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\section*{Acknowledgment}
Yang Wang acknowledges the support of this work by the China Scholarship Council (Grant no.201706290024).
\section*{Appendix}
\subsection{Trajectory Discretization}
Instead of discretization using Euler method, a higher order method is used to increase the accuracy. The classical 4th order Runge--Kutta formula is used as
\begin{equation}
\label{eq:Runge-Kutta}
    \left\{
    \begin{array}{ll}
        \vect x_{n+1} &= \vect x_n + \dfrac{h}{6}\left(\vect K_1 + 2\vect K_2 + 2\vect K_3 +\vect K_4\right) \\
        \vect K_1 &= \mathcal{F} (t_{n,1},\vect x_{n,1}), \quad t_{n,1} = t_n, \ \vect x_{n,1} = \vect x_n\\
        \vect K_2 &= \mathcal{F} (t_{n,2},\vect x_{n,2}), \quad t_{n,2} = t_n + \dfrac{h}{2}, \ \vect x_{n,2} = \vect x_n + \dfrac{h}{2}\vect K_1\\ 
        \vect K_3 &= \mathcal{F} (t_{n,3},\vect x_{n,3}), \quad t_{n,3} = t_n + \dfrac{h}{2}, \ \vect x_{n,3} = \vect x_n + \dfrac{h}{2}\vect K_2\\
        \vect K_4 &= \mathcal{F} (t_{n,4},\vect x_{n,4}), \quad t_{n,4} = t_n + h, \ \vect x_{n,4} = \vect x_n + h\vect K_3
    \end{array}
    \right.
\end{equation}

Combining Eq.\ \eqref{eq:Runge-Kutta} with Eq.\ \eqref{eq:expr_x_k_plus_1} yields
\begin{equation}
    \vect F_n(t_n,\vect x_n) = \vect x_n + \dfrac{h}{6}\left(\vect K_1 + 2\vect K_2 + 2\vect K_3 +\vect K_4\right)
\end{equation}
and its differential w.r.t $\vect x_n$ is
\begin{equation}
    \dfrac{{\rm d} \vect F_n(t_n,\vect x_n)}{{\rm d} \vect x_n} = \vect I_n + \dfrac{h}{6}\left( \dfrac{{\rm d} \vect K_1}{{\rm d} \vect x_n} + 2\dfrac{{\rm d} \vect K_2}{{\rm d} \vect x_n} + 2\dfrac{{\rm d} \vect K_3}{{\rm d} \vect x_n} + \dfrac{{\rm d} \vect K_4}{{\rm d} \vect x_n} \right)
\end{equation}
where
\begin{equation}
    \dfrac{{\rm d} \vect K_i}{{\rm d} \vect x_n} = \dfrac{{\rm d} \mathcal{F} (t_{n,i},\vect x_{n,i})}{{\rm d} \vect x_{n,i}} \dfrac{{\rm d} \vect x_{n,i}}{{\rm d} \vect x_n},\quad i = 1,2,3,4
\end{equation}















\section{Conclusion}

Unlike the applications with continuous control profile, this paper shows that the sensitive matrix is discontinuous at the bang-bang switching point. A robust two-loop MPSP algorithm is further designed as the low-thrust guidance scheme. Numerical simulations illustrate that the proposed MPSP algorithm is robust for various kinds of perturbations. Besides, the fuel consumption is near optimal even when the thrust sequence is required to be changed. The future work will refine the algorithm design to reduce the total iterations.





\subsection{Interplanetary Cubesat Mission}
An interplanetary Cubesat mission to asteroid 99942 is conducted in this part to assess the effectiveness of proposed method. The piggyback launch opportunity to Sun-Earth L2 point is used as the starting position. The launch time and arrival time are set to be 2020-10-01 and 2023-12-01, at which the corresponding position and velocity of the Earth and the asteroid 99942 are extracted from NASA SPICE system\ \cite{acton1996ancillary}. The initial mass of the spacecraft is set to be $m_0 = 25 kg$, the maximum thrust $T_{max} = 1.5 mN$ and $I_{sp}