On-line predictor-corrector reentry guidance law based on Gauss pseudospectral method

To enhance adaptability and robust of reentry guidance, an optimal on-board predictor-corrector guidance algorithm is developed. The differential equation constrains are transformed into algebraic constrains by means of the method of pseudospectral method, thus making the integration of differential equation unnecessary. The design of guidance law then can be solved by nonlinear programming. Owing to fast convergence and high accuracy of Gauss pseudospectral method, the adaptive guidance is obtained using on-line segmented optimization between routes. The benefit of proposed guidance is analyzed. The numerical simulation indicates that the proposed algorithm is not sensitive to initial errors. The corrected time is short, so that the guidance has the potential for on-line implementation.