This paper addresses the challenge of mass uncertainty during the powered descent phase of a Mars lander and proposes a robust powered descent guidance algorithm that accounts for uncertainties in mass and fuel consumption. First, the traditional trajectory optimization method based on convex optimization is improved by developing a fast and accurate solution approach using sequential convex optimization. Second, the effects of mass uncertainty on position are modeled and analyzed, with corresponding computational methods provided for different scenarios. Third, the worst-case scenario under mass uncertainty is analyzed through both geometric and theoretical approaches, and a modified glide-slope constraint method is proposed to ensure safe landing even in adverse conditions. Moreover, a closed-loop receding horizon based guidance is developed to further mitigate the effects of mass uncertainty and improve terminal landing accuracy. Finally, the proposed improved convex optimization algorithm and robust trajectory optimization algorithm are validated through simulation cases and compared with a probabilistic approach. The simulations further test various initial positions, velocities, and glide-slope angles, demonstrating that the solutions are both accurate and robust.

Traditional landers typically encounter difficulties achieving stable landings because of the weak gravity and complex terrain of small celestial bodies. A multi-node lander with flexible connections can improve the stability of a small celestial body landing. However, this also poses new challenges, particularly for landing guidance in hazardous terrain. To address this problem, an equivalent simplified dynamic model of a multi-node flexible lander is first constructed, and its flat output is determined. Subsequently, a trajectory-planning method combining the flow and vector fields is designed to avoid collision, and the parameters of the vector field are optimized online according to the dynamic and obstacle constraints during the descent process to obtain a more suitable trajectory. Finally, the effectiveness of the proposed trajectory-planning method is verified through comparative simulations of landing and obstacle avoidance from the hover point to the landing area. This study offers new prospects for upcoming small celestial body landing missions in complex terrains.

Rapid and reliable onboard optimization of bank angle profiles is crucial for mitigating uncertainties during Mars atmospheric entry. This paper presents a neural-network-accelerated methodology for optimizing parametric bank angle profiles in Mars atmospheric entry missions. The methodology includes a universal approach to handling path constraints and a reliable solution method based on the Particle Swarm Optimization (PSO) algorithm. For illustrative purposes, a mission with the objective of maximizing terminal altitude is considered. The original entry optimization problem is converted into optimizing three coefficients for the bank angle profiles with terminal constraints by formulating a parametric Mars entry bank angle profile and constraint handling methods. The parameter optimization problem is addressed using the PSO algorithm, with reliability enhanced by increasing the PSO swarm size. To improve computational efficiency, an enhanced Deep Operator Network (DeepONet) is used as a dynamics solver to predict terminal states under various bank angle profiles rapidly. Numerical simulations demonstrate that the proposed methodology ensures reliable convergence with a sufficiently large PSO swarm while maintaining high computational efficiency facilitated by the neural-network-based dynamics solver. Compared to the existing methodologies, this methodology offers a streamlined process, the reduced sensitivity to initial guesses, and the improved computational efficiency.