Algorithms Research On Autonomous Navigation And Control Of Lunar Explorer

Autonomous navigation, guidance and control is one of key technologies for lunar explorer. With the supports of lunar exploration pre-research program"lunar soft landing integrative simulation and validation technique", this dissertation performed deep and systemic study on lunar explorer autonomous navigation and control issues. The main contents of this dissertation are as follows.With earth-moon transfer trajectory taken into account, this paper gives the deep space autonomous optical navigation algorithm based on the information of Earth-Moon. Selecting the image elements of Earth and Moon centers as observed quantity, measurement noise model and observed equations of autonomous navigation system are built up. The spacecraft orbit is determined by using the recursive weighted least square based on UD factorization. For the circling moon phase mission, this paper proposes an autonomous optical navigation algorithm using the Gauss-Markov process and Unscented Kalman filter. For the problems arising from the absence of exact dynamics models, the Gauss-Markov process is used to approximate the un-model acceleration term in the circling orbit dynamics. The Unscented Kalman filter is used to estimate the probe position, velocity and un-model acceleration, which improves the orbit estimation accuracy and ensures the stability of navigation algorithm.For the lunar landing phase mission, this paper presents autonomous navigation scheme based on feature points (FPs) tracking. The landing site coordinate system is first defined from the image coordinates of three feature points obtained using an optical navigation camera and the distances from the spacecraft to each FP measured using a laser range finder. Then, the relative position and attitude of the probe can be constructed. Next, autonomous navigation algorithm based Extended Kalman filter is presented in order to estimate the probe state variables and suppress measurement noise effectively.For the power descent mission, two guidance methods are presented based on polynomial planning and sequential quadratic programming. With the assumption that vertical optimal landing trajectory is represented as three-order polynomial, the guidance variable, thruster directional angle, can be reconstructed by use of the...