,Li Lin,Ma Ze-hao 제어로봇시스템학회 2014 제어로봇시스템학회 국제학술대회 논문집 Vol.2014 No.10
This paper describes a new technique for simulating the guidance of a SAM (surface to air missile) without the help of a controller. This analysis is fully automated in a closed loop using the features of commercial software MATLAB. The guidance algorithm is simulated with the help of aerodynamic data. Control law is not used for obtaining the desired deflections of the control surfaces rather reverse aerodynamics are used to calculate the commanded accelerations which are implied by the guidance system. The addition required in aerodynamics coefficients are calculated through reverse aerodynamics which are later on used to calculate the deflections required. These deflections are passed through actuator dynamics to obtain real deflections. The deflections obtained from actuator are then passed to aerodynamic block to calculate the desired aerodynamic forces. The forces update the current information and LOS (line of sight) of the guidance block thus creating a closed loop system. This technique has a unique solution for a unique guidance algorithm thus ensuring the path followed by the SAM is only dependent on guidance algorithm. This enables an efficient way to diagnose which guidance algorithm is suitable for a particular system without getting into the complexity of updating the controller and re-tuning of controller gains.
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An Aircraft's Parameter Identification Algorithm Based on Cloud Model Optimization
Zhang Wei,Liu Yi-lei,Guo Da-Peng,Khayyam Masood,Tian Jing 제어로봇시스템학회 2014 제어로봇시스템학회 국제학술대회 논문집 Vol.2014 No.10
The maximum likelihood (ML) estimation method has been extensively applied to identifying the parameters of an aircraft. But it has to derive sensitivity equations in advance and solve sensitivity matrices, thus being complicated for its application and easily reaching locally optimal solutions. The paper proposes an aircraft"s parameter identification algorithm, which optimizes the ML function with the cloud model optimization theory in accordance with the ML estimation principle, thus obtaining the values of the parameters to be identified. The algorithm does not have to derive sensitivity matrices, has no high requirements for initial values and is little affected by noise. Thus it is easy to apply, can be optimized by the cloud model and have rather fast convergence and nice global search capability and thus not easily reaching locally optimal solutions. The Twin Otter airplane is used as a numerical example to verify the algorithm. The numerical results show that the parameter identification algorithm is easy to implement, has good identification precision and fast convergence and does not reach locally optimal solutions.
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