In the process of applying linear quadratic regulator (LQR) to solve aerial vehicle reentry reference trajectory guidance, to obtain better profile-following performance, the parameters of the aerial vehicle system can be used to calculate weighting matrices according to the Bryson principle. However, the traditional method is not applicable to various disturbances in hypersonic vehicles (HSV) which have particular dynamic characteristics. By calculating the weighting matrices constructed based on Bryson principle using time-varying parameters, a novel time-varying LQR design method is proposed to deal with the various disturbances in HSV reentry profile-following. Different from the previous approaches, the current states of the flight system are employed to calculate the parameters in weighting matrices. Simulation results are given to demonstrate that using the proposed approach in this chapter, performance of HSV profile-following can be improved significantly, and stronger robustness against different disturbances can be obtained.
After generating reference trajectory in the process of reentry guidance for reusable launch vehicle, if the designed bank angle curve is too close to the constraint boundary, there is a great possibility for the obtained reference trajectory to exceed the reentry corridor boundary. This paper proposed a margin searching method, to reduce the likelihood of generating a reference trajectory which exceeds the reentry corridor boundary and to improve the design success rate.
New predictive guidance (NPG) algorithm is proposed and the fuzzy techniques are adopted to improve the real-time capability of the predictive guidance. The simplified kinematical and dynamical equations of model for the entry vehicle are established. The nominal bank angel profile is established beforehand to implement numerical prediction. The predictive errors are used as input of fuzzy logic controller which is designed for generation of correct commands. The fuzzy logic controller avoids massive iterations of conventional predictive guidance (CPG) algorithm and improves the real-time capability. Numerical simulations for the proposed algorithm under the condition of perturbations are presented to demonstrate the capability and effectiveness.
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