## 1 Introduction  
With the increasing demand for launch vehicle missions, power system failures have become the main cause of launch vehicle mission failure [1]. Therefore, advanced launch vehicle technology with high reliability and failure tolerance has attracted extensive attention and research. Currently, launch vehicles are required to have omnidirectional sensing ability and a high degree of autonomous decision-making ability. The improvement of perception capability can be realized by distributed sensors and satellite communication technologies, and the fault information acquisition and the control system reconstruction have become possible with the development and maturity of the design technology of the asynchronous fault detection observer and the data-driven control technology, which can meet the requirements of the random system [2, 3]; therefore, the autonomous decision making of flight missions and trajectory planning under power failure conditions becomes the major challenge in the development of launch technology. With the rapid development of machine learning, RBFNN and other machine-learning methods with powerful mapping capability have been gradually used to solve the trajectory-planning problem [4, 5]. Launch vehicle ascent trajectory-planning methods are mainly divided into two categories, including analytical methods based on optimal control principles and numerical methods that transform and solve the problem parametrically. The iterative guidance method (IGM) was proposed and developed in the 1960s. The IGM has been applied successfully to the Saturn V launch vehicle of the U.S. and the Long March vehicles of China [6; 7]. In references [8; 9; 10; 11], an improved IGM can cope with more terminal constraints and achieve guidance accuracy of less than 10 m through an injection-point position update and shutdown strategy adjustment. Therefore, it can be said that the IGM has become one of the most important launch vehicle online planning methods. With the development of numerical methods, numerical methods such as particle swarm optimization, Gaussian pseudo-spectral, and convex optimization methods have been gradually introduced into launch vehicle trajectory planning. Although the numerical methods have great potential for launch vehicle trajectory planning, issues still affect its engineering applications, such as algorithm convergence and computational efficiency. For example, in the literature [12; 13], the Gaussian pseudo-spectral method was used to discretize the trajectory-planning problem based on the optimal control expression for the ascent section of the launch vehicle. Although the initial guessing technique and the homogeneous technique were adopted to improve the algorithm's convergence, it still could not meet the demand of real-time trajectory online planning under failures or large disturbance conditions. In the literature [14], the orbit-injection problem was transformed into a series of simple convex subproblems by convex optimization, which were solved step by step by serial iterative methods. Although this method can deal with more complex constraints, the number of discrete points and the trust domain's setting will affect the algorithm's accuracy and convergence under different working conditions. Moreover, the existing equipment on the launch vehicle cannot bear the computation load of the iterative solution of continuum subproblem in the convexity of sequence. Therefore, to ensure the algorithm's reliability and timeliness, an improved ATU-IGM is adopted for the online planning of trajectories based on mission parameters. As of now, there has been little research on launch vehicle orbital mission decision making. Most studies are concentrated on the decision optimization of the upper stage orbit transfer of launch vehicles by using optimization algorithms and the determination of optimal orbit in the original target orbit plane. A two-particle hierarchical optimization framework for the multi-pulse orbit transfer of spacecrafts was investigated in the literature [15].